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FSO relay networks for performance improvements in Cloud Computing-Based Radio Access Networks (CC-RANs)
Optics Communications 402 (2017) 653–661
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Optics Communications journal homepage: www.elsevier.com/locate/optcom
Analysis of multiuser mixed RF/FSO relay networks for performance improvements in Cloud Computing-Based Radio Access Networks (CC-RANs) Isiaka A. Alimi *, Paulo P. Monteiro, António L. Teixeira Instituto de Telecomunicações, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal Department of Electronics, Telecommunications and Informatics, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal
a r t i c l e
i n f o
MSC 2010: 00-01 99-00 Keywords: Atmospheric turbulence Gamma–gamma fading Cloud computing-based radio access networks (CC-RANs) Multiuser mixed RF/FSO relay networks Pointing error Rayleigh fading
a b s t r a c t The key paths toward the fifth generation (5G) network requirements are towards centralized processing and small-cell densification systems that are implemented on the cloud computing-based radio access networks (CCRANs). The increasing recognitions of the CC-RANs can be attributed to their valuable features regarding system performance optimization and cost-effectiveness. Nevertheless, realization of the stringent requirements of the fronthaul that connects the network elements is highly demanding. In this paper, considering the small-cell network architectures, we present multiuser mixed radio-frequency/free-space optical (RF/FSO) relay networks as feasible technologies for the alleviation of the stringent requirements in the CC-RANs. In this study, we use the end-to-end (e2e) outage probability, average symbol error probability (ASEP), and ergodic channel capacity as the performance metrics in our analysis. Simulation results show the suitability of deployment of mixed RF/FSO schemes in the real-life scenarios. © 2017 Elsevier B.V. All rights reserved.
1. Introduction There are growing tendencies for high-bandwidth transmission systems in the cellular mobile networks because of high increase in the broadband connections and the subsequent traffic on yearly basis [1]. The fifth generation (5G) wireless communication system has been noted to be a promising technology for supporting the system capacity growth and coverage in an efficient way [2–4]. The key technology for the realization of the 5G network requirements is the cloud radio access network (C-RAN). C-RAN is an emerging architecture in which cloud computing is being integrated into the radio access networks (RANs) [5,6]. Furthermore, the C-RAN has a centralized system in which the baseband processing is realized in the base band unit (BBU) pool. Moreover, a set of densely deployed remote radio heads (RRHs) are connected to the BBU pool by means of the fronthaul links. C-RANs have the capacity for enhancing shared spectrum access, enable cooperative spectrum sensing, as well as facilitating device-to-device (D2D) communications [5]. Furthermore, the C-RANs are futuristic technologies with high potential for realizing significant gains in spectral efficiency (SE) and energy efficiency (EE) for the 5G cellular networks and beyond. Nevertheless, C-RANs challenges are based on the constrained fronthaul.
Basically, the stringent requirements to facilitate centralized processing in the BBU pool are the employment of high bandwidth and low latency fronthaul for the network elements interconnection. However, in practice, fronthaul links are capacity and time-delay constrained. This eventually leads to significant degradation on the C-RAN performances [5,6]. The C-RAN architecture normally employs common public radio interface (CPRI) or open base-station initiatives (OBSAI) interface for digital fronthauling [7,8]. However, optical fronthaul in which these interfaces are implemented requires large bandwidth. This is as a result of the high resolution bits required for the digitalization process of the radio-frequency (RF) samples [9]. Therefore, the associated bandwidth inefficiency could limit or make C-RANs unrealistic to meet the performance requirements of the 5G wireless communication systems in which massive multiple-input multiple-output (massive MIMO) antenna systems will be integrated [10,11]. For instance, with carrier aggregation (CA) of long term evolution-advanced (LTE-A) of five 20 MHz mobile signals with 3 sectors, and 8 × 8 MIMO antennas, about 147.5 Gb/s fronthaul data rate will be required by the CPRI [12,13]. Consequently, for the realization of bandwidth-efficient mobile fronthaul, advanced
* Corresponding author at: Department of Electronics, Telecommunications and Informatics, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal.
E-mail addresses: [email protected] (I.A. Alimi), [email protected] (P.P. Monteiro), [email protected] (A.L. Teixeira). http://dx.doi.org/10.1016/j.optcom.2017.06.097 Received 16 May 2017; Received in revised form 13 June 2017; Accepted 28 June 2017 0030-4018/© 2017 Elsevier B.V. All rights reserved.
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Optics Communications 402 (2017) 653–661
the second hop experiences Gamma–Gamma (𝛤 𝛤 ) fading. Moreover, we study the e2e outage performance of a mixed RF/FSO scheme taken into account the effect of pointing errors in the FSO link. Furthermore, ASEP and ergodic channel capacity are also employed as the performance metrics in the system analysis. In addition, the scintillation effects on the system performance are considered and results for different regimes of irradiance fluctuations are presented. Having presented the state-of-the-art on the subject matter and the need for FSO communication systems in the CC-RANs, in Section 2, we present general overviews of the CC-RANs and the mixed RF/FSO relay schemes. Also, we present the system and channel models in Section 3. Section 4 provides performance metrics such as outage probability, ASEP, and ergodic channel capacity employed in the study. Results and discussion are presented in Section 5, and Section 6 concludes the paper.
measures can be adopted. One of such is the implementation of analog optical transmission technique based on radio over fiber (RoF) technology [7,11,14]. RoF technology is a scheme whose deployment depends largely on the availability of the installed optical fiber cables between various network facilities. On the other hand, when cell densification is considered, optical fiber deployment will be capital-intensive and time-consuming especially in installations that require trenching. In addition, acquisition of right-of-way for BSs locations could be one of the limiting factors for appropriate system deployment. Consequently, these challenges coupled with the limited amount of installed optical fiber cables, bring about the recognition of the feasibility of free-space optical (FSO) communication system. The FSO is a viable technology that can be employed for the RF signal transmission without the need for the installation of fiber cables. The idea of transmitting the RF signals over the FSO; that is, radio-on-FSO (RoFSO), takes advantages of high transmission capacity offers by the optical technologies and ease of deployment of wireless systems [15]. Despite the fact that FSO is a feasible optical technology with various applications, the balance between the required data rates and the limitations of atmospheric channel are the primary implementation challenges in the access networks. So, these challenges prevent FSO from being an efficient standalone fronthaul solution in the network [15]. In order to address the limitations, schemes such as relay-assisted transmission technologies can be implemented to enhance the performance of FSO communication in the access networks. Multiuser mixed RF/FSO communications have gained significant attentions in the literature because of their viabilities in addressing the challenges of spatial diversity and extending the coverage area of the system. The influence of RF cochannel interference on the performance of multiuser mixed RF/FSO relay network with opportunistic user scheduling was analyzed in [16]. In the study, eavesdropping attack was considered over the RF/FSO channels that follow Nakagami-m/Gamma– Gamma fading models, respectively. Also, closed-form expressions and an asymptotic expression for the outage probability were derived. Moreover, a link quality scheduling strategy was proposed in [17]. According to the scheduling strategy, the outage probability and the average bit error rate (BER) were analyzed. Also, asymptotic closed-form expressions were presented. Likewise, the performances of mixed RF/FSO systems were considered in [18] with decode-and-forward relaying. The considered system was based on the RF/FSO channels that follow Nakagami-m/Exponentiated Weibull fading models, respectively. Also, analytical expressions for the outage probability and average BER were presented using the Meijer’s G function. Furthermore, in [19], a dualhop relay system with space time block coding (STBC) was analyzed. In the analysis, the RF/FSO channels follow Rayleigh/Gamma–Gamma fading models, respectively. With pointing errors being considered, closed-form expressions for the end-to-end (e2e) outage probability and average symbol error probability (ASEP) were given in terms of the Meijer’s G functions. In addition, the performance of a multiuser mixed RF/FSO relaying with opportunistic scheduling was studied in [20]. In the study, a power allocation scheme was proposed in order to optimize the system performance. The considered system was based on the RF/FSO channels which follow Rayleigh/Gamma–Gamma fading models, respectively. Also, considering the pointing errors, closedform expressions were presented for the ASEP, outage probability, and ergodic channel capacity. Additionally, diversity order and coding gain are considered at high signal-to-noise ratio (SNR) regime. Furthermore, in [21] a multiuser dual-hop relaying system was considered over mixed RF/FSO links. Analytical expressions were presented for the e2e outage probability and ASEP of the user. Also, closed-form asymptotic expressions were derived for the e2e link. In this paper, we present a mixed RF/FSO relay scheme as an effective technique for realizing optical-wireless convergence in the dualhop communication systems. In this study, we assume an heterogeneous free space link where the first hop experiences Rayleigh fading whereas,
2. Overview of cloud computing-based radio access networks (CCRANs) and mixed RF/FSO relay schemes The increase in broadband connection demands for high-speed and high-capacity communication links in order to support various bandwidth-intensive applications and services. The high bit rate wireless signals can be efficiently supported by the optical link because of its massive aggregate bandwidth. This high bandwidth also enables multiple wireless services to share the same optical network infrastructure. The inherent bandwidth of optical networks can be exploited by the RoF and FSO to offer high throughput required by the CC-RANs. Furthermore, they have features that support centralized network control and simple remote antenna unit. These features are in accordance with the concept of C-RANs and permit easier maintenance, upgrade, and sharing of resources. 2.1. Cloud computing-based radio access networks (CC-RANs) The heterogeneous network (HetNet) architecture enables deployment of very dense and low-power-small-cells that leads to more efficient spatial spectrum reuse. At large, it enhances the cellular network efficiency by the deployment of low power nodes (LPNs). Deployment of LPNs offers high system capacity and relatively alleviate the related challenges of densely deployed macro base stations (MBSs) in the networks. Nonetheless, densely deployed small-cell presents some challenges regarding inter-cell/inter-tier interference and management issues. This may occur when MBSs and LPNs reuse the same spectral resources as anticipated in the 5G cellular networks and beyond [22– 24]. To attend to the issues, the C-RAN architectures that enable efficient implementation of advanced radio signal processing (RSP) techniques such as CA, coordinated multi-point (CoMP), and enhanced inter-cell interference cancellation (eICIC) can be implemented. The C-RAN architecture helps in suppressing intra-tier/inter-tier interference [22]. Moreover, C-RAN aids significantly in the reduction of the capital expenditure (CAPEX) and operation expenditure (OPEX) of the cellular networks [10]. Therefore, C-RANs have gained substantial research interests and have been acknowledged as an integral part of the 5G network [22–27]. However, stringent requirements are imposed on the fronthaul links that connect the RRHs to the BBU pool [8,22,24]. The requirements are even more severe when cooperative techniques such as CoMP are implemented. This is due to the fact that, CoMP scheme implementation limits the number of RRHs that can access the same BBU pool at a time. In order to address the fronthaul constraints, different system architectures have been presented to explore the potentials of C-RANs and cloud computing technologies. One of such architectures is the heterogeneous cloud radio access network (H-CRAN) that has been presented as a promising 5G network solution [22]. High-power nodes (HPNs) are used for seamless coverage and interference mitigation across the elements in the H-CRAN architecture [6]. Furthermore, unnecessary handover between BSs can be prevented with the aid of HPNs [6]. Besides, MIMO schemes can be integrated into 654
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Fig. 1. Schematic of F-RAN architecture.
the HPNs in order to improve the system coverage and capacity [22– 24]. Nevertheless, the performance of H-CRAN is limited by inability to exploit processing and storage capabilities in the edge devices. Similarly, additional burdens are inflicted on the backhaul/fronthaul links by the pervasive surge in the redundant information in the emerging networks. As a result, the fronthaul constraints for information exchange between the edge users are aggravated. Consequently, these challenges bring about the idea of fog RAN (F-RAN) which is another innovative CC-RAN technology [6,23,28]. Fog network is made up of various devices that are accessing the Internet and form several small clouds at the edge of the network [23]. The schematic of fog RAN (F-RAN) architecture is shown in Fig. 1. To alleviate the challenges of H-CRANs in F-RAN, fog-computing is incorporated into the edge devices by upgrading the conventional RRHs into fog-computing-based access points (F-APs) [5,6]. The upgrade is achieved by equipping the F-APs with certain caching, collaboration radio signal processing (CRSP), and cooperative radio resource management (CRRM) capabilities. As a result, part of radio signal processing and resource management can be realized locally [5,6]. Moreover, this enables edge devices like F-APs and smart user equipment (FUEs) to exhibit processing capabilities for certain task whereas those that cannot be efficiently executed by the edge devices are managed in the cloud [5,6]. By this, neighboring devices can effectively share resources like computing/storage capacity. Hence, this reduces the e2e latency and the amount of traffic to be supported by the fronthaul and the backhaul links [23]. However, reliability of data delivery and mobility management in the network requires further investigation. Likewise, lack of efficient resource coordination between edge devices may cause interference which can subsequently hindered system performance [6,23].
Generally, the C-RAN, H-CRAN, and F-RAN are promising CCRAN technologies for realizing the requirements of the 5G networks. Nevertheless, because of the huge amount of heterogeneous devices to be served, substantial traffic burdens are imposed on the backhaul/fronthaul links which limit CC-RAN practical applications in reallife scenarios. Consequently, transmission and reception of digital radio signal require compatible and standard interface for the connection of RRHs to the BBU pool by the optical fiber. In practice, the C-RAN configuration requires each RRH with dedicated link to the centralized BBU. In the 5G networks and beyond, huge number of RRHs may be deployed at locations that are highly challenging to be reached by fiber. Consequently, dedicated wired links from each RRH to the BBU may be impractical. Therefore, wireless fronthaul will be an ideal solution for better flexibility, high costefficiency, and easier deployment of the RRHs [29]. Hence, FSO communication systems are applicable in some regions within the mobile cellular networks where physical connections by optical fiber cables are impracticable or in the rural area that lacks optical fiber infrastructure. 2.2. Mixed RF/FSO relay schemes FSO communication system has been recognized as a viable alternative technology for attending to the broadband connectivity bottleneck. This makes it to be gaining considerable attention as a practical solution to the last mile problem for bridging the bandwidth gap between the network end users and the fiber optic networks [30,31]. Furthermore, due to its various advantages such as, ease of deployment, full duplex transmission, high bit rates, license-free operation, high transmission security, and protocol transparency, it has been attracting significant 655
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3. System and channel models
attention as a broadband access technology that can address the bandwidth requirements of the next-generation cellular systems [32]. Moreover, being an optical wireless technology, FSO system can be deployed in the regions where physical connection by optical fiber cables is impractical or in the rural areas that lacks fiber infrastructure. Besides, it is a very feasible solution in networks such as, metropolitan area network extension, fronthaul, and backhaul for wireless cellular networks [33– 36]. However, the FSO system performance is susceptible to atmospheric related issues such as atmospheric turbulence and pointing error [30– 32,36–38]. Consequently, these factors make the FSO system not to be as reliable as a typical optical fiber technology [14,33]. Moreover, the limitations make the FSO, not so efficient standalone fronthaul solution [39]. In the literature, various physical layer concepts such as maximum likelihood sequence detection (MLSD), adaptive optics, error control coding in conjunction with interleaving, and diversity schemes have been employed for mitigating turbulence-induced fading. Furthermore, certain physical layer schemes like hybrid RF/FSO, and relay-assisted transmission technologies have been gaining considerable recognition as an efficient fading-mitigation technique in the networks where FSO technology is being employed [18,37,38,40]. A notable and feasible method for alleviating turbulence-induced fading is the spatial diversity technique. Implementation of spatial diversity involves deployment of multiple transmit/receive apertures so as to establish and exploit additional degrees of freedom in the spatial dimension. Nevertheless, deployment of multiple transmit/receive apertures leads to another issues such as, increase in the system complexity and expenditure. Moreover, multiple aperture spacing requires substantial attention in order to prevent detrimental effects of spatial correlation on the system performance [37]. Dual-hop relaying has been widely implemented in the RF wireless communication systems to alleviate the implementation challenges of spatial diversity. Dual-hop relaying has also been employed to extend the coverage area in longdistance FSO transmission and to improve the quality of the received signal [18,31,41,42]. For instance, the coverage area of the long-reach and high-data rate 1.6-Tbit/s (16 × 100 Gbit/s) FSO system that we reported in [43] can further be enhanced by the dual-hop relaying. Relay-assisted transmission architecture creates a virtual multipleaperture system in order to offer advantages of MIMO schemes [40]. It exploits both the RF and the FSO features to achieve an effective communication system in a real-life scenario. Additionally, a relay-assisted transmission is a mixed RF/FSO dual-hop communication scheme, in which, the links from the source to the relay are RF links while the link between the relay and the destination is FSO link [18,44]. Therefore, RF transmission is employed at the first hop and FSO transmission is employed at the other. The main goal of the FSO link is to allow the RF users to communicate with the backbone network in order to fill the connectivity gap between the backbone network and the last-mile access network [37,38]. The architecture of mixed RF/FSO dual-hop system effectively address the last-mile transmission bottleneck. This can be attributed to its ability to multiplex multiple users with RF capabilities and their aggregation into a single high-speed FSO link to exploit the inherent optical capacity [37]. Furthermore, its implementation prevents all sort of interference because, RF and FSO operate on different frequency bands. Fig. 2 illustrates a mixed RF/FSO network in which, the source contains multiple RF users that are equipped with antennas. Moreover, at the destination, there is an FSO detector that is equipped with an aperture. Additionally, the source and the destination are interconnected by a relay which can be mounted on a high platform. The relay performs RF to FSO conversion in the system. Furthermore, the relay has one receive antenna for the RF signal reception and one transmit aperture for the optical signal transmission. Consequently, the input RF signal at the relay is converted into optical signal for retransmission from the relay to the destination. Therefore, the relay enables the RF users to get to the backbone network through the FSO link [37,38].
In this paper, we study a mixed RF/FSO dual-hop relay system with source nodes that transmit information to the destination node with the aid of a relay node as depicted in Fig. 2. The considered relay operates under the amplify-and-forward protocol. Moreover, we assume that the direct link between the source node and destination node is in deep fade, so, it is weak enough to be ignored. Furthermore, the links from the source nodes to the relay are RF links whereas the link between the relay and the destination is an FSO link. Therefore, the RF links and the FSO link are assumed to experience Rayleigh and Gamma–Gamma (𝛤 𝛤 ) fading, respectively. Moreover, we assume that the source nodes and the destination node are equipped with 𝑁𝑠 antennas and 𝑁𝑑 aperture, respectively. Besides, we assume that, the relay node is equipped with 𝑁𝑠,𝑟 receive antenna for the reception of RF signal and 𝑁𝑟,𝑑 transmit aperture for the transmission of optical signal. So, at the first hop, the symbol vector 𝑠 ∈ C𝑁𝑠 is transmitted from the source to the relay node and signal 𝑦𝑠,𝑟 ∈ C𝑁𝑟 received at the relay can be written as [45] √ 𝑁 𝑃𝑠 ∑𝑠 𝑦𝑠,𝑟 = ℎ 𝑠 + 𝑛𝑟 , (1) 𝑁𝑠 𝑘=1 𝑠,𝑟 where 𝑃𝑠 denotes the source transmit power, ℎ𝑠,𝑟 ∈ C𝑁𝑠,𝑟 ×𝑁𝑠 denotes the source–relay channel which is assumed to be Rayleigh fading channel, and 𝑛𝑟 ∈ C𝑁𝑟 ∼ (0, 𝑁01 ) is the additive white Gaussian noise (AWGN) vector at the relay node whose components are independent and identically distributed (i.i.d.) with zero mean and variance 𝜎𝑟2 . Furthermore, at the second hop, the RF–optical signal conversion for retransmission to the destination through the FSO links is assumed to be by subcarrier intensity modulation (SIM) scheme. The optical signal at the relay node can be defined as [46] (2)
𝑠𝑟 = 𝐺(1 + 𝜂𝑦𝑠,𝑟 ), where the relay gain, 𝐺 =
√
𝑃𝑟 𝑃𝑠 𝑁𝑠
‖ℎ𝑠,𝑟 ‖2 +𝑁01
, 𝑃𝑟 represents the output
optical power at the relay, 𝜂 denotes the electrical-to-optical conversion coefficient. We assume that the FSO link follows the 𝛤 𝛤 -distributed fading in order to consider a wide range of turbulence conditions [36]. This is due to the fact that, the 𝛤 𝛤 -distribution can be used to characterize the fading gains for weak to strong turbulence scenarios. So, we assume that, the fading gain 𝐼𝑚 (irradiance) is a random variable (RV) between the relay aperture and the receive aperture of the FSO link which follows the 𝛤 𝛤 -distribution. The probability density function (PDF) of 𝐼𝑚 based on the doubly stochastic theory of scintillation in which the smallscale irradiance fluctuations are assumed to be modulated by largescale irradiance fluctuations of the propagating wave can be defined as [36,47,48] ( √ ) (𝛼+𝛽) 2(𝛼𝛽)(𝛼+𝛽)∕2 (ℎa ) 2 −1 𝐾𝛼−𝛽 2 𝛼𝛽ℎa , (3) 𝑓𝐼𝑚 (𝐼𝑚 ) = 𝛤 (𝛼)𝛤 (𝛽) where 𝛤 (⋅) is the gamma function, 𝐾𝑣 (⋅) is the modified Bessel function of the second kind of order 𝑣, 𝛼 and 𝛽 are the effective number of large-scale and small-scale eddies of the scattering process, respectively. The variables 𝛼 and 𝛽 are related to the atmospheric conditions and for spherical wave propagation, they can be expressed respectively as [36,47–49] [ ( ) ]−1 2 0.49𝜎𝑅 𝛼 = exp −1 , (4a) 12∕5 (1 + 0.18𝑑 2 + 0.56𝜎𝑅 )7∕6 [ ( ) ]−1 2 (1 + 0.69𝜎 12∕5 )−5∕6 0.51𝜎𝑅 𝑅 𝛽 = exp −1 , (4b) 12∕5 (1 + 0.9𝑑 2 + 0.62𝑑 2 𝜎𝑅 )5∕6 2 ≜ 0.492 𝐶 2 𝑘7∕6 𝐿11∕6 is the Rytov variance, 𝑑 ≜ (𝑘𝐷2 ∕4𝐿)1∕2 , where 𝜎𝑅 𝑛 𝐷 denotes the aperture diameter of the receiver, 𝑘 ≜ 2𝜋∕𝜆 is the optical
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Fig. 2. Mixed RF/FSO dual-hop communication system.
wave number, 𝜆 denotes the wavelength, 𝐿 is the distance, and 𝐶𝑛2 is the altitude-dependent refractive index structure parameter expressed by the Hufnagel–Valley model as 𝐶𝑛2 (ℎ) = 0.00594(𝑣𝑤 ∕27)2 (10−5 ℎ)10 exp(−ℎ∕1000) + 2.7 × 10−16 exp(−ℎ∕1500) + 𝐴̂ exp(−ℎ∕100),
Therefore, we express the received signal after removing the direct current (DC) as [ (√ )] 𝑦𝑚 = 𝜉𝐺 𝜂 𝑃𝑠 (ℎ𝑠,𝑟 𝑠 + 𝑛𝑟 ) 𝐼𝑚 + 𝑛𝑚 . (10) Furthermore, in this work, we assume opportunistic schedule transmission. Basically, opportunistic scheduling scheme can be employed to select user with the best SNR to transmit at a specified period of time. So, based on this transmission, the e2e instantaneous SNR 𝛾𝑒2𝑒 for computing the outage probability is given by [20,37]
(5)
where 𝑣𝑤 is the root mean square wind velocity in m/s, ℎ is the altitude in m and 𝐴̂ is the nominal value of 𝐶𝑛2 (0) at the ground level in m−2∕3 . 2 , which is normally used for describing The scintillation index, 𝜎𝑁 the strength of atmospheric fading can be expressed as [36,50] 2 𝜎𝑁
E(𝐼𝑚2 ) ≜ [ ]2 − 1, E(𝐼𝑚 )
𝑃out = 𝐹𝛾𝑒2𝑒 (𝛾𝑡ℎ ) ≥ 𝐹𝛾Sel,𝑟 (𝛾𝑡ℎ ) + 𝐹𝛾𝑟,𝑑 (𝛾𝑡ℎ ) − 𝐹𝛾Sel,𝑟 (𝛾𝑡ℎ )𝐹𝛾𝑟,𝑑 (𝛾𝑡ℎ ) (11)
≃ 𝐹𝛾Sel,𝑟 (𝛾𝑡ℎ ) + 𝐹𝛾𝑟,𝑑 (𝛾𝑡ℎ )
(6)
where 𝛾Sel,r = 𝑃Sel |ℎSel,𝑟 |2 ∕(𝑁01 ) denotes the SNR of the selected user through opportunistic scheduling for transmission over the RF hop, 𝛾𝑟,𝑑 = 𝜂𝜉𝑃𝑟 |𝐼𝑚 |2 ∕(𝑁02 ) represents the SNR of the FSO hop, 𝐹𝛾Sel,𝑟 (𝛾) and 𝐹𝛾𝑟,𝑑 (𝛾) are the cumulative distribution functions (CDFs) which can be expressed respectively as [16,20,55] ) ( 𝑘 𝐾 𝐾 ∑ ∑ (−1)𝑘 ∑ 𝐹𝛾Sel,𝑟 (𝛾𝑡ℎ ) = (12a) 𝜆𝑛𝑡 ,𝑟 𝛾𝑡ℎ , exp − 𝑘! 𝑛 ,…,𝑛 𝑘=0 𝑡=1 1 𝑘 ] [ | 1, 𝜒1 𝑟(𝛼+𝛽−2) 𝜁 2 (𝛼𝛽)𝑟 3𝑟,1 𝐹𝛾𝑟,𝑑 (𝛾𝑡ℎ ) = , (12b) G𝑟+1,3𝑟+1 𝛾𝑡ℎ || (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑟(2𝑟) 𝛾̄𝑟,𝑑 | 𝜒2 , 0
where E(⋅) denotes an expectation operator. 2 can be expressed in terms of eddies of the scattering Furthermore, 𝜎𝑁 process (𝛼 and 𝛽) as [36,50–52] [ 2 0.49𝜎𝑅 2 𝜎𝑁 = exp 12∕5 (1 + 0.18𝑑 2 + 0.56𝜎𝑅 )7∕6 ] 12∕6 2 (1 + 0.69𝜎 0.51𝜎𝑅 )−5∕6 𝑅 + − 1, (7a) 12∕5 1 + 0.9𝑑 2 + 0.62𝑑 2 𝜎𝑅 1 1 1 = + + . (7b) 𝛼 𝛽 𝛼𝛽
where G(⋅) is the Meijer G-function, 𝛾̄𝑟,𝑑 = 𝜂𝜉𝑃𝑟 𝜇𝑟,𝑑 ∕(𝑁02 ), 𝜇 denotes
It is noteworthy that, weak irradiance fluctuations are usually characterized by the values of the scintillation index that are less than 2 < 1). However, in situations where the scintillation index is unity (𝜎𝑅 not less than unity, the atmospheric turbulence that the optical wave experiences during propagation can be classified as moderate to strong irradiance fluctuations. The received optical signal at the destination aperture can be expressed as [37,46,53,54] [ (√ )] 𝑁 𝑃𝑠 ∑𝑠 𝑦𝑚 = 𝜉𝐼𝑚 𝐺 1 + 𝜂 ℎ𝑠,𝑟 𝑠 + 𝑛𝑟 + 𝑛𝑚 , 𝑚 = 1, … , 𝑁𝑑 (8) 𝑁𝑠 𝑘=1
2
2
2
2
the mean power of |𝐼𝑚 |2 , 𝜒1 = 𝜁 𝑟+1 , … , 𝜁 𝑟+𝑟 , 𝜒2 = 𝜁𝑟 , … , 𝜁 +𝑟−1 , 𝑟 𝛽+𝑟−1 𝛼+𝑟−1 𝛽 𝛼 , … , , , … , , where 𝜁 is the ratio between the equivalent 𝑟 𝑟 𝑟 𝑟 beam radius and the pointing error displacement standard deviation at the receiver, parameter 𝑟 denotes the type of detection technique employed (i.e., 𝑟 = 1 and 𝑟 = 2 represent heterodyne detection and intensity modulation/direct detection (IM/DD)), respectively. The asymptotic outage performance at high SNRs can be defined as [20] ⎧[ 𝛾̄𝑢,𝑟 ]−𝐾 weak turbulence ⎪ ⎪ 𝛾out ∞ ]− 𝜈 𝑃out = ⎨[ − 2 (13) 2 ⎪ 𝛬 𝜈 𝛾̄ strong turbulence 𝑟,𝑑 ⎪ 𝛾out ⎩
where 𝜉 is the optical-to-electrical conversion coefficient and 𝑛𝑚 ∼ (0, 𝑁02 ) is an AWGN term over the FSO link, with zero mean, and variance 𝜎𝑚2 .
where 𝛾̄𝑢,𝗋 is the average SNR for identical channels case, 𝜈 { } min 𝜁 2 , 𝛼, 𝛽 , and 𝛬 can be expressed as [16,20]
4. Performance analysis In this section, we adopt outage probability, ASEP, and ergodic channel capacity as the performance metrics in the analysis. Outage probability is a key metric for describing performance in quasi-static fading channels and can be defined as the probability that the instantaneous SNR falls below a specific SNR threshold 𝛾𝑡ℎ . We assume that, 𝑁𝑑 = 𝑁𝑠,𝑟 = 𝑁𝑟,𝑑 = 1. So, Eq. (8) can be defined as [ (√ )] 𝑃𝑠 𝑦𝑚 = 𝜉𝐺 1 + 𝜂 𝑁 (ℎ 𝑠 + 𝑛𝑟 ) 𝐼𝑚 + 𝑛𝑚 . (9) 𝑁𝑠 𝑠 𝑠,𝑟
𝛬=
∏6 ( )𝑏 ∕2 6 ∑ 𝑟(𝛼+𝛽−2) 𝜁 2 (𝛼𝛽)𝑟 𝑘 𝑗=1,𝑗≠𝑘 𝛤 (𝑏𝑗 − 𝑏𝑘 )𝛤 (𝑏𝑘 ) , ∏ (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑘=1 3 𝛤 (𝑎𝑗 − 𝑏𝑘 )𝛤 (1 + 𝑏𝑘 ) 𝑟(2𝑟)
=
(14)
𝑗=2
where 𝑎𝑗 = 𝜒1 (𝑗) for 𝑗 = 1, … , 3, 𝑏𝑗 = 𝜒2 (𝑗) for 𝑗 = 1, … , 6, and 𝑏𝑘 = 𝜈. The ASEP, 𝑃𝑒 , can be expressed in terms of the CDF of the received SNR, 𝛾𝑒2𝑒 , denoted by 𝐹𝛾𝑒2𝑒 as [56,57] ∞
𝑃𝑒 = 𝜚 657
∫0
𝑒−𝑏𝛾 √ 𝐹𝛾𝑒2𝑒 (𝛾) 𝑑𝛾, 𝛾
(15)
I.A. Alimi et al.
where 𝜚 =
Optics Communications 402 (2017) 653–661 √ 𝑎 𝑏 √ , 2 𝜋
parameters 𝑎 and 𝑏 are modulation-specific constant
terms such that for binary phase-shift keying, 𝑎 = 𝑏 = 1, for 𝑀-ary phase-shift keying 𝑎 = 2, 𝑏 = sin2 (𝜋∕𝑀), and for 𝑀-ary quadrature amplitude modulation, 𝑎 = 4(𝑀 1∕2 − 1)∕𝑀 1∕2 , 𝑏 = 3∕(2(𝑀 − 1)) [57]. So, 𝐹𝛾𝑒2𝑒 (𝛾) can be expressed as [20] ( 𝑘 ) 𝐾 𝐾 ∑ ∑ (−1)𝑘 ∑ 𝐹𝛾𝑒2𝑒 (𝛾) = exp − 𝜆𝑛𝑡 ,𝑟 𝛾𝑡ℎ 𝑘! 𝑛 ,…,𝑛 𝑘=0 𝑡=1 1 𝑘 { ( ) 𝑟(𝛼+𝛽−2) 𝜁 2 (𝛼𝛽)𝑟 1− G3𝑟,1 × 𝑟(2𝑟) 𝛾̄𝑟,𝑑 (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑟+1,3𝑟+1 [ ]} (𝛼𝛽)𝑟 || 1, 𝜒1 𝑟(𝛼+𝛽−2) 𝜁 2 × 𝛾 G3𝑟,1 + 𝑟(2𝑟) 𝛾̄𝑟,𝑑 || 𝜒2 , 0 (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑟+1,3𝑟+1 [ ] (𝛼𝛽)𝑟 || 1, 𝜒1 𝛾 × , (16) 𝑟(2𝑟) 𝛾̄𝑟,𝑑 || 𝜒2 , 0
Fig. 3. Outage performance of a dual-hop multiuser mixed RF/FSO wireless communication system considering the impact of pointing errors at the FSO links.
Then, 𝑃𝑒 can be expressed as ⎡𝐾 ⎢∑ (−1)𝑘 𝑃𝑒 = 𝜚 ⎢ ⎢𝑘=0 𝑘! ⎣
(
𝐾 ∑
𝑏+
𝑛1 ,…,𝑛𝑘
𝑘 ∑
)− 21 𝜆𝑛𝑡 ,𝑟
RF/FSO wireless communication system. We study the effect of varying the ratio between the equivalent beam radius and the pointing error displacement standard deviation in order to analyze the resultant effects of pointing errors and atmospheric turbulence on the system outage performance. Initially, we consider strong turbulence conditions with channel parameters 𝛼 = 0.8 and 𝛽 = 1.5. Furthermore, for a 𝛾𝑡ℎ = 5 dB and two users, we employ different values of 𝜁 (i.e. 𝜁 = 0.5, 0.7, 0.9, 1.5, 2.1). Fig. 3 shows the plot of outage probability for different values of average SNR/hop and 𝜁. We observed that, as 𝜁 increases, the detrimental effect of pointing error on the system performance decreases, so, the outage performance improves significantly. The observation is due to the fact that, as the value of 𝜁 increases, the effect of pointing error decreases and the probability of outage decreases. On the other hand, when the value of 𝜁 decreases, the effect of pointing error increases and consequently, the probability of outage increases. Moreover, the results show that as 𝜁 → ∞, we are approaching a non-pointing error scenario. Furthermore, it is noteworthy that, the system diversity order is determine by the min{𝜁 2 , 𝛼, 𝛽}∕2. So, when the value of 𝜁 2 is less than that of 𝛼 and 𝛽, it will affect diversity order of the system accordingly. However, when the value of 𝜁 2 is larger than that of 𝛼 and 𝛽, it will affect just the coding gain of the system. In essence, an increase in 𝜁 means that additional number of distributed RRHs and the associated RF users can communicate effectively via the backbone network to the BBU pool within an allowable latency. Furthermore, in Fig. 4, we present results of varying the atmospheric turbulence parameters 𝛼 ∈ (0.5, 0.6, 2.3, 4) and 𝛽 ∈ (0.5, 0.5, 0.9, 1.3) on the system outage performance. In the analysis, we assume a 𝛾𝑡ℎ = 5 dB and 𝜁 = 2.1. We observe that, a decrease in the value of 𝛼 or 𝛽 leads to an increase in the detrimental effect of atmospheric turbulence. Consequently, the system performance is degraded. Also, we observe that the outage probability increases with an increase in the atmospheric 2 = 8 turbulence strength and the performance of the system with 𝜎𝑁 depicts the worst case scenario. For instance, to achieve an outage 2 = 1.21, about 20 dB is required. This probability of 10−1 , with 𝜎𝑁 subsequently increased to about 38 dB for the same outage probability 2 = 7. The result demonstrates that system with low turbulence when 𝜎𝑁 is relatively power efficient and has best performance. So, in essence, 2 a decrease in the value of 𝛼 or 𝛽 or an increase in the value of 𝜎𝑁 reduces the number of RRHs as well as the associated RF users that can be supported efficiently by the mobile fronthaul within an allowable latency. Moreover, in Fig. 5, we show results of varying the channel parameters 𝛼 ∈ (0.8, 1.8, 2.6) and 𝛽 (1.8, 2.6, 4) on the error probability performance of a dual-hop multiuser mixed RF/FSO wireless communication system. Also, we use 𝜁 = 2.1 and assume that the modulationspecific constant terms are equal. So, parameters 𝑎 and 𝑏 are equal to one
𝑡=1
⎧ ⎪ 𝑟(𝛼+𝛽−2) 𝜁 2 × ⎨𝛤 (1∕2) − G3𝑟,2 (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑟+2,3𝑟+1 ⎪ ⎩ ⎡ ⎤⎫ | 1 , 1, 𝜒1 ⎥⎪ ⎢ (𝛼𝛽)𝑟 |2 × ⎢( ) ⎥⎬ | ∑ ⎢ 𝑏 + 𝑘𝑡=1 𝜆𝑛𝑡 ,𝑟 𝑟(2𝑟) 𝛾̄𝑟,𝑑 | 𝜒2 , 0 ⎥⎪ ⎣ ⎦⎭ ]⎤ [ 1 1 𝑟(𝛼+𝛽−2) 𝜁 2 (𝛼𝛽)𝑟 || 2 , 1, 𝜒1 ⎥ + 𝑏− 2 G3𝑟,2 ⎥. 𝑟+2,3𝑟+1 𝑟(2𝑟) 𝛾̄ 𝑏 | (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑟,𝑑 | 𝜒2 , 0 ⎥ ⎦
(17)
Furthermore, the ergodic capacity 𝐶erg is another important performance metric that quantifies the maximum achievable transmission rate under which errors are recoverable. The 𝐶erg can be mathematically formulated in terms of the PDF of 𝛾𝑒2𝑒 as [20,32,36,58] 𝐶erg ≜ E⟨𝐶⟩ =
1 ln(2) ∫0
∞
(18)
ln(1 + 𝛾)𝑓𝛾𝑒2𝑒 (𝛾)𝑑𝛾.
where E is the expectation operator. So, 𝐶erg can be expressed as ) {𝐾 ) ( 𝑘 [ ( 𝑘 𝐾 ∑ (−1)𝑘 ∑ ∑ ∑ 1 𝐶erg = 𝜆𝑛𝑡 ,𝑟 𝜆𝑛𝑡 ,𝑟 E𝑖 − exp ln(2) 𝑘=0 𝑘! 𝑛 ,…,𝑛 𝑡=1 𝑡=1 1 𝑘 ( 𝑟(𝛼+𝛽−2) 𝜁 2 − G0,1∶1,2∶3𝑟,0 1,0∶2,2∶𝑟,3𝑟 (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) [ ] ∑ (𝛼𝛽)𝑟 𝑘𝑡=1 𝜆𝑛𝑡 ,𝑟 | 1 | 1, 1 | 𝜒 1 | 1 | | × ∑𝑘 , | − | 1, 0 | 𝜒 𝑟(2𝑟) 𝛾̄𝗋,𝖽 | | | 2 𝑡=1 𝜆𝑛𝑡 ,𝑟 ( 𝑘 )2 ∑ − 𝜆𝑛𝑡 ,𝑟 G0,1∶1,2∶3𝑟,1 1,0∶2,2∶𝑟+1,3𝑟+1 [ ×
𝑡=1
∑𝑘
(𝛼𝛽)𝑟
1
𝑡=1
, 𝜆𝑛𝑡 ,𝑟
𝑟(𝛼+𝛽−2) 𝜁 2
∑𝑘
𝑡=1 𝜆𝑛𝑡 ,𝑟 || 2 || 1, 1 || 1, 𝜒1 | − | 1, 0 | 𝜒 , 0 (2𝑟) 𝑟 𝛾̄𝗋,𝖽 | | | 2
[
G3𝑟+2,1 𝑟+2,3𝑟+2
])]
(𝛼𝛽)𝑟 || 0, 1, 𝜒1 𝑟(2𝑟) 𝛾̄𝗋,𝖽 || 𝜒2 , 0
]}
, (19) (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) [ ] ||| where G 𝑍1 , 𝑍2 |.|.|. represents the extended generalized bivariate ||| Meijer G-function. +
5. Results and discussion In this section, we present results of our studies on the e2e outage probability, ASEP, and ergodic channel capacity of the considered mixed 658
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Optics Communications 402 (2017) 653–661
Fig. 4. Outage performance of a dual-hop multiuser mixed RF/FSO wireless communication system considering different atmospheric conditions.
Fig. 6. Ergodic channel capacity of a dual-hop multiuser mixed RF/FSO wireless communication system considering different atmospheric conditions.
we have assumed Rayleigh fading for the RF links and 𝛤 𝛤 -distributed fading channel for the FSO link. Furthermore, we have studied the effect of atmospheric turbulence as well as pointing errors between the relay and the destination on the system performance. Consequently, we have shown that, a decrease in the value of the effective number of large-scale or small-scale eddies of the scattering process reduces the total number of the RRHs and the associated RF users that can be supported efficiently by the mobile fronthaul within an allowable latency. Moreover, we have demonstrated that, an increase in the ratio between the equivalent beam radius and the pointing error displacement standard deviation at the receiver means that a considerable number of distributed RRHs can communicate efficiently via the backbone network to the BBU pool within a specified latency. The results presented can be beneficial in the system evaluation, optimization, and prediction of mixed RF/FSO dual-hop relay performance in real-life scenarios deployment.
Fig. 5. ASEP of a dual-hop multiuser mixed RF/FSO wireless communication system considering different atmospheric conditions.
Acknowledgments (i.e. 𝑎 = 𝑏 = 1). This condition corresponds to a case of binary phase-shift keying modulation format. We observe that, an increase in the value of 𝛼 or 𝛽 leads to a decrease in the detrimental effect of atmospheric turbulence. Therefore, the system performance is enhanced. Also, we 2 . For instance, observe that the ASEP increases with an increase in the 𝜎𝑁 2 = 0.73, about 22 dB is required. to achieve an ASEP of 10−4 , with 𝜎𝑁 The value consequently increased to about 35 dB for the same ASEP 2 = 2.5. The result shows that an increase in the 𝜎 2 deteriorates when 𝜎𝑁 𝑁 the system performance. Moreover, we study the effect of varying the channel parameters 𝛼 (i.e. 𝛼 = 15.7, 9.7, 5.7) and 𝛽 (i.e. 𝛽 = 6, 3, 3) on the ergodic channel capacity of a dual-hop multiuser mixed RF/FSO wireless communication system for 𝜁 = 2.1. The plot of ergodic channel capacity is shown in Fig. 6. We observe that, an increase in the value of channel parameters leads to a decrease in the degradation caused by the atmospheric turbulence. Consequently, the system performance is enhanced and more RRHs can be supported efficiently. For instance, we observe that, to achieve a system capacity of 6 𝑏∕𝑠∕𝐻𝑧 with 𝛼 = 5.7 and 𝛽 = 3, about 35 𝑑𝐵 SNR/Hop is required whereas, for 𝛼 = 15.7 and 𝛽 = 6, of the same capacity, about 25 𝑑𝐵 SNR/Hop is required.
This work is supported by the Fundação para a Ciência e a Tecnologia (FCT) under the Ph.D. grant PD/BD/52590/2014. It is also supported by the FCT/MEC through the national funds and when applicable cofunded by FEDER–PT2020 partnership agreement under the project UID/EEA/50008/2013. Also, it is funded by the European Structural Investment Funds (ESIF), through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) under FutPON project [Nr. 003145 (POCI-01-0247-FEDER-003145)]. References [1] M. Ayyash, H. Elgala, A. Khreishah, V. Jungnickel, T. Little, S. Shao, M. Rahaim, D. Schulz, J. Hilt, R. Freund, Coexistence of WiFi and LiFi toward 5G: concepts, opportunities, and challenges, IEEE Commun. Mag. 54 (2) (2016) 64–71. http: //dx.doi.org/10.1109/MCOM.2016.7402263. [2] J. Wu, Z. Zhang, Y. Hong, Y. Wen, Cloud radio access network (C-RAN): a primer, IEEE Netw. 29 (1) (2015) 35–41. http://dx.doi.org/10.1109/MNET.2015.7018201. [3] H. Dahrouj, A. Douik, O. Dhifallah, T.Y. Al-Naffouri, M.S. Alouini, Resource allocation in heterogeneous cloud radio access networks: advances and challenges, IEEE Wirel. Commun. 22 (3) (2015) 66–73. http://dx.doi.org/10.1109/MWC.2015. 7143328. [4] U. Siddique, H. Tabassum, E. Hossain, D.I. Kim, Wireless backhauling of 5G small cells: challenges and solution approaches, IEEE Wirel. Commun. 22 (5) (2015) 22– 31. http://dx.doi.org/10.1109/MWC.2015.7306534. [5] M. Peng, K. Zhang, Recent advances in Fog radio access networks: Performance analysis and radio resource allocation, IEEE Access 4 (2016) 5003–5009. http: //dx.doi.org/10.1109/ACCESS.2016.2603996. [6] M. Peng, S. Yan, K. Zhang, C. Wang, Fog-computing-based radio access networks: issues and challenges, IEEE Netw. 30 (4) (2016) 46–53. http://dx.doi.org/10.1109/ MNET.2016.7513863.
6. Conclusion In this paper, we have presented a mixed RF/FSO dual-hop relay system as a viable backhaul/fronthaul solution for the 5G cellular networks and beyond in which CC-RANs is expected to be implemented. Furthermore, we have studied different performance metrics such as e2e outage probability, ASEP, and ergodic channel capacity. In the analysis, 659
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Optics Communications journal homepage: www.elsevier.com/locate/optcom
Analysis of multiuser mixed RF/FSO relay networks for performance improvements in Cloud Computing-Based Radio Access Networks (CC-RANs) Isiaka A. Alimi *, Paulo P. Monteiro, António L. Teixeira Instituto de Telecomunicações, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal Department of Electronics, Telecommunications and Informatics, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal
a r t i c l e
i n f o
MSC 2010: 00-01 99-00 Keywords: Atmospheric turbulence Gamma–gamma fading Cloud computing-based radio access networks (CC-RANs) Multiuser mixed RF/FSO relay networks Pointing error Rayleigh fading
a b s t r a c t The key paths toward the fifth generation (5G) network requirements are towards centralized processing and small-cell densification systems that are implemented on the cloud computing-based radio access networks (CCRANs). The increasing recognitions of the CC-RANs can be attributed to their valuable features regarding system performance optimization and cost-effectiveness. Nevertheless, realization of the stringent requirements of the fronthaul that connects the network elements is highly demanding. In this paper, considering the small-cell network architectures, we present multiuser mixed radio-frequency/free-space optical (RF/FSO) relay networks as feasible technologies for the alleviation of the stringent requirements in the CC-RANs. In this study, we use the end-to-end (e2e) outage probability, average symbol error probability (ASEP), and ergodic channel capacity as the performance metrics in our analysis. Simulation results show the suitability of deployment of mixed RF/FSO schemes in the real-life scenarios. © 2017 Elsevier B.V. All rights reserved.
1. Introduction There are growing tendencies for high-bandwidth transmission systems in the cellular mobile networks because of high increase in the broadband connections and the subsequent traffic on yearly basis [1]. The fifth generation (5G) wireless communication system has been noted to be a promising technology for supporting the system capacity growth and coverage in an efficient way [2–4]. The key technology for the realization of the 5G network requirements is the cloud radio access network (C-RAN). C-RAN is an emerging architecture in which cloud computing is being integrated into the radio access networks (RANs) [5,6]. Furthermore, the C-RAN has a centralized system in which the baseband processing is realized in the base band unit (BBU) pool. Moreover, a set of densely deployed remote radio heads (RRHs) are connected to the BBU pool by means of the fronthaul links. C-RANs have the capacity for enhancing shared spectrum access, enable cooperative spectrum sensing, as well as facilitating device-to-device (D2D) communications [5]. Furthermore, the C-RANs are futuristic technologies with high potential for realizing significant gains in spectral efficiency (SE) and energy efficiency (EE) for the 5G cellular networks and beyond. Nevertheless, C-RANs challenges are based on the constrained fronthaul.
Basically, the stringent requirements to facilitate centralized processing in the BBU pool are the employment of high bandwidth and low latency fronthaul for the network elements interconnection. However, in practice, fronthaul links are capacity and time-delay constrained. This eventually leads to significant degradation on the C-RAN performances [5,6]. The C-RAN architecture normally employs common public radio interface (CPRI) or open base-station initiatives (OBSAI) interface for digital fronthauling [7,8]. However, optical fronthaul in which these interfaces are implemented requires large bandwidth. This is as a result of the high resolution bits required for the digitalization process of the radio-frequency (RF) samples [9]. Therefore, the associated bandwidth inefficiency could limit or make C-RANs unrealistic to meet the performance requirements of the 5G wireless communication systems in which massive multiple-input multiple-output (massive MIMO) antenna systems will be integrated [10,11]. For instance, with carrier aggregation (CA) of long term evolution-advanced (LTE-A) of five 20 MHz mobile signals with 3 sectors, and 8 × 8 MIMO antennas, about 147.5 Gb/s fronthaul data rate will be required by the CPRI [12,13]. Consequently, for the realization of bandwidth-efficient mobile fronthaul, advanced
* Corresponding author at: Department of Electronics, Telecommunications and Informatics, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal.
E-mail addresses: [email protected] (I.A. Alimi), [email protected] (P.P. Monteiro), [email protected] (A.L. Teixeira). http://dx.doi.org/10.1016/j.optcom.2017.06.097 Received 16 May 2017; Received in revised form 13 June 2017; Accepted 28 June 2017 0030-4018/© 2017 Elsevier B.V. All rights reserved.
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the second hop experiences Gamma–Gamma (𝛤 𝛤 ) fading. Moreover, we study the e2e outage performance of a mixed RF/FSO scheme taken into account the effect of pointing errors in the FSO link. Furthermore, ASEP and ergodic channel capacity are also employed as the performance metrics in the system analysis. In addition, the scintillation effects on the system performance are considered and results for different regimes of irradiance fluctuations are presented. Having presented the state-of-the-art on the subject matter and the need for FSO communication systems in the CC-RANs, in Section 2, we present general overviews of the CC-RANs and the mixed RF/FSO relay schemes. Also, we present the system and channel models in Section 3. Section 4 provides performance metrics such as outage probability, ASEP, and ergodic channel capacity employed in the study. Results and discussion are presented in Section 5, and Section 6 concludes the paper.
measures can be adopted. One of such is the implementation of analog optical transmission technique based on radio over fiber (RoF) technology [7,11,14]. RoF technology is a scheme whose deployment depends largely on the availability of the installed optical fiber cables between various network facilities. On the other hand, when cell densification is considered, optical fiber deployment will be capital-intensive and time-consuming especially in installations that require trenching. In addition, acquisition of right-of-way for BSs locations could be one of the limiting factors for appropriate system deployment. Consequently, these challenges coupled with the limited amount of installed optical fiber cables, bring about the recognition of the feasibility of free-space optical (FSO) communication system. The FSO is a viable technology that can be employed for the RF signal transmission without the need for the installation of fiber cables. The idea of transmitting the RF signals over the FSO; that is, radio-on-FSO (RoFSO), takes advantages of high transmission capacity offers by the optical technologies and ease of deployment of wireless systems [15]. Despite the fact that FSO is a feasible optical technology with various applications, the balance between the required data rates and the limitations of atmospheric channel are the primary implementation challenges in the access networks. So, these challenges prevent FSO from being an efficient standalone fronthaul solution in the network [15]. In order to address the limitations, schemes such as relay-assisted transmission technologies can be implemented to enhance the performance of FSO communication in the access networks. Multiuser mixed RF/FSO communications have gained significant attentions in the literature because of their viabilities in addressing the challenges of spatial diversity and extending the coverage area of the system. The influence of RF cochannel interference on the performance of multiuser mixed RF/FSO relay network with opportunistic user scheduling was analyzed in [16]. In the study, eavesdropping attack was considered over the RF/FSO channels that follow Nakagami-m/Gamma– Gamma fading models, respectively. Also, closed-form expressions and an asymptotic expression for the outage probability were derived. Moreover, a link quality scheduling strategy was proposed in [17]. According to the scheduling strategy, the outage probability and the average bit error rate (BER) were analyzed. Also, asymptotic closed-form expressions were presented. Likewise, the performances of mixed RF/FSO systems were considered in [18] with decode-and-forward relaying. The considered system was based on the RF/FSO channels that follow Nakagami-m/Exponentiated Weibull fading models, respectively. Also, analytical expressions for the outage probability and average BER were presented using the Meijer’s G function. Furthermore, in [19], a dualhop relay system with space time block coding (STBC) was analyzed. In the analysis, the RF/FSO channels follow Rayleigh/Gamma–Gamma fading models, respectively. With pointing errors being considered, closed-form expressions for the end-to-end (e2e) outage probability and average symbol error probability (ASEP) were given in terms of the Meijer’s G functions. In addition, the performance of a multiuser mixed RF/FSO relaying with opportunistic scheduling was studied in [20]. In the study, a power allocation scheme was proposed in order to optimize the system performance. The considered system was based on the RF/FSO channels which follow Rayleigh/Gamma–Gamma fading models, respectively. Also, considering the pointing errors, closedform expressions were presented for the ASEP, outage probability, and ergodic channel capacity. Additionally, diversity order and coding gain are considered at high signal-to-noise ratio (SNR) regime. Furthermore, in [21] a multiuser dual-hop relaying system was considered over mixed RF/FSO links. Analytical expressions were presented for the e2e outage probability and ASEP of the user. Also, closed-form asymptotic expressions were derived for the e2e link. In this paper, we present a mixed RF/FSO relay scheme as an effective technique for realizing optical-wireless convergence in the dualhop communication systems. In this study, we assume an heterogeneous free space link where the first hop experiences Rayleigh fading whereas,
2. Overview of cloud computing-based radio access networks (CCRANs) and mixed RF/FSO relay schemes The increase in broadband connection demands for high-speed and high-capacity communication links in order to support various bandwidth-intensive applications and services. The high bit rate wireless signals can be efficiently supported by the optical link because of its massive aggregate bandwidth. This high bandwidth also enables multiple wireless services to share the same optical network infrastructure. The inherent bandwidth of optical networks can be exploited by the RoF and FSO to offer high throughput required by the CC-RANs. Furthermore, they have features that support centralized network control and simple remote antenna unit. These features are in accordance with the concept of C-RANs and permit easier maintenance, upgrade, and sharing of resources. 2.1. Cloud computing-based radio access networks (CC-RANs) The heterogeneous network (HetNet) architecture enables deployment of very dense and low-power-small-cells that leads to more efficient spatial spectrum reuse. At large, it enhances the cellular network efficiency by the deployment of low power nodes (LPNs). Deployment of LPNs offers high system capacity and relatively alleviate the related challenges of densely deployed macro base stations (MBSs) in the networks. Nonetheless, densely deployed small-cell presents some challenges regarding inter-cell/inter-tier interference and management issues. This may occur when MBSs and LPNs reuse the same spectral resources as anticipated in the 5G cellular networks and beyond [22– 24]. To attend to the issues, the C-RAN architectures that enable efficient implementation of advanced radio signal processing (RSP) techniques such as CA, coordinated multi-point (CoMP), and enhanced inter-cell interference cancellation (eICIC) can be implemented. The C-RAN architecture helps in suppressing intra-tier/inter-tier interference [22]. Moreover, C-RAN aids significantly in the reduction of the capital expenditure (CAPEX) and operation expenditure (OPEX) of the cellular networks [10]. Therefore, C-RANs have gained substantial research interests and have been acknowledged as an integral part of the 5G network [22–27]. However, stringent requirements are imposed on the fronthaul links that connect the RRHs to the BBU pool [8,22,24]. The requirements are even more severe when cooperative techniques such as CoMP are implemented. This is due to the fact that, CoMP scheme implementation limits the number of RRHs that can access the same BBU pool at a time. In order to address the fronthaul constraints, different system architectures have been presented to explore the potentials of C-RANs and cloud computing technologies. One of such architectures is the heterogeneous cloud radio access network (H-CRAN) that has been presented as a promising 5G network solution [22]. High-power nodes (HPNs) are used for seamless coverage and interference mitigation across the elements in the H-CRAN architecture [6]. Furthermore, unnecessary handover between BSs can be prevented with the aid of HPNs [6]. Besides, MIMO schemes can be integrated into 654
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Fig. 1. Schematic of F-RAN architecture.
the HPNs in order to improve the system coverage and capacity [22– 24]. Nevertheless, the performance of H-CRAN is limited by inability to exploit processing and storage capabilities in the edge devices. Similarly, additional burdens are inflicted on the backhaul/fronthaul links by the pervasive surge in the redundant information in the emerging networks. As a result, the fronthaul constraints for information exchange between the edge users are aggravated. Consequently, these challenges bring about the idea of fog RAN (F-RAN) which is another innovative CC-RAN technology [6,23,28]. Fog network is made up of various devices that are accessing the Internet and form several small clouds at the edge of the network [23]. The schematic of fog RAN (F-RAN) architecture is shown in Fig. 1. To alleviate the challenges of H-CRANs in F-RAN, fog-computing is incorporated into the edge devices by upgrading the conventional RRHs into fog-computing-based access points (F-APs) [5,6]. The upgrade is achieved by equipping the F-APs with certain caching, collaboration radio signal processing (CRSP), and cooperative radio resource management (CRRM) capabilities. As a result, part of radio signal processing and resource management can be realized locally [5,6]. Moreover, this enables edge devices like F-APs and smart user equipment (FUEs) to exhibit processing capabilities for certain task whereas those that cannot be efficiently executed by the edge devices are managed in the cloud [5,6]. By this, neighboring devices can effectively share resources like computing/storage capacity. Hence, this reduces the e2e latency and the amount of traffic to be supported by the fronthaul and the backhaul links [23]. However, reliability of data delivery and mobility management in the network requires further investigation. Likewise, lack of efficient resource coordination between edge devices may cause interference which can subsequently hindered system performance [6,23].
Generally, the C-RAN, H-CRAN, and F-RAN are promising CCRAN technologies for realizing the requirements of the 5G networks. Nevertheless, because of the huge amount of heterogeneous devices to be served, substantial traffic burdens are imposed on the backhaul/fronthaul links which limit CC-RAN practical applications in reallife scenarios. Consequently, transmission and reception of digital radio signal require compatible and standard interface for the connection of RRHs to the BBU pool by the optical fiber. In practice, the C-RAN configuration requires each RRH with dedicated link to the centralized BBU. In the 5G networks and beyond, huge number of RRHs may be deployed at locations that are highly challenging to be reached by fiber. Consequently, dedicated wired links from each RRH to the BBU may be impractical. Therefore, wireless fronthaul will be an ideal solution for better flexibility, high costefficiency, and easier deployment of the RRHs [29]. Hence, FSO communication systems are applicable in some regions within the mobile cellular networks where physical connections by optical fiber cables are impracticable or in the rural area that lacks optical fiber infrastructure. 2.2. Mixed RF/FSO relay schemes FSO communication system has been recognized as a viable alternative technology for attending to the broadband connectivity bottleneck. This makes it to be gaining considerable attention as a practical solution to the last mile problem for bridging the bandwidth gap between the network end users and the fiber optic networks [30,31]. Furthermore, due to its various advantages such as, ease of deployment, full duplex transmission, high bit rates, license-free operation, high transmission security, and protocol transparency, it has been attracting significant 655
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3. System and channel models
attention as a broadband access technology that can address the bandwidth requirements of the next-generation cellular systems [32]. Moreover, being an optical wireless technology, FSO system can be deployed in the regions where physical connection by optical fiber cables is impractical or in the rural areas that lacks fiber infrastructure. Besides, it is a very feasible solution in networks such as, metropolitan area network extension, fronthaul, and backhaul for wireless cellular networks [33– 36]. However, the FSO system performance is susceptible to atmospheric related issues such as atmospheric turbulence and pointing error [30– 32,36–38]. Consequently, these factors make the FSO system not to be as reliable as a typical optical fiber technology [14,33]. Moreover, the limitations make the FSO, not so efficient standalone fronthaul solution [39]. In the literature, various physical layer concepts such as maximum likelihood sequence detection (MLSD), adaptive optics, error control coding in conjunction with interleaving, and diversity schemes have been employed for mitigating turbulence-induced fading. Furthermore, certain physical layer schemes like hybrid RF/FSO, and relay-assisted transmission technologies have been gaining considerable recognition as an efficient fading-mitigation technique in the networks where FSO technology is being employed [18,37,38,40]. A notable and feasible method for alleviating turbulence-induced fading is the spatial diversity technique. Implementation of spatial diversity involves deployment of multiple transmit/receive apertures so as to establish and exploit additional degrees of freedom in the spatial dimension. Nevertheless, deployment of multiple transmit/receive apertures leads to another issues such as, increase in the system complexity and expenditure. Moreover, multiple aperture spacing requires substantial attention in order to prevent detrimental effects of spatial correlation on the system performance [37]. Dual-hop relaying has been widely implemented in the RF wireless communication systems to alleviate the implementation challenges of spatial diversity. Dual-hop relaying has also been employed to extend the coverage area in longdistance FSO transmission and to improve the quality of the received signal [18,31,41,42]. For instance, the coverage area of the long-reach and high-data rate 1.6-Tbit/s (16 × 100 Gbit/s) FSO system that we reported in [43] can further be enhanced by the dual-hop relaying. Relay-assisted transmission architecture creates a virtual multipleaperture system in order to offer advantages of MIMO schemes [40]. It exploits both the RF and the FSO features to achieve an effective communication system in a real-life scenario. Additionally, a relay-assisted transmission is a mixed RF/FSO dual-hop communication scheme, in which, the links from the source to the relay are RF links while the link between the relay and the destination is FSO link [18,44]. Therefore, RF transmission is employed at the first hop and FSO transmission is employed at the other. The main goal of the FSO link is to allow the RF users to communicate with the backbone network in order to fill the connectivity gap between the backbone network and the last-mile access network [37,38]. The architecture of mixed RF/FSO dual-hop system effectively address the last-mile transmission bottleneck. This can be attributed to its ability to multiplex multiple users with RF capabilities and their aggregation into a single high-speed FSO link to exploit the inherent optical capacity [37]. Furthermore, its implementation prevents all sort of interference because, RF and FSO operate on different frequency bands. Fig. 2 illustrates a mixed RF/FSO network in which, the source contains multiple RF users that are equipped with antennas. Moreover, at the destination, there is an FSO detector that is equipped with an aperture. Additionally, the source and the destination are interconnected by a relay which can be mounted on a high platform. The relay performs RF to FSO conversion in the system. Furthermore, the relay has one receive antenna for the RF signal reception and one transmit aperture for the optical signal transmission. Consequently, the input RF signal at the relay is converted into optical signal for retransmission from the relay to the destination. Therefore, the relay enables the RF users to get to the backbone network through the FSO link [37,38].
In this paper, we study a mixed RF/FSO dual-hop relay system with source nodes that transmit information to the destination node with the aid of a relay node as depicted in Fig. 2. The considered relay operates under the amplify-and-forward protocol. Moreover, we assume that the direct link between the source node and destination node is in deep fade, so, it is weak enough to be ignored. Furthermore, the links from the source nodes to the relay are RF links whereas the link between the relay and the destination is an FSO link. Therefore, the RF links and the FSO link are assumed to experience Rayleigh and Gamma–Gamma (𝛤 𝛤 ) fading, respectively. Moreover, we assume that the source nodes and the destination node are equipped with 𝑁𝑠 antennas and 𝑁𝑑 aperture, respectively. Besides, we assume that, the relay node is equipped with 𝑁𝑠,𝑟 receive antenna for the reception of RF signal and 𝑁𝑟,𝑑 transmit aperture for the transmission of optical signal. So, at the first hop, the symbol vector 𝑠 ∈ C𝑁𝑠 is transmitted from the source to the relay node and signal 𝑦𝑠,𝑟 ∈ C𝑁𝑟 received at the relay can be written as [45] √ 𝑁 𝑃𝑠 ∑𝑠 𝑦𝑠,𝑟 = ℎ 𝑠 + 𝑛𝑟 , (1) 𝑁𝑠 𝑘=1 𝑠,𝑟 where 𝑃𝑠 denotes the source transmit power, ℎ𝑠,𝑟 ∈ C𝑁𝑠,𝑟 ×𝑁𝑠 denotes the source–relay channel which is assumed to be Rayleigh fading channel, and 𝑛𝑟 ∈ C𝑁𝑟 ∼ (0, 𝑁01 ) is the additive white Gaussian noise (AWGN) vector at the relay node whose components are independent and identically distributed (i.i.d.) with zero mean and variance 𝜎𝑟2 . Furthermore, at the second hop, the RF–optical signal conversion for retransmission to the destination through the FSO links is assumed to be by subcarrier intensity modulation (SIM) scheme. The optical signal at the relay node can be defined as [46] (2)
𝑠𝑟 = 𝐺(1 + 𝜂𝑦𝑠,𝑟 ), where the relay gain, 𝐺 =
√
𝑃𝑟 𝑃𝑠 𝑁𝑠
‖ℎ𝑠,𝑟 ‖2 +𝑁01
, 𝑃𝑟 represents the output
optical power at the relay, 𝜂 denotes the electrical-to-optical conversion coefficient. We assume that the FSO link follows the 𝛤 𝛤 -distributed fading in order to consider a wide range of turbulence conditions [36]. This is due to the fact that, the 𝛤 𝛤 -distribution can be used to characterize the fading gains for weak to strong turbulence scenarios. So, we assume that, the fading gain 𝐼𝑚 (irradiance) is a random variable (RV) between the relay aperture and the receive aperture of the FSO link which follows the 𝛤 𝛤 -distribution. The probability density function (PDF) of 𝐼𝑚 based on the doubly stochastic theory of scintillation in which the smallscale irradiance fluctuations are assumed to be modulated by largescale irradiance fluctuations of the propagating wave can be defined as [36,47,48] ( √ ) (𝛼+𝛽) 2(𝛼𝛽)(𝛼+𝛽)∕2 (ℎa ) 2 −1 𝐾𝛼−𝛽 2 𝛼𝛽ℎa , (3) 𝑓𝐼𝑚 (𝐼𝑚 ) = 𝛤 (𝛼)𝛤 (𝛽) where 𝛤 (⋅) is the gamma function, 𝐾𝑣 (⋅) is the modified Bessel function of the second kind of order 𝑣, 𝛼 and 𝛽 are the effective number of large-scale and small-scale eddies of the scattering process, respectively. The variables 𝛼 and 𝛽 are related to the atmospheric conditions and for spherical wave propagation, they can be expressed respectively as [36,47–49] [ ( ) ]−1 2 0.49𝜎𝑅 𝛼 = exp −1 , (4a) 12∕5 (1 + 0.18𝑑 2 + 0.56𝜎𝑅 )7∕6 [ ( ) ]−1 2 (1 + 0.69𝜎 12∕5 )−5∕6 0.51𝜎𝑅 𝑅 𝛽 = exp −1 , (4b) 12∕5 (1 + 0.9𝑑 2 + 0.62𝑑 2 𝜎𝑅 )5∕6 2 ≜ 0.492 𝐶 2 𝑘7∕6 𝐿11∕6 is the Rytov variance, 𝑑 ≜ (𝑘𝐷2 ∕4𝐿)1∕2 , where 𝜎𝑅 𝑛 𝐷 denotes the aperture diameter of the receiver, 𝑘 ≜ 2𝜋∕𝜆 is the optical
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Optics Communications 402 (2017) 653–661
Fig. 2. Mixed RF/FSO dual-hop communication system.
wave number, 𝜆 denotes the wavelength, 𝐿 is the distance, and 𝐶𝑛2 is the altitude-dependent refractive index structure parameter expressed by the Hufnagel–Valley model as 𝐶𝑛2 (ℎ) = 0.00594(𝑣𝑤 ∕27)2 (10−5 ℎ)10 exp(−ℎ∕1000) + 2.7 × 10−16 exp(−ℎ∕1500) + 𝐴̂ exp(−ℎ∕100),
Therefore, we express the received signal after removing the direct current (DC) as [ (√ )] 𝑦𝑚 = 𝜉𝐺 𝜂 𝑃𝑠 (ℎ𝑠,𝑟 𝑠 + 𝑛𝑟 ) 𝐼𝑚 + 𝑛𝑚 . (10) Furthermore, in this work, we assume opportunistic schedule transmission. Basically, opportunistic scheduling scheme can be employed to select user with the best SNR to transmit at a specified period of time. So, based on this transmission, the e2e instantaneous SNR 𝛾𝑒2𝑒 for computing the outage probability is given by [20,37]
(5)
where 𝑣𝑤 is the root mean square wind velocity in m/s, ℎ is the altitude in m and 𝐴̂ is the nominal value of 𝐶𝑛2 (0) at the ground level in m−2∕3 . 2 , which is normally used for describing The scintillation index, 𝜎𝑁 the strength of atmospheric fading can be expressed as [36,50] 2 𝜎𝑁
E(𝐼𝑚2 ) ≜ [ ]2 − 1, E(𝐼𝑚 )
𝑃out = 𝐹𝛾𝑒2𝑒 (𝛾𝑡ℎ ) ≥ 𝐹𝛾Sel,𝑟 (𝛾𝑡ℎ ) + 𝐹𝛾𝑟,𝑑 (𝛾𝑡ℎ ) − 𝐹𝛾Sel,𝑟 (𝛾𝑡ℎ )𝐹𝛾𝑟,𝑑 (𝛾𝑡ℎ ) (11)
≃ 𝐹𝛾Sel,𝑟 (𝛾𝑡ℎ ) + 𝐹𝛾𝑟,𝑑 (𝛾𝑡ℎ )
(6)
where 𝛾Sel,r = 𝑃Sel |ℎSel,𝑟 |2 ∕(𝑁01 ) denotes the SNR of the selected user through opportunistic scheduling for transmission over the RF hop, 𝛾𝑟,𝑑 = 𝜂𝜉𝑃𝑟 |𝐼𝑚 |2 ∕(𝑁02 ) represents the SNR of the FSO hop, 𝐹𝛾Sel,𝑟 (𝛾) and 𝐹𝛾𝑟,𝑑 (𝛾) are the cumulative distribution functions (CDFs) which can be expressed respectively as [16,20,55] ) ( 𝑘 𝐾 𝐾 ∑ ∑ (−1)𝑘 ∑ 𝐹𝛾Sel,𝑟 (𝛾𝑡ℎ ) = (12a) 𝜆𝑛𝑡 ,𝑟 𝛾𝑡ℎ , exp − 𝑘! 𝑛 ,…,𝑛 𝑘=0 𝑡=1 1 𝑘 ] [ | 1, 𝜒1 𝑟(𝛼+𝛽−2) 𝜁 2 (𝛼𝛽)𝑟 3𝑟,1 𝐹𝛾𝑟,𝑑 (𝛾𝑡ℎ ) = , (12b) G𝑟+1,3𝑟+1 𝛾𝑡ℎ || (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑟(2𝑟) 𝛾̄𝑟,𝑑 | 𝜒2 , 0
where E(⋅) denotes an expectation operator. 2 can be expressed in terms of eddies of the scattering Furthermore, 𝜎𝑁 process (𝛼 and 𝛽) as [36,50–52] [ 2 0.49𝜎𝑅 2 𝜎𝑁 = exp 12∕5 (1 + 0.18𝑑 2 + 0.56𝜎𝑅 )7∕6 ] 12∕6 2 (1 + 0.69𝜎 0.51𝜎𝑅 )−5∕6 𝑅 + − 1, (7a) 12∕5 1 + 0.9𝑑 2 + 0.62𝑑 2 𝜎𝑅 1 1 1 = + + . (7b) 𝛼 𝛽 𝛼𝛽
where G(⋅) is the Meijer G-function, 𝛾̄𝑟,𝑑 = 𝜂𝜉𝑃𝑟 𝜇𝑟,𝑑 ∕(𝑁02 ), 𝜇 denotes
It is noteworthy that, weak irradiance fluctuations are usually characterized by the values of the scintillation index that are less than 2 < 1). However, in situations where the scintillation index is unity (𝜎𝑅 not less than unity, the atmospheric turbulence that the optical wave experiences during propagation can be classified as moderate to strong irradiance fluctuations. The received optical signal at the destination aperture can be expressed as [37,46,53,54] [ (√ )] 𝑁 𝑃𝑠 ∑𝑠 𝑦𝑚 = 𝜉𝐼𝑚 𝐺 1 + 𝜂 ℎ𝑠,𝑟 𝑠 + 𝑛𝑟 + 𝑛𝑚 , 𝑚 = 1, … , 𝑁𝑑 (8) 𝑁𝑠 𝑘=1
2
2
2
2
the mean power of |𝐼𝑚 |2 , 𝜒1 = 𝜁 𝑟+1 , … , 𝜁 𝑟+𝑟 , 𝜒2 = 𝜁𝑟 , … , 𝜁 +𝑟−1 , 𝑟 𝛽+𝑟−1 𝛼+𝑟−1 𝛽 𝛼 , … , , , … , , where 𝜁 is the ratio between the equivalent 𝑟 𝑟 𝑟 𝑟 beam radius and the pointing error displacement standard deviation at the receiver, parameter 𝑟 denotes the type of detection technique employed (i.e., 𝑟 = 1 and 𝑟 = 2 represent heterodyne detection and intensity modulation/direct detection (IM/DD)), respectively. The asymptotic outage performance at high SNRs can be defined as [20] ⎧[ 𝛾̄𝑢,𝑟 ]−𝐾 weak turbulence ⎪ ⎪ 𝛾out ∞ ]− 𝜈 𝑃out = ⎨[ − 2 (13) 2 ⎪ 𝛬 𝜈 𝛾̄ strong turbulence 𝑟,𝑑 ⎪ 𝛾out ⎩
where 𝜉 is the optical-to-electrical conversion coefficient and 𝑛𝑚 ∼ (0, 𝑁02 ) is an AWGN term over the FSO link, with zero mean, and variance 𝜎𝑚2 .
where 𝛾̄𝑢,𝗋 is the average SNR for identical channels case, 𝜈 { } min 𝜁 2 , 𝛼, 𝛽 , and 𝛬 can be expressed as [16,20]
4. Performance analysis In this section, we adopt outage probability, ASEP, and ergodic channel capacity as the performance metrics in the analysis. Outage probability is a key metric for describing performance in quasi-static fading channels and can be defined as the probability that the instantaneous SNR falls below a specific SNR threshold 𝛾𝑡ℎ . We assume that, 𝑁𝑑 = 𝑁𝑠,𝑟 = 𝑁𝑟,𝑑 = 1. So, Eq. (8) can be defined as [ (√ )] 𝑃𝑠 𝑦𝑚 = 𝜉𝐺 1 + 𝜂 𝑁 (ℎ 𝑠 + 𝑛𝑟 ) 𝐼𝑚 + 𝑛𝑚 . (9) 𝑁𝑠 𝑠 𝑠,𝑟
𝛬=
∏6 ( )𝑏 ∕2 6 ∑ 𝑟(𝛼+𝛽−2) 𝜁 2 (𝛼𝛽)𝑟 𝑘 𝑗=1,𝑗≠𝑘 𝛤 (𝑏𝑗 − 𝑏𝑘 )𝛤 (𝑏𝑘 ) , ∏ (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑘=1 3 𝛤 (𝑎𝑗 − 𝑏𝑘 )𝛤 (1 + 𝑏𝑘 ) 𝑟(2𝑟)
=
(14)
𝑗=2
where 𝑎𝑗 = 𝜒1 (𝑗) for 𝑗 = 1, … , 3, 𝑏𝑗 = 𝜒2 (𝑗) for 𝑗 = 1, … , 6, and 𝑏𝑘 = 𝜈. The ASEP, 𝑃𝑒 , can be expressed in terms of the CDF of the received SNR, 𝛾𝑒2𝑒 , denoted by 𝐹𝛾𝑒2𝑒 as [56,57] ∞
𝑃𝑒 = 𝜚 657
∫0
𝑒−𝑏𝛾 √ 𝐹𝛾𝑒2𝑒 (𝛾) 𝑑𝛾, 𝛾
(15)
I.A. Alimi et al.
where 𝜚 =
Optics Communications 402 (2017) 653–661 √ 𝑎 𝑏 √ , 2 𝜋
parameters 𝑎 and 𝑏 are modulation-specific constant
terms such that for binary phase-shift keying, 𝑎 = 𝑏 = 1, for 𝑀-ary phase-shift keying 𝑎 = 2, 𝑏 = sin2 (𝜋∕𝑀), and for 𝑀-ary quadrature amplitude modulation, 𝑎 = 4(𝑀 1∕2 − 1)∕𝑀 1∕2 , 𝑏 = 3∕(2(𝑀 − 1)) [57]. So, 𝐹𝛾𝑒2𝑒 (𝛾) can be expressed as [20] ( 𝑘 ) 𝐾 𝐾 ∑ ∑ (−1)𝑘 ∑ 𝐹𝛾𝑒2𝑒 (𝛾) = exp − 𝜆𝑛𝑡 ,𝑟 𝛾𝑡ℎ 𝑘! 𝑛 ,…,𝑛 𝑘=0 𝑡=1 1 𝑘 { ( ) 𝑟(𝛼+𝛽−2) 𝜁 2 (𝛼𝛽)𝑟 1− G3𝑟,1 × 𝑟(2𝑟) 𝛾̄𝑟,𝑑 (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑟+1,3𝑟+1 [ ]} (𝛼𝛽)𝑟 || 1, 𝜒1 𝑟(𝛼+𝛽−2) 𝜁 2 × 𝛾 G3𝑟,1 + 𝑟(2𝑟) 𝛾̄𝑟,𝑑 || 𝜒2 , 0 (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑟+1,3𝑟+1 [ ] (𝛼𝛽)𝑟 || 1, 𝜒1 𝛾 × , (16) 𝑟(2𝑟) 𝛾̄𝑟,𝑑 || 𝜒2 , 0
Fig. 3. Outage performance of a dual-hop multiuser mixed RF/FSO wireless communication system considering the impact of pointing errors at the FSO links.
Then, 𝑃𝑒 can be expressed as ⎡𝐾 ⎢∑ (−1)𝑘 𝑃𝑒 = 𝜚 ⎢ ⎢𝑘=0 𝑘! ⎣
(
𝐾 ∑
𝑏+
𝑛1 ,…,𝑛𝑘
𝑘 ∑
)− 21 𝜆𝑛𝑡 ,𝑟
RF/FSO wireless communication system. We study the effect of varying the ratio between the equivalent beam radius and the pointing error displacement standard deviation in order to analyze the resultant effects of pointing errors and atmospheric turbulence on the system outage performance. Initially, we consider strong turbulence conditions with channel parameters 𝛼 = 0.8 and 𝛽 = 1.5. Furthermore, for a 𝛾𝑡ℎ = 5 dB and two users, we employ different values of 𝜁 (i.e. 𝜁 = 0.5, 0.7, 0.9, 1.5, 2.1). Fig. 3 shows the plot of outage probability for different values of average SNR/hop and 𝜁. We observed that, as 𝜁 increases, the detrimental effect of pointing error on the system performance decreases, so, the outage performance improves significantly. The observation is due to the fact that, as the value of 𝜁 increases, the effect of pointing error decreases and the probability of outage decreases. On the other hand, when the value of 𝜁 decreases, the effect of pointing error increases and consequently, the probability of outage increases. Moreover, the results show that as 𝜁 → ∞, we are approaching a non-pointing error scenario. Furthermore, it is noteworthy that, the system diversity order is determine by the min{𝜁 2 , 𝛼, 𝛽}∕2. So, when the value of 𝜁 2 is less than that of 𝛼 and 𝛽, it will affect diversity order of the system accordingly. However, when the value of 𝜁 2 is larger than that of 𝛼 and 𝛽, it will affect just the coding gain of the system. In essence, an increase in 𝜁 means that additional number of distributed RRHs and the associated RF users can communicate effectively via the backbone network to the BBU pool within an allowable latency. Furthermore, in Fig. 4, we present results of varying the atmospheric turbulence parameters 𝛼 ∈ (0.5, 0.6, 2.3, 4) and 𝛽 ∈ (0.5, 0.5, 0.9, 1.3) on the system outage performance. In the analysis, we assume a 𝛾𝑡ℎ = 5 dB and 𝜁 = 2.1. We observe that, a decrease in the value of 𝛼 or 𝛽 leads to an increase in the detrimental effect of atmospheric turbulence. Consequently, the system performance is degraded. Also, we observe that the outage probability increases with an increase in the atmospheric 2 = 8 turbulence strength and the performance of the system with 𝜎𝑁 depicts the worst case scenario. For instance, to achieve an outage 2 = 1.21, about 20 dB is required. This probability of 10−1 , with 𝜎𝑁 subsequently increased to about 38 dB for the same outage probability 2 = 7. The result demonstrates that system with low turbulence when 𝜎𝑁 is relatively power efficient and has best performance. So, in essence, 2 a decrease in the value of 𝛼 or 𝛽 or an increase in the value of 𝜎𝑁 reduces the number of RRHs as well as the associated RF users that can be supported efficiently by the mobile fronthaul within an allowable latency. Moreover, in Fig. 5, we show results of varying the channel parameters 𝛼 ∈ (0.8, 1.8, 2.6) and 𝛽 (1.8, 2.6, 4) on the error probability performance of a dual-hop multiuser mixed RF/FSO wireless communication system. Also, we use 𝜁 = 2.1 and assume that the modulationspecific constant terms are equal. So, parameters 𝑎 and 𝑏 are equal to one
𝑡=1
⎧ ⎪ 𝑟(𝛼+𝛽−2) 𝜁 2 × ⎨𝛤 (1∕2) − G3𝑟,2 (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑟+2,3𝑟+1 ⎪ ⎩ ⎡ ⎤⎫ | 1 , 1, 𝜒1 ⎥⎪ ⎢ (𝛼𝛽)𝑟 |2 × ⎢( ) ⎥⎬ | ∑ ⎢ 𝑏 + 𝑘𝑡=1 𝜆𝑛𝑡 ,𝑟 𝑟(2𝑟) 𝛾̄𝑟,𝑑 | 𝜒2 , 0 ⎥⎪ ⎣ ⎦⎭ ]⎤ [ 1 1 𝑟(𝛼+𝛽−2) 𝜁 2 (𝛼𝛽)𝑟 || 2 , 1, 𝜒1 ⎥ + 𝑏− 2 G3𝑟,2 ⎥. 𝑟+2,3𝑟+1 𝑟(2𝑟) 𝛾̄ 𝑏 | (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) 𝑟,𝑑 | 𝜒2 , 0 ⎥ ⎦
(17)
Furthermore, the ergodic capacity 𝐶erg is another important performance metric that quantifies the maximum achievable transmission rate under which errors are recoverable. The 𝐶erg can be mathematically formulated in terms of the PDF of 𝛾𝑒2𝑒 as [20,32,36,58] 𝐶erg ≜ E⟨𝐶⟩ =
1 ln(2) ∫0
∞
(18)
ln(1 + 𝛾)𝑓𝛾𝑒2𝑒 (𝛾)𝑑𝛾.
where E is the expectation operator. So, 𝐶erg can be expressed as ) {𝐾 ) ( 𝑘 [ ( 𝑘 𝐾 ∑ (−1)𝑘 ∑ ∑ ∑ 1 𝐶erg = 𝜆𝑛𝑡 ,𝑟 𝜆𝑛𝑡 ,𝑟 E𝑖 − exp ln(2) 𝑘=0 𝑘! 𝑛 ,…,𝑛 𝑡=1 𝑡=1 1 𝑘 ( 𝑟(𝛼+𝛽−2) 𝜁 2 − G0,1∶1,2∶3𝑟,0 1,0∶2,2∶𝑟,3𝑟 (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) [ ] ∑ (𝛼𝛽)𝑟 𝑘𝑡=1 𝜆𝑛𝑡 ,𝑟 | 1 | 1, 1 | 𝜒 1 | 1 | | × ∑𝑘 , | − | 1, 0 | 𝜒 𝑟(2𝑟) 𝛾̄𝗋,𝖽 | | | 2 𝑡=1 𝜆𝑛𝑡 ,𝑟 ( 𝑘 )2 ∑ − 𝜆𝑛𝑡 ,𝑟 G0,1∶1,2∶3𝑟,1 1,0∶2,2∶𝑟+1,3𝑟+1 [ ×
𝑡=1
∑𝑘
(𝛼𝛽)𝑟
1
𝑡=1
, 𝜆𝑛𝑡 ,𝑟
𝑟(𝛼+𝛽−2) 𝜁 2
∑𝑘
𝑡=1 𝜆𝑛𝑡 ,𝑟 || 2 || 1, 1 || 1, 𝜒1 | − | 1, 0 | 𝜒 , 0 (2𝑟) 𝑟 𝛾̄𝗋,𝖽 | | | 2
[
G3𝑟+2,1 𝑟+2,3𝑟+2
])]
(𝛼𝛽)𝑟 || 0, 1, 𝜒1 𝑟(2𝑟) 𝛾̄𝗋,𝖽 || 𝜒2 , 0
]}
, (19) (2𝜋)𝑟−1 𝛤 (𝛼)𝛤 (𝛽) [ ] ||| where G 𝑍1 , 𝑍2 |.|.|. represents the extended generalized bivariate ||| Meijer G-function. +
5. Results and discussion In this section, we present results of our studies on the e2e outage probability, ASEP, and ergodic channel capacity of the considered mixed 658
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Optics Communications 402 (2017) 653–661
Fig. 4. Outage performance of a dual-hop multiuser mixed RF/FSO wireless communication system considering different atmospheric conditions.
Fig. 6. Ergodic channel capacity of a dual-hop multiuser mixed RF/FSO wireless communication system considering different atmospheric conditions.
we have assumed Rayleigh fading for the RF links and 𝛤 𝛤 -distributed fading channel for the FSO link. Furthermore, we have studied the effect of atmospheric turbulence as well as pointing errors between the relay and the destination on the system performance. Consequently, we have shown that, a decrease in the value of the effective number of large-scale or small-scale eddies of the scattering process reduces the total number of the RRHs and the associated RF users that can be supported efficiently by the mobile fronthaul within an allowable latency. Moreover, we have demonstrated that, an increase in the ratio between the equivalent beam radius and the pointing error displacement standard deviation at the receiver means that a considerable number of distributed RRHs can communicate efficiently via the backbone network to the BBU pool within a specified latency. The results presented can be beneficial in the system evaluation, optimization, and prediction of mixed RF/FSO dual-hop relay performance in real-life scenarios deployment.
Fig. 5. ASEP of a dual-hop multiuser mixed RF/FSO wireless communication system considering different atmospheric conditions.
Acknowledgments (i.e. 𝑎 = 𝑏 = 1). This condition corresponds to a case of binary phase-shift keying modulation format. We observe that, an increase in the value of 𝛼 or 𝛽 leads to a decrease in the detrimental effect of atmospheric turbulence. Therefore, the system performance is enhanced. Also, we 2 . For instance, observe that the ASEP increases with an increase in the 𝜎𝑁 2 = 0.73, about 22 dB is required. to achieve an ASEP of 10−4 , with 𝜎𝑁 The value consequently increased to about 35 dB for the same ASEP 2 = 2.5. The result shows that an increase in the 𝜎 2 deteriorates when 𝜎𝑁 𝑁 the system performance. Moreover, we study the effect of varying the channel parameters 𝛼 (i.e. 𝛼 = 15.7, 9.7, 5.7) and 𝛽 (i.e. 𝛽 = 6, 3, 3) on the ergodic channel capacity of a dual-hop multiuser mixed RF/FSO wireless communication system for 𝜁 = 2.1. The plot of ergodic channel capacity is shown in Fig. 6. We observe that, an increase in the value of channel parameters leads to a decrease in the degradation caused by the atmospheric turbulence. Consequently, the system performance is enhanced and more RRHs can be supported efficiently. For instance, we observe that, to achieve a system capacity of 6 𝑏∕𝑠∕𝐻𝑧 with 𝛼 = 5.7 and 𝛽 = 3, about 35 𝑑𝐵 SNR/Hop is required whereas, for 𝛼 = 15.7 and 𝛽 = 6, of the same capacity, about 25 𝑑𝐵 SNR/Hop is required.
This work is supported by the Fundação para a Ciência e a Tecnologia (FCT) under the Ph.D. grant PD/BD/52590/2014. It is also supported by the FCT/MEC through the national funds and when applicable cofunded by FEDER–PT2020 partnership agreement under the project UID/EEA/50008/2013. Also, it is funded by the European Structural Investment Funds (ESIF), through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) under FutPON project [Nr. 003145 (POCI-01-0247-FEDER-003145)]. References [1] M. Ayyash, H. Elgala, A. Khreishah, V. Jungnickel, T. Little, S. Shao, M. Rahaim, D. Schulz, J. Hilt, R. Freund, Coexistence of WiFi and LiFi toward 5G: concepts, opportunities, and challenges, IEEE Commun. Mag. 54 (2) (2016) 64–71. http: //dx.doi.org/10.1109/MCOM.2016.7402263. [2] J. Wu, Z. Zhang, Y. Hong, Y. Wen, Cloud radio access network (C-RAN): a primer, IEEE Netw. 29 (1) (2015) 35–41. http://dx.doi.org/10.1109/MNET.2015.7018201. [3] H. Dahrouj, A. Douik, O. Dhifallah, T.Y. Al-Naffouri, M.S. Alouini, Resource allocation in heterogeneous cloud radio access networks: advances and challenges, IEEE Wirel. Commun. 22 (3) (2015) 66–73. http://dx.doi.org/10.1109/MWC.2015. 7143328. [4] U. Siddique, H. Tabassum, E. Hossain, D.I. Kim, Wireless backhauling of 5G small cells: challenges and solution approaches, IEEE Wirel. Commun. 22 (5) (2015) 22– 31. http://dx.doi.org/10.1109/MWC.2015.7306534. [5] M. Peng, K. Zhang, Recent advances in Fog radio access networks: Performance analysis and radio resource allocation, IEEE Access 4 (2016) 5003–5009. http: //dx.doi.org/10.1109/ACCESS.2016.2603996. [6] M. Peng, S. Yan, K. Zhang, C. Wang, Fog-computing-based radio access networks: issues and challenges, IEEE Netw. 30 (4) (2016) 46–53. http://dx.doi.org/10.1109/ MNET.2016.7513863.
6. Conclusion In this paper, we have presented a mixed RF/FSO dual-hop relay system as a viable backhaul/fronthaul solution for the 5G cellular networks and beyond in which CC-RANs is expected to be implemented. Furthermore, we have studied different performance metrics such as e2e outage probability, ASEP, and ergodic channel capacity. In the analysis, 659
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