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Throughput and Outage Probability of Wireless Energy Harvesting Based Cognitive DF Relaying Network
Available online at www.sciencedirect.com
ScienceDirect Materials Today: Proceedings 4 (2017) 10304–10308
www.materialstoday.com/proceedings
ICEMS 2016
Throughput and Outage Probability of Wireless Energy Harvesting Based Cognitive DF Relaying Network Binod Prasad, Adduru U G Sankararao, Sumit Kundu, Sanjay Dhar Roy National Institute of Technology, Durgapur, 713209, West Bengal, India
Abstract This paper evaluates the throughput performance and outage probability of a secondary user (SU) in a decode-and-forward (DF) relaying network based on wireless energy harvesting under cognitive radio constraint. The energy constrained relay node first harvests energy through radio-frequency (RF) signals from the source node. Next, the relay node uses the harvested energy to forward the decoded source information to the destination node. The power transmitted by source and relay node is constrained by the tolerable interference threshold of the primary unit receiver. The source node transfers energy and information to the relay node through power splitting-based relaying (PSR). In PSR, the relay splits the received power for energy harvesting and information processing. The interference caused by a primary unit transmitter at the SU relay and destination nodes is also considered. Considering wireless energy harvesting constraint at the relay node, we analyse the achievable throughput and outage performance of a cognitive DF relaying network. We study the impact of different system parameters such as power splitting ratio, primary transmitter power and tolerable interference threshold of PU receiver on the throughput and outage performance of SU. © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Recent Trends in Engineering and Material Sciences (ICEMS-2016). Keywords: Energy harvesting relay; power splitting relaying; cognitive relaying network; spectrum sharing.
1. Introduction In cognitive radio the secondary user (SU) is allowed to concurrently occupy the radio frequency spectrum which is licensed to the primary user (PU). Cognitive radio with spectrum sharing is an emerging technology to overcome the inefficient utilization of scarce radio frequency spectrum [1]-[4]. In spectrum sharing environment cognitive user is allowed to access the licensed spectrum as long as its transmission does not interfere with the primary user’s communication. In wireless energy harvesting, RF energy is captured by the receiver antennas and converted into a 2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Recent Trends in Engineering and Material Sciences (ICEMS2016).
Binod Prasad/ Materials Today: Proceedings 4 (2017) 10304–10308
10305
DC voltage through appropriate circuits [5]. The application of wireless energy harvesting to cognitive radio based systems has been considered in [8]. An important application of wireless energy harvesting is in cooperative relaying networks, where an intermediate relay node assists in the information transmission from the source to the destination. The use of relay between source and destination reduces the overall path loss. Relay transmission improves throughput and extends coverage. If the relay node has limited battery reserves, then it relies on some external charging mechanism in order to remain active in the network [6]. Hence energy harvesting is important in such networks as it can enable information relaying. The outage performance of a typical cooperative communication system has been studied in [9]. However, the authors in [9] assumed that the relay has its own internal energy source and does not require external charging. The power allocation strategies and outage performance for a decode-and-forward (DF) relaying network under energy harvesting constraints is studied in [10]. A DF relaying network is considered in [7], the system throughput and ergodic capacity are analysed in the presence of energy harvesting constraints. The relay is assumed as energy constrained node and thus harvests energy from the RF signal broadcasted by the source node. In this paper, a DF relaying network is considered, throughput and outage probability are analysed in the presence of both energy harvesting and cognitive radio constraint. Power splitting (PS) receiver architecture proposed in [5] is adopted for separate information processing and energy harvesting at the relay node. In power splitting-based relaying (PSR), the relay splits the received power for energy harvesting and information processing. The power transmitted by the source and relay is limited by the tolerable interference threshold at the primary receiver (PU-Rx). The interference caused by a primary transmitter (PU-Tx) at relay and destination nodes is also considered while analysing the performance of SU. The main contributions of this work are summarized below: • The achievable throughput and outage probability are analysed for a DF relaying network in the presence of both wireless energy harvesting and cognitive radio constraints for PSR scheme. • Throughput and outage probabilities are compared for different system parameters, such as power splitting ratio, primary transmitter power and tolerable interference threshold at primary receiver. The rest of the paper is organized as follows. Section II describes the overall system model and assumptions. Section III presents the analysis of the throughput and outage probability of the network for PSR scheme. Section IV discusses the numerical results; finally Section V concludes the paper. 2. System Model The system model consists of a SU source (S), a SU relay (R), a SU destination (D), a PU receiver (PU-Rx) and a PU transmitter (PU-Tx) as shown in Fig. 1 (a). We assume that there is no direct link between S and D and hence SU transmission is assisted by energy constrained relay node working in DF mode. First the DF relay harvests energy from the source signal. Then, it uses harvested energy as a source of transmit power to forward decoded information to the destination while maintaining the interference produced at PU receiver below interference threshold Ith . The primary transmitter with transmitting power Pp causes interference at both relay and destinations. For the joint task of energy harvesting and information processing power splitting based relaying (PSR) architecture [5] is considered at relay node. During first half of the total block time T, the relay is assumed to harvest energy from the fraction of received power ρP and the remaining power, (1-ρ) P is used for the source to relay information transmission. During the second half of the block time the information is transmitted from relay to destination. The S to R, R to D, PU-Tx to R, PU-Tx to D, S to PU-Rx and R to PU-Rx channel gains are denoted by hsr , hrd , h pr , h pd , hsp and hrp respectively, are modelled as block-fading and frequency non selective parameters. The links are assumed to be Rayleigh fading [5] channels with zero mean and are constant over the block time T and independent 2 and identically distributed from one block to the next block. Therefore, the channel power gain, g xy = hxy is exponentially distributed, where x=y={s, r, d, p}. The maximum transmit powers of SU transmitter and relay due to underlay constraint are given by I Psmax = th g sp
and
I Prmax = th g rp
(1)
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Binod Prasad/ Materials Today: Proceedings 4 (2017) 10304–10308
The transmit power of the relay is derived from harvesting energy only. However, the relay transmit power is limited by the interference threshold at the primary receiver. If the harvested power Prharvest is less than the maximum allowed transmit power Prmax then the relay transmits with the power Prharvest , otherwise it transmits with power Prmax .
(b) Fig. 1. (a) System model for DF Relaying network; (b) Transmission block structure in the PSR scheme for energy harvesting and information processing.
3. Throughput and Outage Probability Under Power Splitting-Based Relaying (PSR) In this section the throughput and outage probability of the system are analysed by considering PSR scheme of energy harvesting as in [5]. The transmission block structure in PSR scheme for energy harvesting and information processing at the relay node is shown in Fig. 1 (b), where P is the received signal power and T is the block time. Half of the time, T/2 is used for the source to relay information transmission and remaining half, T/2 is used for the relay to destination information transmission. During the first half, the fraction of the received power, ρP is used for energy harvesting and remaining received power, (1-ρ)P is used for transmitting source information to relay node, where ρ ϵ (0,1). The choice of power fraction, ρ controls the achievable throughput and outage probability at destination. However the SU source and relay transmit powers are controlled in order to protect PU from the interference caused by their transmission. The SU source transmitting power is given by I (2) Ps = th g sp
The harvested energy at relay node during the time T/2 is given by Erharvest =
ηρ Ps g sr d1m
(3)
(T 2)
where 0 < η < 1 is the energy conversion efficiency which depends on the energy harvesting circuitry and d is the distance between source and relay and ‘m’ is the path loss exponent. The harvested power at the relay node is given by ηρ Ps g sr E harvest = Prhavest = r T 2 d1m
(4)
The relay transmitting power is the minimum of harvested power and maximum allowed transmit power as explained in Section II and is given by I (5) Pr = min Prharvest , th
g rp
The SNR at the relay node is given by γ sr =
Ps g sr (1 − ρ )
(
d1m σ n2 + Pp g pr
)
(6)
where σ n2 is the noise variance of overall AWGN at relay node, Pp is the PU-Tx transmitting power and Ps is the source power as given by (2). The SNR at the destination node is given by Pr g rd (7) γ rd = d 2m (σ n2 + Pp g pd )
where d2 is the distance between relay and destination, Pr is the relay transmit power as given by (5) and σ n2 is the variance of overall AWGN at destination.
Binod Prasad/ Materials Today: Proceedings 4 (2017) 10304–10308
10307
Throughput Analysis: In order to find out the throughput of the SU, the ergodic capacities of source to relay link and relay to destination links are to be evaluated as Csr = E{log 2 (1 + γ sr )} , Crd = E{log 2 (1 + γ rd )} (8) where γ sr depends on hsr and γ rd depends on both hsr and hrd . The ergodic capacity of the overall system is given by minimum of source to relay link and relay to destination link ergodic capacities C erg = min{Csr , Crd } (9) In PSR scheme, T/2 is the effective communication time from source node to the destination node in the block of time T seconds and if the source transmits at a fixed rate equal to the ergodic capacity, i.e., C erg bits/sec, the throughput of the system is given by T / 2 erg 1 (10) C τ= = min {Csr , Crd } T
2
where the throughput, τ depends on Ps , ƞ, ρ, d1, d2 , σ n2 . Outage Probability: The Outage probability is the probability that the ergodic capacity is less than some predefined rate, R bits/sec. The outage probability of the system is given by
(
Poutage = Pr C erg < R
(
)
= Pr min {Csr , Crd } < R
(11)
)
where the outage probability, Poutage depends on Ps, ƞ, ρ, d1, d2 , σ n2 . 4. Results and Discussions This section numerically analyses the throughput performance and outage probability of a DF relaying network in presence of energy harvesting with cognitive radio constraint. Unless otherwise stated, we set the energy harvesting efficiency, η =1, path loss exponent m=2.7. The distances d1, d2, are normalized to unit value. For simplicity, similar noise variances at the relay and the destination nodes are assumed. Fig. 2 plots the throughput, τ of SU with respect to ρ, for PSR scheme for different values of primary transmitting powers Pp. It is observed that the throughput of SU degrades as the primary transmit power increases. As the transmit power of SU increases, the interference at the relay node and SU receiver increases and hence reduce their respective SNR and there by decreases the throughput of SU. The impact of allowable interference value Ith on throughput τ of SU is shown in Fig. 3. An improvement in the SU throughput performance is observed as Ith increases. An increase in Ith means PU can tolerate more interference and hence allows SU and relay to increase their transmit power and thereby increases the throughput of SU. The impact of PU transmit power on the outage probability of SU is shown in Fig. 4 while keeping other parameters fixed. It is observed that the outage probability of SU degrades as the primary transmit power, Pp increases. As the transmit power of PU increases, the interference at the SU relay and destination increases and thereby increases the outage probability of SU. Further the effect of Ith on the outage probability of SU is also shown in Fig. 4. The outage probability of SU improves as the allowable interference increases.
Fig. 2: Throughput at SU destination against ρ for different values of interference threshold,
I th at PU-Rx.
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Binod Prasad/ Materials Today: Proceedings 4 (2017) 10304–10308
Fig. 3: Throughput at SU destination against ρ for different values of PU transmitting powers, Pp.
Fig. 4: Outage probability at SU destination against ρ for different values of Pp and Ith.
5. Conclusion In this paper, the performance of a cognitive DF relaying network under EH constraint at relay is evaluated in terms of throughput and outage probability of SU. We have also incorporated the effect of PU interference while evaluating the performance of SU. The performance of SU degrades as the interference from PU on SU increases. The impact of power splitting ratio (ρ) on SU performance is also studied. Further the impact of tolerable interference threshold on the throughput and outage probability of SU is also investigated. The performance of SU improves as allowable interference threshold at PU increases. References [1] A. Ghasemi and E. S. Sousa, “Fundamental limits of spectrum-sharing in fading environments,” IEEE Trans. Wireless Commun., vol. 6, no. 2, pp. 649– 658, Feb. 2007. [2] Trung Q. Duong, Tran Trung Duy, Michail Matthaiou , Theodoros Tsiftsis , and George K. Karagiannidis, “ Cognitive Cooperative networks in Dual Hop Asymmetric Fading Channels” , in IEEE GLOBECOM, 2013.. [3] H. Ding, J. Ge, D. B. da Costa, and Z. Jiang, “Asymptotic analysis of cooperative diversity systems with relay selection in a spectrum sharing scenario,” IEEE Trans. Veh. Technol., vol. 60, no. 2, pp. 457–472, Feb. 2011. [4] L. B. Le and E. Hossain, “Resource allocation for spectrum underlay in cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, no. 12, pp. 5306–5315, Dec. 2008. [5] X. Zhou, R.Zhang, and C.k. Ho, “Wireless information and power transfer: Architecture design and rate-energy trade off.” In Proc. IEEE GLOBECOM, 2012. [6] B. Medepally and N. B. Mehta, “Voluntary energy harvesting relays and selection in cooperative wireless networks,” IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3543–3553, Nov. 2010. [7] Ali A. Nasir, Xiangyun Zhou, Salman Durrani, and Rodney A. Kennedy, “Throughput and ergodic capacity of wireless energy Harvesting based DF relaying network,” IEEE Trans. Communications (ICC), 2014. [8] S. H. Lee, R. Zhang, and K. B. Huang, “Opportunistic wireless energy harvesting in cognitive radio networks,” in IEEE Trans. Wireless Commun., 2013. [9] K. Ishibashi, H. Ochiai, and V. Tarokh, “Energy harvesting cooperative communications,” in Proc. IEEE PIMRC, 2012. [10] Z. Ding, S. M. Perlaza, I. Esnaola, and H. V. Poor, “Power allocation strategies in energy harvesting wireless cooperative networks,” ArXiv Technical Report, 2013.
ScienceDirect Materials Today: Proceedings 4 (2017) 10304–10308
www.materialstoday.com/proceedings
ICEMS 2016
Throughput and Outage Probability of Wireless Energy Harvesting Based Cognitive DF Relaying Network Binod Prasad, Adduru U G Sankararao, Sumit Kundu, Sanjay Dhar Roy National Institute of Technology, Durgapur, 713209, West Bengal, India
Abstract This paper evaluates the throughput performance and outage probability of a secondary user (SU) in a decode-and-forward (DF) relaying network based on wireless energy harvesting under cognitive radio constraint. The energy constrained relay node first harvests energy through radio-frequency (RF) signals from the source node. Next, the relay node uses the harvested energy to forward the decoded source information to the destination node. The power transmitted by source and relay node is constrained by the tolerable interference threshold of the primary unit receiver. The source node transfers energy and information to the relay node through power splitting-based relaying (PSR). In PSR, the relay splits the received power for energy harvesting and information processing. The interference caused by a primary unit transmitter at the SU relay and destination nodes is also considered. Considering wireless energy harvesting constraint at the relay node, we analyse the achievable throughput and outage performance of a cognitive DF relaying network. We study the impact of different system parameters such as power splitting ratio, primary transmitter power and tolerable interference threshold of PU receiver on the throughput and outage performance of SU. © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Recent Trends in Engineering and Material Sciences (ICEMS-2016). Keywords: Energy harvesting relay; power splitting relaying; cognitive relaying network; spectrum sharing.
1. Introduction In cognitive radio the secondary user (SU) is allowed to concurrently occupy the radio frequency spectrum which is licensed to the primary user (PU). Cognitive radio with spectrum sharing is an emerging technology to overcome the inefficient utilization of scarce radio frequency spectrum [1]-[4]. In spectrum sharing environment cognitive user is allowed to access the licensed spectrum as long as its transmission does not interfere with the primary user’s communication. In wireless energy harvesting, RF energy is captured by the receiver antennas and converted into a 2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Recent Trends in Engineering and Material Sciences (ICEMS2016).
Binod Prasad/ Materials Today: Proceedings 4 (2017) 10304–10308
10305
DC voltage through appropriate circuits [5]. The application of wireless energy harvesting to cognitive radio based systems has been considered in [8]. An important application of wireless energy harvesting is in cooperative relaying networks, where an intermediate relay node assists in the information transmission from the source to the destination. The use of relay between source and destination reduces the overall path loss. Relay transmission improves throughput and extends coverage. If the relay node has limited battery reserves, then it relies on some external charging mechanism in order to remain active in the network [6]. Hence energy harvesting is important in such networks as it can enable information relaying. The outage performance of a typical cooperative communication system has been studied in [9]. However, the authors in [9] assumed that the relay has its own internal energy source and does not require external charging. The power allocation strategies and outage performance for a decode-and-forward (DF) relaying network under energy harvesting constraints is studied in [10]. A DF relaying network is considered in [7], the system throughput and ergodic capacity are analysed in the presence of energy harvesting constraints. The relay is assumed as energy constrained node and thus harvests energy from the RF signal broadcasted by the source node. In this paper, a DF relaying network is considered, throughput and outage probability are analysed in the presence of both energy harvesting and cognitive radio constraint. Power splitting (PS) receiver architecture proposed in [5] is adopted for separate information processing and energy harvesting at the relay node. In power splitting-based relaying (PSR), the relay splits the received power for energy harvesting and information processing. The power transmitted by the source and relay is limited by the tolerable interference threshold at the primary receiver (PU-Rx). The interference caused by a primary transmitter (PU-Tx) at relay and destination nodes is also considered while analysing the performance of SU. The main contributions of this work are summarized below: • The achievable throughput and outage probability are analysed for a DF relaying network in the presence of both wireless energy harvesting and cognitive radio constraints for PSR scheme. • Throughput and outage probabilities are compared for different system parameters, such as power splitting ratio, primary transmitter power and tolerable interference threshold at primary receiver. The rest of the paper is organized as follows. Section II describes the overall system model and assumptions. Section III presents the analysis of the throughput and outage probability of the network for PSR scheme. Section IV discusses the numerical results; finally Section V concludes the paper. 2. System Model The system model consists of a SU source (S), a SU relay (R), a SU destination (D), a PU receiver (PU-Rx) and a PU transmitter (PU-Tx) as shown in Fig. 1 (a). We assume that there is no direct link between S and D and hence SU transmission is assisted by energy constrained relay node working in DF mode. First the DF relay harvests energy from the source signal. Then, it uses harvested energy as a source of transmit power to forward decoded information to the destination while maintaining the interference produced at PU receiver below interference threshold Ith . The primary transmitter with transmitting power Pp causes interference at both relay and destinations. For the joint task of energy harvesting and information processing power splitting based relaying (PSR) architecture [5] is considered at relay node. During first half of the total block time T, the relay is assumed to harvest energy from the fraction of received power ρP and the remaining power, (1-ρ) P is used for the source to relay information transmission. During the second half of the block time the information is transmitted from relay to destination. The S to R, R to D, PU-Tx to R, PU-Tx to D, S to PU-Rx and R to PU-Rx channel gains are denoted by hsr , hrd , h pr , h pd , hsp and hrp respectively, are modelled as block-fading and frequency non selective parameters. The links are assumed to be Rayleigh fading [5] channels with zero mean and are constant over the block time T and independent 2 and identically distributed from one block to the next block. Therefore, the channel power gain, g xy = hxy is exponentially distributed, where x=y={s, r, d, p}. The maximum transmit powers of SU transmitter and relay due to underlay constraint are given by I Psmax = th g sp
and
I Prmax = th g rp
(1)
10306
Binod Prasad/ Materials Today: Proceedings 4 (2017) 10304–10308
The transmit power of the relay is derived from harvesting energy only. However, the relay transmit power is limited by the interference threshold at the primary receiver. If the harvested power Prharvest is less than the maximum allowed transmit power Prmax then the relay transmits with the power Prharvest , otherwise it transmits with power Prmax .
(b) Fig. 1. (a) System model for DF Relaying network; (b) Transmission block structure in the PSR scheme for energy harvesting and information processing.
3. Throughput and Outage Probability Under Power Splitting-Based Relaying (PSR) In this section the throughput and outage probability of the system are analysed by considering PSR scheme of energy harvesting as in [5]. The transmission block structure in PSR scheme for energy harvesting and information processing at the relay node is shown in Fig. 1 (b), where P is the received signal power and T is the block time. Half of the time, T/2 is used for the source to relay information transmission and remaining half, T/2 is used for the relay to destination information transmission. During the first half, the fraction of the received power, ρP is used for energy harvesting and remaining received power, (1-ρ)P is used for transmitting source information to relay node, where ρ ϵ (0,1). The choice of power fraction, ρ controls the achievable throughput and outage probability at destination. However the SU source and relay transmit powers are controlled in order to protect PU from the interference caused by their transmission. The SU source transmitting power is given by I (2) Ps = th g sp
The harvested energy at relay node during the time T/2 is given by Erharvest =
ηρ Ps g sr d1m
(3)
(T 2)
where 0 < η < 1 is the energy conversion efficiency which depends on the energy harvesting circuitry and d is the distance between source and relay and ‘m’ is the path loss exponent. The harvested power at the relay node is given by ηρ Ps g sr E harvest = Prhavest = r T 2 d1m
(4)
The relay transmitting power is the minimum of harvested power and maximum allowed transmit power as explained in Section II and is given by I (5) Pr = min Prharvest , th
g rp
The SNR at the relay node is given by γ sr =
Ps g sr (1 − ρ )
(
d1m σ n2 + Pp g pr
)
(6)
where σ n2 is the noise variance of overall AWGN at relay node, Pp is the PU-Tx transmitting power and Ps is the source power as given by (2). The SNR at the destination node is given by Pr g rd (7) γ rd = d 2m (σ n2 + Pp g pd )
where d2 is the distance between relay and destination, Pr is the relay transmit power as given by (5) and σ n2 is the variance of overall AWGN at destination.
Binod Prasad/ Materials Today: Proceedings 4 (2017) 10304–10308
10307
Throughput Analysis: In order to find out the throughput of the SU, the ergodic capacities of source to relay link and relay to destination links are to be evaluated as Csr = E{log 2 (1 + γ sr )} , Crd = E{log 2 (1 + γ rd )} (8) where γ sr depends on hsr and γ rd depends on both hsr and hrd . The ergodic capacity of the overall system is given by minimum of source to relay link and relay to destination link ergodic capacities C erg = min{Csr , Crd } (9) In PSR scheme, T/2 is the effective communication time from source node to the destination node in the block of time T seconds and if the source transmits at a fixed rate equal to the ergodic capacity, i.e., C erg bits/sec, the throughput of the system is given by T / 2 erg 1 (10) C τ= = min {Csr , Crd } T
2
where the throughput, τ depends on Ps , ƞ, ρ, d1, d2 , σ n2 . Outage Probability: The Outage probability is the probability that the ergodic capacity is less than some predefined rate, R bits/sec. The outage probability of the system is given by
(
Poutage = Pr C erg < R
(
)
= Pr min {Csr , Crd } < R
(11)
)
where the outage probability, Poutage depends on Ps, ƞ, ρ, d1, d2 , σ n2 . 4. Results and Discussions This section numerically analyses the throughput performance and outage probability of a DF relaying network in presence of energy harvesting with cognitive radio constraint. Unless otherwise stated, we set the energy harvesting efficiency, η =1, path loss exponent m=2.7. The distances d1, d2, are normalized to unit value. For simplicity, similar noise variances at the relay and the destination nodes are assumed. Fig. 2 plots the throughput, τ of SU with respect to ρ, for PSR scheme for different values of primary transmitting powers Pp. It is observed that the throughput of SU degrades as the primary transmit power increases. As the transmit power of SU increases, the interference at the relay node and SU receiver increases and hence reduce their respective SNR and there by decreases the throughput of SU. The impact of allowable interference value Ith on throughput τ of SU is shown in Fig. 3. An improvement in the SU throughput performance is observed as Ith increases. An increase in Ith means PU can tolerate more interference and hence allows SU and relay to increase their transmit power and thereby increases the throughput of SU. The impact of PU transmit power on the outage probability of SU is shown in Fig. 4 while keeping other parameters fixed. It is observed that the outage probability of SU degrades as the primary transmit power, Pp increases. As the transmit power of PU increases, the interference at the SU relay and destination increases and thereby increases the outage probability of SU. Further the effect of Ith on the outage probability of SU is also shown in Fig. 4. The outage probability of SU improves as the allowable interference increases.
Fig. 2: Throughput at SU destination against ρ for different values of interference threshold,
I th at PU-Rx.
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Fig. 3: Throughput at SU destination against ρ for different values of PU transmitting powers, Pp.
Fig. 4: Outage probability at SU destination against ρ for different values of Pp and Ith.
5. Conclusion In this paper, the performance of a cognitive DF relaying network under EH constraint at relay is evaluated in terms of throughput and outage probability of SU. We have also incorporated the effect of PU interference while evaluating the performance of SU. The performance of SU degrades as the interference from PU on SU increases. The impact of power splitting ratio (ρ) on SU performance is also studied. Further the impact of tolerable interference threshold on the throughput and outage probability of SU is also investigated. The performance of SU improves as allowable interference threshold at PU increases. References [1] A. Ghasemi and E. S. Sousa, “Fundamental limits of spectrum-sharing in fading environments,” IEEE Trans. Wireless Commun., vol. 6, no. 2, pp. 649– 658, Feb. 2007. [2] Trung Q. Duong, Tran Trung Duy, Michail Matthaiou , Theodoros Tsiftsis , and George K. Karagiannidis, “ Cognitive Cooperative networks in Dual Hop Asymmetric Fading Channels” , in IEEE GLOBECOM, 2013.. [3] H. Ding, J. Ge, D. B. da Costa, and Z. Jiang, “Asymptotic analysis of cooperative diversity systems with relay selection in a spectrum sharing scenario,” IEEE Trans. Veh. Technol., vol. 60, no. 2, pp. 457–472, Feb. 2011. [4] L. B. Le and E. Hossain, “Resource allocation for spectrum underlay in cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, no. 12, pp. 5306–5315, Dec. 2008. [5] X. Zhou, R.Zhang, and C.k. Ho, “Wireless information and power transfer: Architecture design and rate-energy trade off.” In Proc. IEEE GLOBECOM, 2012. [6] B. Medepally and N. B. Mehta, “Voluntary energy harvesting relays and selection in cooperative wireless networks,” IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3543–3553, Nov. 2010. [7] Ali A. Nasir, Xiangyun Zhou, Salman Durrani, and Rodney A. Kennedy, “Throughput and ergodic capacity of wireless energy Harvesting based DF relaying network,” IEEE Trans. Communications (ICC), 2014. [8] S. H. Lee, R. Zhang, and K. B. Huang, “Opportunistic wireless energy harvesting in cognitive radio networks,” in IEEE Trans. Wireless Commun., 2013. [9] K. Ishibashi, H. Ochiai, and V. Tarokh, “Energy harvesting cooperative communications,” in Proc. IEEE PIMRC, 2012. [10] Z. Ding, S. M. Perlaza, I. Esnaola, and H. V. Poor, “Power allocation strategies in energy harvesting wireless cooperative networks,” ArXiv Technical Report, 2013.