Adaptive beam design for UAV network with uniform plane array

Physical Communication 34 (2019) 58–65

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Adaptive beam design for UAV network with uniform plane array ∗

Weizhi Zhong a , Lei Xu a , Xin Liu b , , Qiuming Zhu c , Jianjiang Zhou c a

College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China Schools of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China c College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China b

article

info

Article history: Received 3 November 2018 Received in revised form 27 January 2019 Accepted 27 February 2019 Available online 1 March 2019 Keywords: UAV network mmWave communication Unstable beam pointing Adaptive beam design

a b s t r a c t To support millimeter wave (mmWave) communications for unmanned aerial vehicles (UAV) networks, many beamforming and tracking methods have been proposed. However, unstable beam pointing (UBP) problem between UAVs results from the wind or the estimation error of position is always ignored. In this paper, an adaptive beam design strategy is presented to overcome this shortcoming. Firstly, combining the feedback information of the beam deviation measured by different sensors, an equivalent model of time-varying channel is established, and the formulation of the expected data rate (EDR) is released. Then, combining the EDR and greedy geometric (GG) algorithm, a novel beam design method is presented. Finally, the performance of our proposed method is analyzed and compared with traditional ones. The experiment data proves that, when the problem of UBP happens, the proposed method can effectively improve the EDR of the group as well as lower complexity, which is of great significance in UAVs network. © 2019 Elsevier B.V. All rights reserved.

1. Introduction UAVs have received great attentions in recent years due to its wide range of applications [1–3]. For some special scenes where network of fixed infrastructure are destroyed, it is very important to quickly deploy a UAV cellular network to support urgent communications for the ground users [4,5]. In UAV cellular network, UAV communicates with each other in order to satisfy the need of various applications and services such as joint path planning, target monitoring and spectrum cognitive [6–8]. Therefore, overcrowded spectrum and increased channel capacity encourage the adoption of mmWave band owing to its abundant frequency spectrum resource [9,10]. Though the serious path loss of mmWave seemingly prevents it from practical applications, the short wavelength of mmWave enable the deployment of large amount of antennas onto a small region to achieve sufficient spatial gains and combat such path losses [11,12], which are very suitable for UAV mounted transmission. However, different from the traditional wireless communication systems, UAV based one would face new challenges. First, due to the continuous movement of UAVs, the channels may vary quickly, and the beam alignment (BA) should be finished in a very short time. Second, to effectively improve the gain of multipleantennas, the beams need to be narrow, which raise the difficulty of beam alignment and thus calls for the fast and reliable beam ∗ Corresponding author. E-mail address: [email protected] (X. Liu). https://doi.org/10.1016/j.phycom.2019.02.007 1874-4907/© 2019 Elsevier B.V. All rights reserved.

search method [13,14]. In [15] beam sweeping methods were adopted for beam tracking, which consumes a lot of resources to search the right beam. In [16,17], a beam tracking method for the UAV-satellite communication with multiple-antennas is proposed. The mechanical adjustment is firstly employed to roughly alleviate the effects of UAV navigation, and then an electrical adjustment to further correct beam pointing is done. However, due to the influence of the wind, the deviation of GPS, and the fault of attitude control, unstable beam pointing may happen and impact the communication quality significantly. This paper aims to fill this research gap. Overall, the major contributions of this paper are organized as follows (1) To improve the EDR of the UAV group when UBP happens, an adaptive beam design system with the key part of flight control, UAV users, and base station (BS) of air is devised. (2) Based on the proposed adaptive beam design system, an equivalent model of time-varying channel is derived in terms of the formulated beam deviation. (3) To evaluate the effects of beam deviation, the expression of the EDR is derived by taking into account of the equivalent channel model. (4) Combining the EDR and the GG algorithm, a novel beam design method which can produce the optimized beams is proposed. The remainder of this paper is organized as follows. The formulation of the problem is presented in 2. A novel beam design

W. Zhong, L. Xu, X. Liu et al. / Physical Communication 34 (2019) 58–65

59

gain, d user (ϕ) represents the response vector of the uniform plane array (UPA) for the transmitter, and it can be expressed as d user (ϕ) = d user (ϕh , ϕv ) = d user (ϕh ) ⊗ d user (ϕv )

(2)

and

{

[1, ejkdh sin(ϕh ) cos(ϕv ) , . . . , ejk(N −1)dh sin(ϕh ) cos(ϕv ) ]T ∈ CN [ ]T d user (ϕv ) = √1 1, ejkdv sin(ϕv ) , . . . , ejk(M −1)dv sin(ϕv ) ∈ CM M

d user (ϕh ) =

√1

N

(3) where ϕ is the angle of departure (AoD), h and v denote the domain of horizontal and vertical, respectively, the value of k is 2π/λ and λ is the carrier wavelength, da (a ∈ {h, v}) is the distance between antenna elements, and are generally equal to half wavelengths. In the same way, we also can achieve the array response d BS (θ) of the receiver. Then the received signal of the BS can be modeled as follow y = w∗ Hcs + w∗ n

Fig. 1. The system model of ABDS.

(4)

where c = FRF vBB ∈ C is the procoding vector of the transmitter, [which is composed of an analog beam steering matrix ] FRF = f 1 ,f 2 , . . . ,f NRF ∈ CNBS ×NRF and a baseband beamformer vector vBB . Equivalently, the hybrid combiner w = WRF wBB ∈ CNBS is composed of a RF combiner WRF and a baseband combiner [ ] wBB . s is the transmit symbol and satisfy the constraints of E |s|2 ≤ 1, and n is the additive white Gaussian noise (AWGN) with mean zero and variance σ 2 = 1. Pointing deviation (PD) of the beam needed to be quantified to facilitate the problem formulation. According to the output of sensors, the PD of beams can be expressed as Fig. 2. As depicted in Fig. 2, the blue one represents the coverage area of the target beam, and the red one correspondent to the unstable beam with PD. Therefore, in combination with (2), the array response incorporating the angle deviation can be expressed as Nuser

method is proposed in 3, while the simulations are shown in Section 4. Finally, conclusions are drawn in Section 5. We use following notations throughout this paper: C denotes the field of complex numbers, a is a scalar and A is a matrix. |A| is the determinant of A, ∥A∥F is Frobenius norm, where a AT , A−1 , and A∗ are transpose, inverse, and conjugate transpose, respectively. [A]:,R ( [A]R,: ) are the columns (rows) of matrix A with indexes in the set R. E [·] is the expectation operator and ⊗ is the Kronecker product. 2. Problem formulation 2.1. Adaptive beam design system The proposed adaptive beam design system (ABDS) consists of three parts, the flight control system (FCS) (usually mounted in the body frame and consists of several sensors like micro inertial unit (MIMU) and GPS), the UAVs users, and the base station (BS) of air. The structure of the ABDS is described as Fig. 1 and the system consists of four working processes which can be described as follow. (1) When UBP happens, the information of position and status should be collected by the sensors of UAV users. (2) With the help of control link, the information collected by the sensors is sent back to the BS of air which has higher processing capability. (3) In the central processor of BS, the beams are optimized using the proposed method by taking into account of the collected information. (4) The BS feedback the results and all of UAV users (the transmitters) are equipped with the optimized beams, which can increase the EDR of the system.

l l l l d user ϕ l (t ) = d user ϕv, 0 + A (t ) , ϕh,0 + B (t ) .

(

)

(

)

(5)

l l where ϕv, 0 , ϕh,0 , l = 1, 2 · · · , L are the angles of azimuth and elevation for the lth transmitter (the lth UAV user). Denote Al (t ) and Bl (t ) as the angle deviation which are time-variant and uniformly distribute in a certain range of [−ψl , ψl ]. Obviously, the time varying of the array response is caused by the continuous deviation of the beam pointing. Based on the results of (5), the channel matrix between the BS and the lth UAV user can be described as

√

NBS Nuser Kl a0,l d BS 1+Kl

( l ) ∗ ( l ) l l θh,0 , θv, 0 d user ϕh (t ) , ϕv (t ) √ ( )[ ( ) Kl l = NBS1N+user a0,l d BS θhl ,0 , θv, d user ϕhl ,0 + Al (t ) 0 Kl ( l )]∗ l ⊗ d user ϕv, . 0 + B (t )

Hl (t ) =

(6)

l l where θv, 0 , θh,0 are the arrival angle of azimuth and elevation, respectively, Kl is Ricean factor of the channel. From (6) we can find that, the UBP would cause the time varying of channel.

2.2. Equivalent channel 2.3. Expected data rate The mmWave channel is usually dominated by a line-of-sight (LOS) path, while a few weak non-line-of-sight (NLOS) paths can be neglected [18,19]. Therefore, the mmWave channel can be modeled as

√ H=

NBS Nuser K 1+K

a0 d BS (θ )d user (ϕ ) ∗

(1)

where K means the Ricean factor, NBS = N × M and Nuser = N × M are the antenna number of arrays, a0 is the complex channel

According to (6), the EDR of the lth transmitter can be expressed as

˜ Rl = E [Rl (t )] ⏐]} { [⏐ ∗ ∗ 1 ∗ ⏐ = E log2 ⏐I + ρl R− n wl Hl (t ) c l c l Hl (t ) wl { [⏐ ∗ −1 ∗ ⏐ = E log2 I + ρl Rn wBB,l WRF ,l Hl (t ) FRF ,l vBB,l v∗BB,l ⏐]} × F∗ H∗ (t ) WRF ,l wBB,l ⏐ RF ,l

l

(7)

60

W. Zhong, L. Xu, X. Liu et al. / Physical Communication 34 (2019) 58–65

Fig. 2. The PD of beams.

Fig. 3. Distribution of the beam energy.

W. Zhong, L. Xu, X. Liu et al. / Physical Communication 34 (2019) 58–65

Fig. 4. The performance of EDR.

Fig. 5. The performance of EDR.

61

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W. Zhong, L. Xu, X. Liu et al. / Physical Communication 34 (2019) 58–65

Fig. 6. Computational time required to operate the beam design methods as function of (a) the number of antennas and (b) the number of RF chains.

where Rl (t ) is the data rate, ρl is the transmit power, and Rn = w∗ wσ 2 is the auto-correlation matrix of the noise. Without considering the interference, the EDR of the UAVs group can be modeled as

Rgroup =

L ∑

˜ Rl

of the beam pointing. Consequently, (11) can be calculated as

{ [ ⏐ ⏐2 ]} ˜ Rl = E log2 1 + ρl ⏐h∗l (t ) FRF ,l vBB,l ⏐ { [ ⏐ ( ⏐2 ]} ) l l ⏐ = E log2 1 + ρl ⏐h∗l ϕhl ,0 + Al (t ) , ϕv, 0 + B (t ) FRF ,l vBB,l

(8) (a)

l=1

=

where L is the number of UAV users. In order to achieve the largest value of Rgroup , we need to make every ˜ Rl maximum. However, Directly maximizing Rl requires a joint optimization ) ( ∗ ˜ over the four variable of wBB,l , W∗RF ,l , FRF ,l , vBB,l . To simplify the optimization ( problem, ) we focus on the design of the transmitter precoders FRF ,l , vBB,l [19]. An SVD of Hl (t ) = Ul (t ) Σl (t ) V∗l (t ) and standard mathematical manipulation may be used, and then (7) can be calculated as

)] [ ( ˜ Rl = E log2 |I + ρl Σl2 (t ) V∗l (t ) FRF ,l vBB,l v∗BB,l F∗RF ,l Vl (t )

(9)

furthermore, defining the matrices Σl (t ) and Vl (t ) as

Σl (t ) =

[

Σl,1 (t ) 0

0

Σl,2 (t )

]

[ ] , V = Vl,1 (t ) , Vl,2 (t )

(10)

where Σl,1 (t ) ∈ C 1×1 and Vl,1 (t ) ∈ C NBS ×1 , then (9) can be simplified as

[ [ ]] ˜ Rl = E log2 |I + ρl Σl2 (t ) V∗l (t ) FRF ,l vBB,l v∗BB,l F∗RF ,l Vl (t ) | ]] [ (a) [ ≈ E log2 1 + ρl Σl2,1 (t ) V∗l,1 (t ) FRF ,l vBB,l v∗BB,l F∗RF ,l Vl,1 (t) [ [ ]] = E log2 1 + ρl h∗l (t ) FRF ,l vBB,l v∗BB,l F∗RF ,l h (t ) [ [ ]] ⏐ ⏐2 = E log2 1 + ρl ⏐h∗l (t ) FRF ,l vBB,l ⏐

where (a) is the result of [20], hl (t ) = Σl,1 (t ) Vl,1 (t ) is the equivalent channel state information (CSI). From (6) we can find that the time variation of hl (t ) is caused by the continuous deviation

ϕh,0 −ψl

=

ϕv,0 −ψl

l +ψ ∫ ϕhl ,0 +ψl ∫ ϕv, l 0

ϕhl ,0 −ψl

[

∫∫

Rl log2 1+ρl Gl

(

l −ψ ϕv, l 0

ϕhl ,ϕvl

)]

dϕvl dϕhl

dϕvl dϕhl

l l Rl dϕv dϕh

∫∫

( ≤ log2 1 +

(b)

ρl

∫∫

R l Gl

(

) ) ϕhl ,ϕvl dϕvl dϕhl

l l Rl dϕv dϕh

∫∫

(12) where Rl is the range of beam deviation for the lth transmitter and Gl (ϕhl , ϕvl ) is the equivalent gain of beam in the direction (ϕhl , ϕvl ). Note that (b) is derived based on Jensen’s inequality and the equality in (12) holds if the Gl (ϕhl , ϕvl ) is uniform over the region Rl [18]. It has been proved by (12) that, in order to guarantee the maximal EDR when UBP happens, the ideal beam produced by the lth transmitter should have a constant gain within the range of Rl . At the same time, the energy outside Rl should be zero. Of course, such ideal beam does not exist in practice. In this paper, opt we regard the optimal procoding vector c l corresponding to the ideal beam as the objective of optimization. According to the conclusion of literature [20–23], it is not opt difficult to obtain the value of the ideal procoding matrix c l based on the deviation range of Rl . In the actual beam forming opt system, the RF terminal cannot directly generates c l , therefore, opt it is necessary to approximate the value of c l with matrix FRF ,l and vector vBB,l [18]. In the condition, the procoder optimization problem can be summarized as opt

opt

˜ FRF ,l vBB,l = argmax [ Rl] FRF ,l :,i s.t . ∈ F RF , i = 1, 2, . . . , NRF ∥FRF ,l vBB,l ∥2F = 1 ; l = 1, 2.., L

( (11)

l +ψ )] [ ( ∫ ϕhl ,0 +ψl ∫ ϕv, l 0 log2 1+ρl Gl ϕhl ,ϕvl dϕvl dϕhl l l

)

( )

(13)

where F RF is the set of usable RF precoders corresponding to a hybrid architecture.

W. Zhong, L. Xu, X. Liu et al. / Physical Communication 34 (2019) 58–65 Table 1 GG algorithm based beam optimization. Stage 1:

FRF = [ ] ; Fres = c l ;

3:

for i ≤ NRF do

4:

opt

Fq = quantify (Fres )

5:

FRF = [FRF

6:

M = max |Fres | ,

7:

J = find (Fres (J ) ≥ (M + 2) /2)

8:

( ) ′ λ ⏐= ⏐mean Fres (J ) /F q (J ) ⏐ ′⏐ if ⏐λ ⏐ > (M + m) /2 then ( ′ ⏐ ′ ⏐) ⏐ ⏐ λ = λ / ⏐λ ⏐ · [(M + m) /2]

9: 10:

Fq ]

11:

else

12:

λ=λ

4. Simulatioin results m = min |Fres |

else if

14:

Fres = Fres − λFq

16:

In this section, we carry out several experiments to evaluate the performance of our proposed adaptive beam design method. We split the problem into two scenes of single user and multiple users. Both transmitters and receiver are equipped with UPA of λ/2-spaced isotropic antennas.

′

13: 15:

As shown in Fig. 3, with the increase of the iteration times, the distribution of the beam energy becomes concentrated from decentralization, and gradually converges to the area of Rl . Finally, the shape of the beam nearly fits with the ideal beam which has constant gain in the target range.

Operation

2:

end for [

vBB,l =

(

F∗RF FRF

4.1. BS equipped with single user

)−1

opt

F∗RF c l

]

3. Novel beam design method

The design of the matrix FRF and vector vBB can be accomplished by solving opt

  = arg min c opt − FRF ,l vBB,l F , l [ ]FRF ,l ,F BB,l FRF ,l :,i ∈ F RF i = 1, 2, . . . , NRF ∥FRF ,l vBB,l ∥2F = 1 opt

FRF , vBB

{

s.t .

63

}

(14)

Unfortunately, there is no closed-form solution for the optimization problem of (14). To access a feasible, but usually suboptimal solution, the minimization problem in (14) can be treated as a sparse approximation problem solved by using orthogonal matching pursuit (OMP) algorithm, which are proposed in most of the literatures with advantage of simplicity [20]. However, the solving of the inverse matrix in OMP algorithm will increase the calculated complexity. Moreover, non-complete dictionary sets of the OMP algorithm will reduce the accuracy. In this paper, we employ a GG algorithm [24] to solve (14) to overcome this problem. The optimization process of FRF and vBB using GG algorithm is shown in Table 1. opt Firstly, the residual matrix Fres is initialized by c l and then is quantified with 2-bit phase shifters of RF. Then, An OMPbased strategy is used to select the column vectors of beam control matrix FRF from the dictionary in stage 5. Compared to the OMP algorithm, we use the dictionary-free strategy and save only one matrix–vector multiplication to reduce computational complexity. Stages 6 to 13 represent the residual update strategy. The process continues until all NRF beam forming vectors have opt been chosen. Finally, an optimal beam pattern in terms of FRF opt and vBB can be obtained. It should be noted that this algorithm opt does not require a dictionary set and approximates c l by a hybrid architecture with 2-bit phase shifters of RF, which makes computational complexity lower than the OMP method. We show the relationship between the number of iterations and the energy distribution of beams in Fig. 3. Assuming that the range of Rl is 0.05π ×0.05π and there are five beams in the group.

We compare the performance of beam design methods in terms of the EDR. For the transmitter (UAV user), we denote the simulation parameters as follows, the signal to noise ratio (SNR) observed at the BS is 5 dB (8 dB), the channel gain a0 is 1, and the carrier frequency fc is 60 GHz. Due to the channel model of mmWave is LOS channel, the Ricean factor K can be set as K ≫ 1. We consider a BS with NBS = 16 × 8 antennas, and it is stable. Respectively, the user is equipped with Nuser = 16 × 8(Nuser = 20 × 10) antennas and its beam is unstable. Moreover, the number of RF chains satisfies the condition of NRF = 6. The performance of EDR is shown in Fig. 4. As we can observe that when the angular deviation increases, the EDR will significantly decrease. The trends of the methods are obviously different. When the beam deviation is smaller than 5o , the performance of the proposed method and the narrow beam presented in [20] are better than the wide beam proposed in [18]. However, when the beam deviation is larger than 5o , both of the proposed method and the wide beam are better than the narrow beam. The reason is that the proposed adaptive beam design method can adjust the beam shape in terms of the beam deviation, and can generate near constant beam gain within the deflected region Rl (near flattened beam pattern). The simulated results of the EDR performance are consistent with the theoretical results of (12). 4.2. BS equipped with multiple users In this section, we aim at analyzing the performance of the multiusers. We assume that all of the transmitters (UAV user) are unstable, and there is no attitude deflection at the receiver (BS). In the simulation, the power constraint, the carrier frequency, and the bandwidth are the same as Fig. 4. We also consider a BS with NBS = 16 × 8 antennas, and the users with Nuser = 16 × 8 (Nuser = 20 × 10) antennas respectively. The number of RF chains is NRF = 6, and the number of the users is L = 9 (ten UAVs in the group). Ψl , l = 1, 2, . . . , L is randomly distributed in [0, π/16], and each user designs the beam according to its different deviated region Ψl . In order to investigate the performance of the beam design method, the relationship between Rgroup and SNR is described in Fig. 5. From Fig. 5(a) and Fig. 5(b), we can see that the proposed adaptive beam design method significantly outperforms the others, especially in larger antenna array. The main reason is that, the proposed method changes the beam pattern of each user in terms of the deviated angle. According to (7) and (8), when each user’s EDR is gets maximum, the EDR of the group is also maximal, and moreover, the larger the antenna array, the better the beam pattern.

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4.3. Computational complexity One of the major problems of the hybrid precoding method is their complexity, especially for UAVs applications. In this section, we will present the comparisons of the complexity between the proposed method and the traditional one. The computer of PC with quad-core Inter Core i5 is adopted, and the performance is evaluated by Monte Carlo simulations with 1000 independent channel realizations. The computational time required to operate the beam design methods is compared in Fig. 6. In the experiment of Fig. 6(a), the number of RF chains is NRF = 4 and the other simulation parameters are the same as Fig. 5. In Fig. 6(b), we consider a hybrid architecture with Nuser = 16 × 8 antennas and plot the average computational times as a function of NRF . From Fig. 6(a) we can observe that, in the same numbers of RF chains, the proposed method provides shorter computational time than the strategy in [18] and [20]. We also evaluate the efficiency of the methods by taking into account the number of RF chains. It is evident from Fig. 6(b) that, in the same numbers of antenna elements, the computational time of the proposed algorithm is much less than the methods in [18] and [20]. 5. Conclusions A novel beam design method to deal with the problem of UBP for mmWave communications in UAV network has been proposed in this paper. Adaptive transmission mechanism has been established, and the expected data rate has been derived by taking into account of deviation region. By using a GG algorithm, the beam pattern has been optimized. The simulation results have validated that our proposed method has the better performance in UAV network when time-invariant channel happens. It is worth noting that there are some necessary extensions of current work. For example, in real UAVs communication scene, a more low-complexity and universal beam design scheme should be applied. Acknowledgments This work was supported by Aeronautical Science Foundation of China (2017ZC52021), the National Natural Science Foundations of China (61827801 and 61601221), the Joint Foundations of the National Natural Science Foundations of China and the Civil Aviation of China (U1833102), the Fundamental Research Funds for the Central Universities (NS2017066), the China Postdoctoral Science Foundations under Grants (2015M580425 and 2018T110496), the Foundation of Graduate Innovation Center in NUAA (kfjj20171501) and China Postdoctoral Science Foundation Funded Project (2015M581791). References [1] Nan Zhao, Fen Cheng, F. Richard Yu, Jie Tang, Yunfei Chen, Guan Gui, Hikmet Sari, Caching UAV assisted secure transmission in hyper-dense networks based on interference alignment, IEEE Trans. Commun. 66 (5) (2018) 2281–2294. [2] Fen Cheng, Shun Zhang, Zan Li, Yunfei Chen, Nan Zhao, F. Richard Yu, Victor C.M. Leung, UAV Trajectory optimization for data offloading at the edge of multiple cells, IEEE Trans. Veh. Technol. 67 (7) (2018) 6732–6736. [3] Y. Zeng, R. Zhang, J.L. Teng, Wireless communications with unmanned aerial vehicles: opportunities and challenges, IEEE Commun. Mag. 54 (5) (2016) 36–42. [4] S. Chandrasekharan, K. Gomez, A. Al-Hourani, et al., Designing and implementing future aerial communication networks, IEEE Commun. Mag. 54 (5) (2016) 26–34.

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Weizhi Zhong is a lecturer in the College of Astronautics, Nanjing University of Aeronautics and Astronautics (NUAA). She received her B.S. and M.S. degrees in communication and information system from the Jilin University, and received the Ph.D. degree in communication and information system from the Harbin Institute of Technology, respectively. Her research interests include millimeter wave communication and MIMO technique. E-mail: [email protected].

W. Zhong, L. Xu, X. Liu et al. / Physical Communication 34 (2019) 58–65

Lei Xu is a graduate student in Nanjing University of Aeronautics and Astronautics. He received a bachelor’s degree from Kharkiv Aviation Institute in Ukraine and Nanjing University of Aeronautics and Astronautics in China, respectively. His research interests include mm-wave communication of UAV and beamforming technology. E-mail: [email protected].

Xin Liu is an associate professor in school of information and communication engineering, Dalian University of Technology. He received B.S. degree in information and communication engineering from Harbin Institute of Technology, and M.S. and Ph.D. in information and communication engineering from Harbin Institute of Technology respectively. From 2012 to 2013, he was also a research fellow at Nan yang Technological University. His research interests include cognitive radio and UAV Communications.

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Qiuming Zhu is an associate professor in wireless communication, Nanjing University of Aeronautics and Astronautics (NUAA). He received B.S. degree in electronic engineering from NUAA, and M.S. and Ph.D. in communication and information system respectively. From 2016 to 2017, he was also an academic visitor at Heriot-Watt University. His research interests include channel modeling for 5G communication systems and wireless channel emulator. E-mail: zhuqiuming@nuaa. edu.cn. Jianjiang Zhou is a professor in the department of electronic and information engineering, Nanjing University of Aeronautics and Astronautics (NUAA), He received his Ph.D. in communication and information system form NUAA. His research interests include the airborne electronic information system and target recognition. E-mail: [email protected].