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Volume 43 Issue 12
Dec.  2021
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Yongjun XU, Hao XIE, Qianbin CHEN, Qilie LIU. Beamforming Algorithm for MIMO-based Heterogeneous Networks with Hardware Impairments[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3571-3579. doi: 10.11999/JEIT200776
Citation: Yongjun XU, Hao XIE, Qianbin CHEN, Qilie LIU. Beamforming Algorithm for MIMO-based Heterogeneous Networks with Hardware Impairments[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3571-3579. doi: 10.11999/JEIT200776

Beamforming Algorithm for MIMO-based Heterogeneous Networks with Hardware Impairments

doi: 10.11999/JEIT200776
Funds:  The National Natural Science Foundation of China (61601071, 62071078), Natural Science Foundation of Chongqing (cstc2019jcyj-xfkxX0002), The Graduate Scientific Research Innovation Project of Chongqing (CYS20253, CYS20251), The Chongqing Science and Technology Innovation Leading Talent Support Program (CSTCCXLJRC201908), The Basic and Advanced Research Projects of CSTC (2019jcyj-zdxmX0008)
  • Received Date: 2020-09-01
  • Rev Recd Date: 2021-09-22
  • Available Online: 2021-10-27
  • Publish Date: 2021-12-21
  • Multiple-Input Multiple-Output (MIMO)-based Heterogeneous Network (HetNet) can improve system capacity and achieve more connectivity, which is dramatically concerned by academia and industry. Therefore, it becomes one of the key technologies in the next-generation communication system. However, due to the effect of factors such as amplifier nonlinearities, phase noise, and I/Q imbalance, these impairments become the bottlenecks for further improving the performance of beamforming in MIMO-based HetNets. In order to solve this problem, this paper studies the beamforming design in MIMO-based HetNets by considering hardware impairments ahead of time. Firstly, the resource allocation problem is formulated as the total transmit power minimization of the system with hardware impairments under the constraints of the maximum transmit power of each base station and the minimum signal-to-interference-plus-noise ratio of each user. Then, the original non-convex problem is transformed into an equivalent convex optimization problem by using the methods of the equivalent transformation and the semidefinite programming relaxation. Simulation results verify that the proposed algorithm has a low outage probability and can overcome the impact of hardware impairments by comparing it with the traditional beamforming algorithm with perfect hardware.
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  • [1]
    徐勇军, 彭瑶, 余晓磊, 等. 面向5G协作通信系统的资源分配技术综述[J]. 重庆邮电大学学报:自然科学版, 2019, 31(2): 143–157. doi: 10.3979/j.issn.1673-825X.2019.02.001

    XU Yongjun, PENG Yao, YU Xiaolei, et al. Survey on resource allocation techniques for 5G cooperative communication networks[J]. Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition, 2019, 31(2): 143–157. doi: 10.3979/j.issn.1673-825X.2019.02.001
    [2]
    李国权, 徐勇军, 陈前斌. 基于干扰效率多蜂窝异构无线网络最优基站选择及功率分配算法[J]. 电子与信息学报, 2020, 42(4): 957–964. doi: 10.11999/JEIT190419

    LI Guoquan, XU Yongjun, and CHEN Qianbin. Interference efficiency-based base station selection and power allocation algorithm for multi-cell heterogeneous wireless networks[J]. Journal of Electronics &Information Technology, 2020, 42(4): 957–964. doi: 10.11999/JEIT190419
    [3]
    NGUYEN L D, TUAN H, DUONG T Q, et al. Downlink beamforming for energy-efficient heterogeneous networks with massive MIMO and small cells[J]. IEEE Transactions on Wireless Communications, 2018, 17(5): 3386–3400. doi: 10.1109/TWC.2018.2811472
    [4]
    XU Yongjun, GUI Guan, GACANIN H, et al. A survey on resource allocation for 5G heterogeneous networks: Current research, future trends, and challenges[J]. IEEE Communications Surveys & Tutorials, 2021, 23(2): 668–695. doi: 10.1109/COMST.2021.3059896
    [5]
    STUDER C, WENK M, and BURG A. MIMO transmission with residual transmit-RF impairments[C]. Proceedings of 2010 International ITG Workshop on Smart Antennas (WSA), Bremen, Germany, 2010: 189–196.
    [6]
    ZHANG Jiayi, XUE Xipeng, BJÖRNSON E, et al. Spectral efficiency of multipair massive MIMO two-way relaying with hardware impairments[J]. IEEE Wireless Communications Letters, 2018, 7(1): 14–17. doi: 10.1109/LWC.2017.2750162
    [7]
    TLEBALDIYEVA L, MAHAM B, and TSIFTSIS T A. Capacity analysis of device-to-device mmWave networks under transceiver distortion noise and imperfect CSI[J]. IEEE Transactions on Vehicular Technology, 2020, 69(5): 5707–5712. doi: 10.1109/TVT.2020.2983417
    [8]
    BALLTI E, GUIZANI M, HAMDAOUI B, et al. Aggregate hardware impairments over mixed RF/FSO relaying systems with outdated CSI[J]. IEEE Transactions on Communications, 2018, 66(3): 1110–1123. doi: 10.1109/TCOMM.2017.2776261
    [9]
    SHARMA P K and UPADHYAY P K. Cognitive relaying with transceiver hardware impairments under interference constraints[J]. IEEE Communications Letters, 2016, 20(4): 820–823. doi: 10.1109/LCOMM.2016.2533500
    [10]
    SOLANKI S, UPADHYAY P, DA COSTA D B, et al. Joint impact of RF hardware impairments and channel estimation errors in spectrum sharing multiple-relay networks[J]. IEEE Transactions on Communications, 2018, 66(9): 3809–3824. doi: 10.1109/TCOMM.2018.2832623
    [11]
    LI Xingwang, LI Jingjing, LIU Yuanwei, et al. Residual transceiver hardware impairments on cooperative NOMA networks[J]. IEEE Transactions on Wireless Communications, 2020, 19(1): 680–695. doi: 10.1109/TWC.2019.2947670
    [12]
    BJÖRNSON E, HOYDIS J, KOUNTOURIS M, et al. Massive MIMO systems with non-ideal hardware: Energy efficiency, estimation, and capacity limits[J]. IEEE Transactions on Information Theory, 2014, 60(11): 7112–7139. doi: 10.1109/TIT.2014.2354403
    [13]
    ZHU Jun, NG D W K, WANG Ning, et al. Analysis and design of secure massive MIMO systems in the presence of hardware impairments[J]. IEEE Transactions on Wireless Communications, 2017, 16(3): 2001–2016. doi: 10.1109/TWC.2017.2659724
    [14]
    PAPAZAFEIROPOULOS A, CLERCKX B, and RATNARAJAH T. Rate-splitting to mitigate residual transceiver hardware impairments in massive MIMO systems[J]. IEEE Transactions on Vehicular Technology, 2017, 66(9): 8196–8211. doi: 10.1109/TVT.2017.2691014
    [15]
    BOSHKOVSKA E, NG D W K, DAI Linglong, et al. Power-efficient and secure WPCNs with hardware impairments and non-linear EH circuit[J]. IEEE Transactions on Communications, 2018, 66(6): 2642–2657. doi: 10.1109/TCOMM.2017.2783628
    [16]
    PAPAZAFEIROPOULOS A K and RATNARAJAH T. Downlink MIMO HCNs with residual transceiver hardware impairments[J]. IEEE Communications Letters, 2016, 20(10): 2023–2026. doi: 10.1109/LCOMM.2016.2593480
    [17]
    PAPAZAFEIROPOULOS A and RATNARAJAH T. Toward a realistic assessment of multiple antenna HCNs: Residual additive transceiver hardware impairments and channel aging[J]. IEEE Transactions on Vehicular Technology, 2017, 66(10): 9061–9073. doi: 10.1109/TVT.2017.2710188
    [18]
    PAPAZAFEIROPOULOS A, RATNARAJAH T, KOURTESSIS P, et al. Nuts and bolts of a realistic stochastic geometric analysis of mmWave HetNets: Hardware impairments and channel aging[J]. IEEE Transactions on Vehicular Technology, 2019, 68(6): 5657–5671. doi: 10.1109/TVT.2019.2908044
    [19]
    DAMNJANOVIC A, MONTOJO J, WEI Yongbin et al. A survey on 3GPP heterogeneous networks[J]. IEEE Wireless Communications, 2011, 18(3): 10–21. doi: 10.1109/MWC.2011.5876496
    [20]
    FANG Fang, CHENG Julian, and DING Zhiguo. Joint energy efficient subchannel and power optimization for a downlink NOMA heterogeneous network[J]. IEEE Transactions on Vehicular Technology, 2019, 68(2): 1351–1364. doi: 10.1109/TVT.2018.2881314
    [21]
    SHENG Min, WANG Liang, WANG Xijun, et al. Energy efficient beamforming in MISO heterogeneous cellular networks with wireless information and power transfer[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(4): 954–968. doi: 10.1109/JSAC.2016.2544538
    [22]
    BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004.
    [23]
    LUO Zhiquan, MA W K, SO A M C, et al. Semidefinite relaxation of quadratic optimization problems[J]. IEEE Signal Processing Magazine, 2010, 27(3): 20–34. doi: 10.1109/MSP.2010.936019
    [24]
    WANG Kunyu, SO A M C, CHANG T H, et al. Outage constrained robust transmit optimization for multiuser MISO downlinks: Tractable approximations by conic optimization[J]. IEEE Transactions on Signal Processing, 2014, 62(21): 5690–5705. doi: 10.1109/TSP.2014.2354312
    [25]
    CHU Zheng, ZHU Zhengyu, JOHNSTON M, et al. Simultaneous wireless information power transfer for MISO secrecy channel[J]. IEEE Transactions on Vehicular Technology, 2016, 65(9): 6913–6925. doi: 10.1109/TVT.2015.2499439
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