广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (03): 119-130.doi: 10.12052/gdutxb.230040

• 信息与通信技术 • 上一篇    下一篇

智能反射面辅助认知无线携能通信次用户网络吞吐量优化

乐文英, 崔苗, 张广驰   

  1. 广东工业大学 信息工程学院, 广东 广州 510006
  • 收稿日期:2023-02-28 出版日期:2024-05-25 发布日期:2024-05-25
  • 通信作者: 崔苗(1978-),女,讲师,博士,主要研究方向为电子信息与无线通信技术,E-mail:cuimiao@gdut.edu.cn
  • 作者简介:乐文英(1996-),女,硕士研究生,主要研究方向为智能反射平面和认知无线电等,E-mail:353903450@qq.com
  • 基金资助:
    广东省科技计划项目(2023A0505050127，2022A0505020008，2022A0505050023) ；广东省自然科学基金资助项目(2023A1515011980) ；江西省军民融合北斗通航重点实验室开放基金项目(2022JXRH0004) ；广东省海洋经济发展项目(粤自然资合[2023]24号)

Throughout Optimization for IRS-assisted Cognitive SWIPT Secondary User Networks

Le Wen-ying, Cui Miao, Zhang Guang-chi   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-02-28 Online:2024-05-25 Published:2024-05-25

HTML

PDF (PC)

306

摘要： 为了提高认知无线携能通信(Simultaneous Wireless Information and Power Transfer, SWIPT)网络的频谱利用率并改善其能量受限情况，本文研究智能反射面(Intelligent Reflecting Surface, IRS)辅助的认知SWIPT网络，其中主用户网络以覆盖方式与次用户网络共享频谱，而次用户发射机同时为主用户发射机供能并与次用户接收机传输信息。提出次用户网络吞吐量优化算法，在满足次用户发射机的最大发射功率约束、主用户网络的最小吞吐量约束、总时隙约束以及智能反射面移约束的条件下，联合优化次用户发射机的波束成形矢量、时隙分配和智能反射面反射相移，最大化次用户网络吞吐量。该问题的优化变量相互耦合并且结构高度非凸，难以直接求解。所提算法采用交替优化、半正定松弛以及连续凸逼近方式，将原问题转化为三个子问题进行迭代求解。仿真结果表明与已有基准方案相比，所提算法能明显提高次用户网络的吞吐量。

关键词: 智能反射平面, 认知无线携能通信, 覆盖方式

Abstract: In order to improve the spectrum utilization efficiency and the energy limitation of cognitive simultaneous wireless information and power transfer (SWIPT) network, a study is conducted on an intelligent reflecting surface (IRS) -assisted cognitive SWIPT network, where the primary user network shares its spectrum with the secondary user network in overlay mode, the secondary transmitter simultaneously transmits energy to the primary transmitter and information to the secondary receiver. An optimization algorithm for the throughput of the secondary user network is proposed, under the constraints of the maximum transmit power of the secondary user transmitter, the minimum throughput requirement of the primary user network, the available time slots, and the phase shifts of the IRS, and the beamforming vector of the secondary transmitter, the time slot allocation, and the phase shifts of the IRS are jointly optimized to maximize the throughput of the secondary user network. The optimization variables of the proposed problem are coupled with each other and the structure is highly non-convex, making it is difficult to solve directly. The proposed algorithm applies alternating optimization, semi-positive relaxation, and successive convex approximation techniques to transform the original problem into three subproblems for alternative solution. Simulation results show that the proposed algorithm can significantly improve the throughput of the secondary user network compared with the existing benchmark schemes.

Key words: intelligent reflecting surface, cognitive simultaneous wireless information and power transfer, overlay mode

中图分类号: 

  • TN929.5
[1] WONG V W, SCHOBER R, NG D W K, et al. Key Technologies for 5G Wireless Systems [M]. Cambridge: Cambridge University Press, 2017.
[2] DATLA D, WYGLINSKI A M, MINDEN G J. A spectrum surveying framework for dynamic spectrum access networks [J]. IEEE Transactions on Vehicular Technology, 2009, 58(8): 4158-4168.
[3] ZHANG W, WANG C X, GE X, et al. Enhanced 5G cognitive radio networks based on spectrum sharing and spectrum aggregation [J]. IEEE Transactions on Communications, 2018, 66(12): 6304-6316.
[4] HONG X, WANG J, WANG C X, et al. Cognitive radio in 5G: a perspective on energy-spectral efficiency trade-off [J]. IEEE Communications Magazine, 2014, 52(7): 46-53.
[5] XIONG T, YAO Y D, REN Y, et al. Multiband spectrum sensing in cognitive radio networks with secondary user hardware limitation: random and adaptive spectrum sensing strategies [J]. IEEE Transactions on Wireless Communications, 2018, 17(5): 3018-3029.
[6] TORLAK M, NAMGOONG W. Sub-nyquist sampling receiver for overlay cognitive radio users [J]. IEEE Transactions on Signal Processing, 2018, 66(16): 4160-4169.
[7] TSENG F H, CHAO H, WANG J. Ultra-dense small cell planning using cognitive radio network toward 5G [J]. IEEE Wireless Communications, 2015, 22(6): 76-83.
[8] WU Q, ZHANG G, NG D W K, et al. Generalized wireless-powered communications: When to activate wireless power transfer? [J]. IEEE Transactions on Vehicular Technology, 2019, 68(8): 8243-8248.
[9] ZHANG G, XU J, WU Q, et al. Wireless powered cooperative jamming for secure OFDM system [J]. IEEE Transactions on Vehicular Technology, 2017, 67(2): 1331-1346.
[10] ZHANG R, HO C K. MIMO broadcasting for simultaneous wireless information and power transfer [J]. IEEE Transactions on Wireless Communications, 2013, 12(5): 1989-2001.
[11] LEE S, ZHANG R, HUANG K. Opportunistic wireless energy harvesting in cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2013, 12(9): 4788-4799.
[12] YIN S, ZHANG E, QU Z, et al. Optimal cooperation strategy in cognitive radio systems with energy harvesting [J]. IEEE Transactions on Wireless Communications, 2014, 13(9): 4693-4707.
[13] ZHAI C, LIU J, LAN P, et al. Strive for the spectrum sharing by transferring energy and relaying data for the primary user [J]. Transactions on Emerging Telecommunications Technologies, 2017, 28(3): e2966.
[14] LU X, XU W, LI S, et al. Simultaneous information and power transfer for relay-assisted cognitive radio networks[C]//2014 IEEE International Conference on Communications Workshops. Sydney: IEEE, 2014: 331-336.
[15] WU Q, ZHANG R. Weighted sum power maximization for intelligent reflecting surface aided SWIPT [J]. IEEE Wireless Communications Letters, 2019, 9(5): 586-590.
[16] YUAN J, LIANG Y C, JOUNG J, et al. Intelligent reflecting surface (IRS) -enhanced cognitive radio system[C]//ICC 2020-2020 IEEE International Conference on Communications. Dublin: IEEE, 2020: 1-6.
[17] XU D, YU X, SCHOBER R. Resource allocation for intelligent reflecting surface-assisted cognitive radio networks[C]//2020 IEEE 21st International Workshop on Signal in Wireless Communications (SPAWC) . Atlanta: IEEE, 2020: 1-5.
[18] NTOUGIAS K, KRIKIDIS I. Interference-Constrained IRS-aided SWIPT[C]//2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) . Lucca: IEEE, 2021: 116-120.
[19] CUI M, ZHANG G, ZHANG R. Secure wireless communication via intelligent reflecting surface [J]. IEEE Wireless Communications Letters, 2019, 8(5): 1410-1414.
[20] ZHANG L, WANG Y, TAO W, et al. Intelligent reflecting surface aided MIMO cognitive radio systems [J]. IEEE Transactions on Vehicular Technology, 2020, 69(10): 11445-11457.
[21] ZHONG C, CUI M, ZHANG G, et al. Deep reinforcement learning-based optimization for IRS-assisted cognitive radio systems [J]. IEEE Transactions on Communications, 2022, 70(6): 3849-3864.
[22] ZHANG D, WU Q, CUI M, et al. Throughput maximization for IRS-assisted wireless powered hybrid NOMA and TDMA [J]. IEEE Wireless Communications Letters, 2021, 10(9): 1944-1948.
[23] WU Q, ZHANG R. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming [J]. IEEE Transactions on Wireless Communications, 2019, 18(11): 5394-5409.
[24] LUO Z Q, 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.
[25] YYANG Y, ZHENG B, ZHANG S, et al. Intelligent reflecting surface meets OFDM: Protocol design and rate maximization [J]. IEEE Transactions on Communications, 2020, 68(7): 4522-4535.
[26] XU D, YU X, SUN Y, et al. Resource allocation for IRS-assisted full-duplex cognitive radio systems [J]. IEEE Transactions on Communications, 2020, 68(12): 7376-7394.
[1] 易雅倩, 吴庆捷, 崔苗, 张广驰. 具有URLLC任务卸载的无人机辅助移动边缘计算系统的计算时延最小化研究[J]. 广东工业大学学报, 0, (): 10-.
[2] 曾文坦, 叶龙建, 翟雄飞, 韩国军. 基于单调排序与并行选择的连续删除堆栈译码器的硬件实现[J]. 广东工业大学学报, 2024, 41(01): 101-109.
[3] 李宇龙, 梁静轩, 王丰. 移动边缘计算系统的双服务器协同与计算通信资源联合优化[J]. 广东工业大学学报, 2024, 41(01): 93-100.
[4] 苏天赐, 何梓楠, 崔苗, 张广驰. 多无人机辅助数据收集系统的智能路径规划算法[J]. 广东工业大学学报, 2023, 40(04): 77-84.
[5] 何一汕, 王永华, 万频, 王磊, 伍文韬. 面向多用户动态频谱接入的改进双深度Q网络方法研究[J]. 广东工业大学学报, 2023, 40(04): 85-93.
[6] 吴庆捷, 崔苗, 张广驰, 陈伟. 无人机信息采集系统的端到端吞吐量最大化研究[J]. 广东工业大学学报, 2022, 39(06): 53-61.
[7] 王丰, 李宇龙, 林志飞, 崔苗, 张广驰. 基于计算吞吐量最大化的能量采集边缘计算系统在线资源优化配置[J]. 广东工业大学学报, 2022, 39(04): 17-23.
[8] 吴家锐, 崔苗, 张广驰, 王丰. 可重构智能表面辅助的非正交多址接入系统的安全通信研究[J]. 广东工业大学学报, 2022, 39(03): 49-54,69.
[9] 蒋丽, 谢胜利, 张彦. 面向6G网络的联邦学习资源协作激励机制设计[J]. 广东工业大学学报, 2021, 38(06): 47-52,83.
[10] 郑思远, 崔苗, 张广驰. 基于强化学习的无人机安全通信轨迹在线优化策略[J]. 广东工业大学学报, 2021, 38(04): 59-64.
[11] 谭艺枝, 陈宝仁. 无线携能传输协同中继非正交多址接入系统的速率优化设计[J]. 广东工业大学学报, 2020, 37(01): 81-86.
[12] 崔苗, 庞浩然, 张广驰, 刘怡俊, 邹为民. 基于无线供电的非正交多址接入网络的鲁棒资源分配研究[J]. 广东工业大学学报, 2018, 35(06): 37-42.
[13] 童辉志, 张广驰, 周绪龙, 崔苗, 刘怡俊, 林凡. 具有能量获取基站的相邻多蜂窝小区的能量与频谱分配研究[J]. 广东工业大学学报, 2018, 35(04): 68-74.
[14] 蒋阅, 刘海林, 王强. 基于基站休眠技术的无线通信网络能源协作策略[J]. 广东工业大学学报, 2018, 35(02): 69-74.
[15] 何丽君, 张广驰, 黄高飞, 万林青, 崔苗, 刘怡俊, 林凡. 无线携能OFDM中继系统的联合资源分配研究[J]. 广东工业大学学报, 2018, 35(01): 29-34.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发