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

doi:10.12052/gdutxb.230040

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

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

信息与通信技术 | 更新时间：2026-05-27

- 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
- Throughput Optimization for IRS-assisted Cognitive SWIPT Secondary User Networks
- 广东工业大学学报 2024年41卷第3期页码：119-130
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,510006
- 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
  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
- 基金信息：
  
  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)
- DOI：10.12052/gdutxb.230040
  中图分类号： TN929.5
- 纸质出版：2024
- 稿件说明：
移动端阅览
乐文英, 崔苗, 张广驰. 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[J]. 广东工业大学学报, 2024, 41(3): 119-130.

Le Wen-ying, Cui Miao, Zhang Guang-chi. Throughput Optimization for IRS-assisted Cognitive SWIPT Secondary User Networks[J]. 2024, 41(3): 119-130.
乐文英, 崔苗, 张广驰. 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[J]. 广东工业大学学报, 2024, 41(3): 119-130. DOI： 10.12052/gdutxb.230040.

Le Wen-ying, Cui Miao, Zhang Guang-chi. Throughput Optimization for IRS-assisted Cognitive SWIPT Secondary User Networks[J]. 2024, 41(3): 119-130. DOI： 10.12052/gdutxb.230040.

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。

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.

关键词

Keywords

references

WONG V W, SCHOBER R, NG D W K, et al. Key Technologies for 5G Wireless Systems [M]. Cambridge: Cambridge University Press, 2017.

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.

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.

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.

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.

TORLAK M, NAMGOONG W. Sub-nyquist sampling receiver for overlay cognitive radio users [J]. IEEE Transactions on Signal Processing, 2018, 66(16): 4160-4169.

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.

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.

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.

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.

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.

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.

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.

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.

WU Q, ZHANG R. Weighted sum power maximization for intelligent reflecting surface aided SWIPT [J]. IEEE Wireless Communications Letters, 2019, 9(5): 586-590.

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.

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.

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.

CUI M, ZHANG G, ZHANG R. Secure wireless communication via intelligent reflecting surface [J]. IEEE Wireless Communications Letters, 2019, 8(5): 1410-1414.

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.

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.

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.

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.

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.

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.

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.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于AAGV-YOLOX模型的输电线路异物检测研究

双模态迭代交叉注意力融合集成框架

储能变流器电能质量治理策略研究

人工噪声辅助无人机太赫兹安全通信的鲁棒设计

基于数字混频的脉冲式激光测距方法的研究与实现