混合智能反射面辅助的通信感知一体化：高能效波束成形设计

褚宏云; 杨梦瑶; 黄航; 郑凌; 潘雪; 肖戈

doi:10.11999/JEIT230699

混合智能反射面辅助的通信感知一体化：高能效波束成形设计

doi: 10.11999/JEIT230699

褚宏云¹,
杨梦瑶^1, ,,
黄航²,
郑凌¹,
潘雪¹,
肖戈¹

1.
西安邮电大学西安 710121
2.
南京电子设备研究所南京 210013

基金项目: 国家自然科学基金(62102314)，173计划技术领域基金(2022-JCJQ-JJ-0730)，陕西省自然科学基金(2022JQ-635)

详细信息

作者简介:
褚宏云：女，讲师，硕士生导师，研究方向为智能超表面使能无线通信系统关键技术

杨梦瑶：女，硕士生，研究方向为通信感知一体化

黄航：男，高级工程师，研究方向为电子对抗

郑凌：男，讲师，硕士生导师，研究方向为下一代网络体系架构、高性能网络与交换、人工智能算法及其FPGA实现

潘雪：女，硕士生，研究方向为智能超表面信道估计

肖戈：男，硕士生，研究方向为智能超表面波束形成

通讯作者:
杨梦瑶　myyang1@yeah.net

中图分类号: TN929.5
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出版历程
- 收稿日期: 2023-07-12
- 修回日期: 2023-11-14
- 录用日期: 2023-11-14
- 网络出版日期: 2023-11-21
- 刊出日期: 2024-06-30

Hybrid Reconfigurable Intelligent Surface Assisted Integrated Sensing and Communication: Energy Efficient Beamforming Design

1.
Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Nanjing Research Institute of Electronic Equipment, Nanjing 210013, China

Funds: The National Natural Science Foundation of China (62102314), The 173 Program for Technology (2022-JCJQ-JJ-0730), The Natural Science Foundation of Shaanxi Province (2022JQ-635)

摘要

摘要: 能量效率(EE)是5G+/6G无线通信的重要设计指标，而智能反射面(RIS)被普遍认为是改善EE的潜在手段。不同于被动RIS，混合RIS由有源和无源元件组成，对来波移相的同时可放大信号强度，能够有效克服被动RIS引起的“乘性衰落”效应。鉴于此，该文提出一种混合RIS辅助通信感知一体化(ISAC)的下行链路传输系统。为探究数据传输速率与能耗之间的内在关联，该文以RIS辅助ISAC网络能量效率最大化为目标，在满足基站(BS)发射功率、波束图增益以及混合RIS功率和幅值约束的条件下，联合优化基站端的波束赋形和混合RIS的相移。为解决该复杂的分数规划问题，提出基于交替优化(AO)的算法来求解。为克服AO算法中引入辅助变量造成算法复杂度高的难题，利用耦合优化变量的关联，提出一种基于级联深度学习网络的求解算法。仿真结果表明，提出的混合RIS辅助ISAC方案在和速率、能效方面皆优于现有方案，且算法收敛速度快。
- 通信感知一体化 /
- 能量效率最大化 /
- 混合可重构智能超表面 /
- 联合波束赋形 /
- 级联深度学习
Abstract: Energy Efficiency (EE) is an important design metric for 5G+/6G wireless communications, and Reconfigurable Intelligent Surface (RIS) is widely recognized as a potential means to improve EE. Unlike passive RIS, hybrid RIS consists of both active and passive components, which can amplify the signal strength while phase-shifting the incoming wave, and can effectively overcome the “multiplicative fading” effect caused by fully passive RIS. In view of this, a hybrid RIS-assisted Integrated Sensing and Communication (ISAC) downlink transmission system is proposed in this paper. In order to investigate the intrinsic correlation between data transmission capacity and energy consumption, the paper jointly optimizes the beamforming and phase-shifting of hybrid RIS at the Base Station (BS) under the constraints of BS transmit power, beampattern gain, and hybrid RIS power and amplitude with the goal of maximizing the global EE in a multiuser network. To solve this complex fractional programming problem, an algorithm based on Alternating Optimization (AO) is proposed to solve it. To overcome the problem of high algorithm complexity caused by the introduction of auxiliary variables in the AO algorithm, a solution algorithm based on a cascaded deep learning network is proposed using the association of coupled optimization variables. Simulation results show that the proposed hybrid RIS-assisted ISAC scheme outperforms existing schemes in terms of sum rate and EE, and the algorithm converges quickly.
- Integrated Sensing and Communication (ISAC) /
- Maximizing energy efficiency /
- Hybrid reconfigurable intelligent surfaces /
- Joint beamforming /
- Cascade deep learning

HTML全文

图 1 混合RIS辅助的ISAC系统模型

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图 2 两阶段波束赋形网络结构图

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图 3 批量大小对损失的影响

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图 4 不同基站发射功率下的能量效率

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图 5 不同基站发射功率下的用户和速率

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图 6 不同RIS元件数目下的能量效率

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图 7 波束图

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算法1　算法整体流程
初始化：变量$ {b^{(0)}} $、$ {\xi ^{(0)}} $、$ {{\boldsymbol{W}}^{(0)}} $和$ {{\boldsymbol{\theta }}^{(0)}} $
迭代次数$ i = 1 $，最大迭代次数$ {I_{\max }} $
(1)While$ {f_i} - {f_{i - 1}} \ge \varepsilon $或$ i \lt {I_{\max }} $do
(2)根据式(11)更新拉格朗日对偶重组辅助变量$ {\xi ^{(i)}} $
(3)根据式(13)更新二次变换辅助变量$ {b^{(i)}} $
(4)利用CVX求解凸规划问题$ {{\text{P}}_{{\text{2-1}}}} $，更新优化变量$ {{\boldsymbol{W}}^{(i)}} $
(5)利用CVX求解凸规划问题$ {{\text{P}}_{{\text{3}}}} $，更新优化变量$ {{\boldsymbol{\varphi}} ^{(i)}} $
(6)更新能量效率$ \eta $，迭代次数$ i = i + 1 $
(7)End While

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表 1 部分仿真参数

参数名称	符号	数值	参数名称	符号	数值
BS发射天线数	$M$	8	用户噪声功率(dBm)	$\sigma _0^2$	–80
用户数	$K$	4	RIS噪声功率(dBm)	$\sigma _c^2$	–70
目标数	$L$	2	Rice因子	$ \rho $	10
RIS元件总数	$N$	256	最小波束图增益(dB)	$ \varGamma $	10
RIS主动元件数	$T$	64	目标方向($ ^\circ $)	$ {\theta _1},{\theta _2} $	$ \pm 45 $
RIS最大消耗功率(dBm)	${P_0}$	10	能量放大系数	${{a,b}}$	0.8
RIS有源元件放大系数(dB)	$\alpha $	10	惩罚系数	$ {\beta _1},{\beta _2},{\beta _3} $	50

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