双主动 RIS 辅助雷达-通信共存系统的联合波束成形设计

IF 7.4 1区 计算机科学 Q1 TELECOMMUNICATIONS IEEE Transactions on Cognitive Communications and Networking Pub Date : 2024-08-05 DOI:10.1109/TCCN.2024.3438350
Mengyu Liu;Hong Ren;Cunhua Pan;Boshi Wang;Zhiyuan Yu;Ruisong Weng;Kangda Zhi;Yongchao He
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引用次数: 0

摘要

综合传感与通信(ISAC)技术已被视为下一代无线通信系统的关键候选技术之一。然而,当雷达和通信设备共存于同一系统(即雷达-通信共存(RCC))时,通信系统对雷达的干扰可能很大,不容忽视。最近,可重构智能表面(RIS)被引入 RCC 系统,以减少干扰。然而,无源 RIS 带来的 "乘法衰落 "效应限制了其性能。为了解决这个问题,我们考虑了一种双主动 RIS 辅助 RCC 系统,重点是设计雷达的波束成形矢量和主动 RIS 的反射系数矩阵,以最大限度地提高通信系统的可实现数据速率。所考虑的系统需要满足雷达探测约束以及雷达和 RIS 的功率预算。由于问题是非凸的,我们提出了一种基于惩罚性对偶分解(PDD)框架的算法。具体来说,我们首先引入辅助变量,将耦合变量重新表述为等式约束,并通过 PDD 框架将这些约束纳入目标函数。然后,我们通过调用块坐标下降(BCD)方法将等效问题解耦为多个子问题。此外,我们还采用拉格朗日对偶法交替优化这些子问题。仿真结果验证了所提算法的有效性。此外,结果还表明,在相同的功率预算下,在 RCC 系统中部署双主动 RIS 比部署单主动 RIS 和双被动 RIS 能获得更高的数据速率。
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Joint Beamforming Design for Double Active RIS-Assisted Radar-Communication Coexistence Systems
Integrated sensing and communication (ISAC) technology has been considered as one of the key candidate technologies in the next-generation wireless communication systems. However, when radar and communication equipment coexist in the same system, i.e., radar-communication coexistence (RCC), the interference from communication systems to radar can be large and cannot be ignored. Recently, reconfigurable intelligent surface (RIS) has been introduced into RCC systems to reduce the interference. However, the “multiplicative fading” effect introduced by passive RIS limits its performance. To tackle this issue, we consider a double active RIS-assisted RCC system, which focuses on the design of the radar’s beamforming vector and the active RISs’ reflecting coefficient matrices, to maximize the achievable data rate of the communication system. The considered system needs to meet the radar detection constraint and the power budgets at the radar and the RISs. Since the problem is non-convex, we propose an algorithm based on the penalty dual decomposition (PDD) framework. Specifically, we initially introduce auxiliary variables to reformulate the coupled variables into equation constraints and incorporate these constraints into the objective function through the PDD framework. Then, we decouple the equivalent problem into several subproblems by invoking the block coordinate descent (BCD) method. Furthermore, we employ the Lagrange dual method to alternately optimize these subproblems. Simulation results verify the effectiveness of the proposed algorithm. Furthermore, the results also show that under the same power budget, deploying double active RISs in RCC systems can achieve higher data rate than those with single active RIS and double passive RISs.
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来源期刊
IEEE Transactions on Cognitive Communications and Networking
IEEE Transactions on Cognitive Communications and Networking Computer Science-Artificial Intelligence
CiteScore
15.50
自引率
7.00%
发文量
108
期刊介绍: The IEEE Transactions on Cognitive Communications and Networking (TCCN) aims to publish high-quality manuscripts that push the boundaries of cognitive communications and networking research. Cognitive, in this context, refers to the application of perception, learning, reasoning, memory, and adaptive approaches in communication system design. The transactions welcome submissions that explore various aspects of cognitive communications and networks, focusing on innovative and holistic approaches to complex system design. Key topics covered include architecture, protocols, cross-layer design, and cognition cycle design for cognitive networks. Additionally, research on machine learning, artificial intelligence, end-to-end and distributed intelligence, software-defined networking, cognitive radios, spectrum sharing, and security and privacy issues in cognitive networks are of interest. The publication also encourages papers addressing novel services and applications enabled by these cognitive concepts.
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