Joint waveform and phase shift design in integrated sensing and communication systems

doi:10.3785/j.issn.1008-973X.2026.04.022

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (4): 906-914 DOI: 10.3785/j.issn.1008-973X.2026.04.022

Joint waveform and phase shift design in integrated sensing and communication systems

Qingqing YANG1,2(

),Runpeng TANG1,2,Yi PENG1,2,*(

)

1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
2. Yunnan Provincial Key Laboratory of Computer Science, Kunming University of Science and Technology, Kunming 650500, China

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Abstract

An irregular topology of reconfigurable intelligent surface (RIS) elements, combined with deep reinforcement learning (DRL) algorithms, was proposed to enhance the capacity of integrated sensing and communication (ISAC) systems. A simulated annealing algorithm was employed to solve the topological structure optimization problem of irregular RIS, ensuring optimal spatial utilization efficiency under a limited number of elements. Under the constraint of sensing beam pattern gain, multi-user interference (MUI) was minimized by two approaches. The first combined the Adam optimizer with traditional gradient descent. The second relied on DRL, where discrete RIS phase shifts and constant-modulus ISAC waveform design were managed by deep Q-network (DQN) and proximal policy optimization (PPO), respectively. Simulation results indicated that the weighted sum rate (WSR) of the irregular RIS-assisted system optimized by DRL increased by 13.3% compared with the conventional RIS scheme. The DRL algorithm also showed stronger capability in suppressing constant-modulus beam energy leakage. These results confirmed the feasibility of jointly optimizing irregular RIS topology and DRL algorithms in integrated sensing and communication systems.

Key words： integrated sensing and communication (ISAC) irregular reconfigurable intelligent surface (RIS) joint waveform design phase shift matrix deep reinforcement learning (DRL)

Received: 14 May 2025 Published: 19 March 2026

CLC:

TN 929.5

Fund: 国家自然科学基金资助项目 (62461030)；云南省基础研究重点项目 (202401AS070105).

Corresponding Authors: Yi PENG E-mail: 20090119@kust.edu.cn;12309214@kust.edu.cn

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Cite this article:

Qingqing YANG,Runpeng TANG,Yi PENG. Joint waveform and phase shift design in integrated sensing and communication systems. Journal of ZheJiang University (Engineering Science), 2026, 60(4): 906-914.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.04.022 OR https://www.zjujournals.com/eng/Y2026/V60/I4/906

通信感知一体化系统中的联合波形与相移设计

针对如何在可重构智能表面(RIS)辅助通信感知一体化(ISAC)系统中有效提升系统容量的问题，提出RIS单元的不规则拓扑结构以及深度强化学习(DRL)算法. 采用模拟退火算法用于解决不规则RIS的拓扑优化问题，以提高在有限元件数量下的最优空间利用效率. 在感知波束图增益约束下，分别采用Adam优化器结合传统的梯度下降法与基于DRL的方法来解决最小化用户间干扰(MUI)的问题. 具体而言，DRL方案通过深度Q网络(DQN)与近端策略优化(PPO)这2种算法分别处理RIS的离散相移控制和ISAC的恒模波形设计. 仿真结果表明，基于DRL算法的不规则RIS辅助通感一体化系统的加权和速率(WSR)相较传统RIS方案提升了13.3%. DRL算法在抑制恒模波束能量泄漏方面具有更显著的优势，进一步验证了不规则RIS的拓扑设计和DRL算法在通感一体化系统中协同优化的可行性.

关键词： 通信感知一体化(ISAC), 不规则可重构智能表面(RIS), 联合波形设计, 相移矩阵, 深度强化学习(DRL)

Fig.1 Model of irregular RIS-assisted integrated sensing and communication system

Fig.2 Deep neural network (DNN)

Fig.3 Schematic diagram of deep reinforcement learning network architecture

Tab.1 Simulation parameter settings of irregular RIS-assisted integrated sensing and communication system

Fig.4 Weighted sum rate under different RIS topology deployment structures

Fig.5 Instantaneous reward curves for different batch sizes

Fig.6 Instantaneous reward curves under different transmission powers

Fig.7 Average reward curves under different base station transmission powers

Fig.8 Multi-user interference variation plot of proposed algorithm under different quantization levels

Fig.9 Graph of system weighted sum rate versus base station power

$ {P}_{\mathrm{t}} $

under different RIS-assisted scenarios using proposed algorithm

Fig.10 Graph of system sum rate versus base station signal-to-noise ratio under different RIS-assisted scenarios using proposed algorithm

Fig.11 Graph of system weighted sum rate versus RIS element number under different RIS assistance using proposed algorithm

Fig.12 Base station beam intensity maps under different schemes


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