[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82573-en":3,"doc-seo-82573-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82573,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","DRL-Based Joint Beamforming and Surface Shape Optimization for Flexible Intelligent Metasurface-Aided ISAC Systems","Integrated sensing and communication (ISAC) unifies high-precision sensing with wireless data transmission, targeting next-generation scenarios such as perception, localization, and tracking. This work designs an ISAC system enabled by flexible intelligent metasurfaces (FIM), minimizing the Cramér–Rao bound under quality-of-service (QoS) constraints. The optimization jointly tunes the beamforming matrix and the FIM surface shape, while enforcing transmit power and surface-shape feasibility. A deep deterministic policy gradient (DDPG) actor-critic approach with constraint-aware rewards addresses the non-convex formulation and improves sensing performance while maintaining communication quality, outperforming rigid-array baselines.","DRL-Based Joint Beamforming and Surface Shape Optimization for Flexible Intelligent Metasurface-Aided ISAC Systems  \nMaoyuan Wang, Qian Zhang, Graduate Student Member, IEEE, Jiancheng An, Senior Member, IEEE, Xuejun Cheng, Zheng Dong, Member, IEEE, and Deqiang Wang, Senior Member, IEEE  \narXiv :2607 .0095 1v 1 [ cs .IT] 1 Jul 2026  \nAbstract—Integrated sensing and communication (ISAC) unifies high-precision sensing and wireless data transmission. In this paper, we investigate the design of ISAC systems enabled by flexible intelligent metasurface (FIM) and aim to minimize the Cram´er–Rao bound (CRB) with quality of service (QoS) constraints using deep reinforcement learning (DRL). Specifically, we formulate the joint design of beamforming matrix and FIMs surface shape to reduce the CRB subject to transmit power, QoSand the FIMs surface shape constraints. However, the non-convex formulation makes optimization problem difficult to solve. To tackle this issue, we develop a deep deterministic policy gradient (DDPG) actor critic DRL scheme for the joint design, guided by a constraint aware reward to progressively improve sensing performance. Numerical results demonstrate that jointly optimizing the beamforming matrix and the FIMs surface shape substantially decreases CRB while ensuring communication quality compared with existing rigid arrays.  \nIndex Terms—Integrated sensing and communications, flexible intelligent metasurfaces, Cram´er–Rao bound, deep reinforcement learning.  \nI. INTRODUCTION  \nIN the future sixth generation (6G) era, wireless communi  \ncations technologies are expected to support a broad range of emerging vertical applications such as urban digital twins, smart factories, and autonomous vehicles that require both ultra-reliable, low-latency communication and high-fidelity sensing for perception, localization, and tracking [1] . To satisfy these requirements while alleviating spectrum scarcity and reducing system cost, communication systems evolve from supporting just communication to joint sensing and communications, where multi-antenna beamforming is a key enabler, as it focuses transmit energy and separating spatial channels to support joint sensing and communication. Integrated sensing and communications (ISAC) systems that exploit multiantenna processing emerge as an important direction that  \nThis research was supported in part by the Shandong Provincial Natural Science Foundation under Grant ZR2023LZH003 and the National Key R&D Program of China under Grant 2024YFF0727101 . The work of J. An was supported by the National Natural Science Foundation of China (NSFC) under Grant 62471096 . The scientific calculations in this paper have been done on the HPC Cloud Platform of Shandong University.  \nMaoyuan Wang, Xuejun Cheng, Zheng Dong, and Deqiang Wang are with the School of Information Science and Engineering, Shandong University, Qingdao 266237, China (e-mail: {maoyuanwang2024, chengxue[jun](jun}@mail.sdu.edu.cn)[}](jun}@mail.sdu.edu.cn)[@mail.sdu.edu.cn](jun}@mail.sdu.edu.cn); {zhengdong, wdq sdu}@sdu.edu.cn) .  \nQian Zhang is with the School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China, and with the School of Information Science and Engineering, Shandong University, Qingdao 266237, China (e-mail: [qianzhang2021@mail.sdu.edu.cn](qianzhang2021@mail.sdu.edu.cn)).  \nJ. An is with the School of Electronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China (e-mail: [jiancheng.an@uestc.edu.cn](jiancheng.an@uestc.edu.cn)).  \nCorresponding author: Zheng Dong.  \nunifies wireless communications and radar sensing for efficient spectrum and hardware utilization [2]–[6] .  \nAdditionally, metasurfaces play a crucial role in 6G networks, and a primary application is the deployment of reconfigurable intelligent surfaces (RISs) [7] . By adaptively changing the phase of incident electromagnetic waves, RIS can e","cbCaimIYrOskFzir","https://ap.wps.com/l/cbCaimIYrOskFzir","pdf",997324,3,1,5,"English","en",105,"# Introduction\n## Motivation for ISAC in 6G\n## Role of metasurfaces (RIS, STAR-RIS, SIM)\n## Motivation for flexible intelligent metasurfaces (FIM)","[{\"question\":\"What problem does the paper address in ISAC system design?\",\"answer\":\"The paper addresses designing an ISAC system using flexible intelligent metasurfaces while minimizing the Cramér–Rao bound under QoS and transmit power constraints.\"},{\"question\":\"Which variables are optimized jointly in the proposed approach?\",\"answer\":\"It jointly optimizes the beamforming matrix and the FIM surface shape to improve sensing accuracy while satisfying communication-quality requirements.\"},{\"question\":\"Why is deep reinforcement learning used, and what method is proposed?\",\"answer\":\"The formulation is non-convex and difficult to solve directly, 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