[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127500-en":3,"doc-seo-127500-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127500,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging","Beamforming for downlink multi-user massive MIMO requires accurate transmitter-side channel state information (CSIT), yet perfect CSIT is hard to obtain under user mobility and CSI feedback/acquisition delays. The paper proposes a multi-agent deep reinforcement learning framework that jointly designs transmit and receive beamforming to maximize the average information rate across users under imperfect CSIT. It investigates interference management and compares distributed, partial-distributed, and central-learning schemes, showing strong robustness and outperforming random and delay-sensitive baselines while achieving over 90% of benchmark information rates with lower complexity.","arXiv :2302 .06853v1 [ ee ss . SP] 14 Feb 2023  \nDeep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging  \nZhenyuan Feng, Member, IEEE, Bruno Clerckx, Fellow, IEEE  \nAbstract  \nThe design of beamforming for downlink multi-user massive multi-input multi-output (MIMO) relies on accurate downlink channel state information (CSI) at the transmitter (CSIT) . In fact, it is dif􀀂cult for the base station (BS) to obtain perfect CSIT due to user mobility, latency/feedback delay (between downlink data transmission and CSI acquisition) . Hence, robust beamforming under imperfect CSIT is needed. In this paper, considering multiple antennas at all nodes (base station and user terminals), we develop a multi-agent deep reinforcement learning (DRL) framework for massive MIMO under imperfect CSIT, where the transmit and receive beamforming are jointly designed to maximize the average information rate of all users. Leveraging this DRL-based framework, interference management is explored and three DRL-based schemes, namely the distributed-learning-distributedprocessing scheme, partial-distributed-learning-distributed-processing, and central-learning-distributedprocessing scheme, are proposed and analyzed. This paper 1) highlights the fact that the DRL-based strategies outperform the random action-chosen strategy and the delay-sensitive strategy named as sample-and-hold (SAH) approach, and achieved over 90% of the information rate of two selected benchmarks with lower complexity: the zero-forcing channel-inversion (ZF-CI) with perfect CSIT and the Greedy Beam Selection strategy, 2) demonstrates the inherent robustness of the proposed designs in the presence of user mobility.  \nIndex Terms  \nDeep learning, interference management, massive MIMO, reinforcement learning, wireless communication  \nZ. Feng is with the Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K.(e-mail: [z.feng19@imperial.ac.uk](z.feng19@imperial.ac.uk)).B. Clerckx is with the Department of Electrical and Electronic Engineering at Imperial College London, London SW7 2AZ, UK and with Silicon Austria Labs (SAL), Graz A-8010, Austria (email: [b.clerckx@imperial.ac.uk](b.clerckx@imperial.ac.uk); [bruno.clerckx@silicon-austria.com](bruno.clerckx@silicon-austria.com))  \nFebruary 15, 2023 DRAFT  \nI. INTRODUCTION  \nDue to the increasing demand for data and connectivity in 􀀂fth-generation (5G) [1] and sixthgeneration (6G) [2], multi-antenna technologies have attracted great attention in academia and industry. The research on multi-antenna techniques has promoted the development of MIMO technology. MIMO nowadays plays an indispensable role in the physical layer, media access control (MAC) layer and network layer in wireless communications and networking [3] . At the physical layer, multi-antenna beamforming strategies have attracted interest due to their ability to achieve considerable antenna gains, multiplexing gains, and diversity gains in wireless MIMO transmission [4], [5] . To enable a high throughput in the massive MIMO system, the base station (BS) relies on the huge demand for the global and instantaneous channel state information (CSI) . Nevertheless, the ground/air/space platforms such as high-speed trains/unmanned aerial vehicles (UAV)/satellites have a common characteristic of 3D mobility which leads to a stringent time constraint on CSI acquisition and even causes misalignment of narrow beams. Therefore, in the future communication systems, how to maintain good connectivity and system capacity without perfect CSIT (so-called imperfect CSIT) is regarded as an important problem that yearns for prompt solutions.  \nThe imperfect CSIT is usually caused by the drastic change of the propagation environment due to user mobility [6] and CSI feedback/acquisition delay between the base station (BS) and users [7] . The CSI feedback or acquisition delay is the time gap between the time point when the channel is estimated and","cbCailfpFLcxb1iW","https://ap.wps.com/l/cbCailfpFLcxb1iW","pdf",2931619,1,30,"English","en",105,"# Introduction\n## Challenges of imperfect CSIT\n## Related approaches\n## Machine learning and deep reinforcement learning","[{\"question\":\"Why is imperfect CSIT important in multi-user massive MIMO?\",\"answer\":\"Imperfect CSIT arises from user mobility and CSI feedback/acquisition delays, which make estimated channels outdated at the time of downlink transmission, degrading massive MIMO performance.\"},{\"question\":\"What is the main contribution of the proposed method?\",\"answer\":\"It introduces a multi-agent DRL framework that jointly designs transmit and receive beamforming using imperfect CSIT to maximize the average information rate of all users.\"},{\"question\":\"How does the paper validate the DRL schemes?\",\"answer\":\"It analyzes interference management and compares distributed-learning, partial-distributed-learning, and central-learning strategies, showing they outperform random and the delay-sensitive sample-and-hold (SAH) approach and reach over 90% of benchmark rates with lower complexity.\"}]","Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging | 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is imperfect CSIT important in multi-user massive MIMO?","Question",{"text":76,"@type":77},"Imperfect CSIT arises from user mobility and CSI feedback/acquisition delays, which make estimated channels outdated at the time of downlink transmission, degrading massive MIMO performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is the main contribution of the proposed method?",{"text":81,"@type":77},"It introduces a multi-agent DRL framework that jointly designs transmit and receive beamforming using imperfect CSIT to maximize the average information rate of all users.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the paper validate the DRL schemes?",{"text":85,"@type":77},"It analyzes interference management and compares distributed-learning, partial-distributed-learning, and central-learning strategies, showing they outperform random and the delay-sensitive sample-and-hold (SAH) approach and reach over 90% of benchmark rates with lower 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