北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (5): 51-58.

• 论文 • 上一篇    下一篇

具有空间相干信道的5G物联网系统在反馈受限条件下的用户选择方法

王强1,朱晨鸣1,潘甦2,李子博3   

  1. 1. 中通服咨询设计研究院有限公司
    2. 南京邮电大学通信
    3. 南京邮电大学
  • 收稿日期:2023-09-18 修回日期:2023-10-30 出版日期:2024-10-28 发布日期:2024-11-10
  • 通讯作者: 潘甦 E-mail:supan@njupt.edu.cn
  • 基金资助:
    国家自然科学基金

User Selection Method for 5G IoT System with Spatially Correlated Channel under the Condition of Limited Feedback

  • Received:2023-09-18 Revised:2023-10-30 Online:2024-10-28 Published:2024-11-10
  • Contact: Su PAN E-mail:supan@njupt.edu.cn

摘要: 现有的用户调度算法大多以多输入多输出(Multiple Input Multiple Output,MIMO)信道不相干为背景进行研究,而实际通信中信道之间可能存在相干性。同时在多用户MIMO系统中为了缓解开销,用户端只需向基站端反馈部分信道状态信息,因此不可避免带来多用户干扰。本文分析了5G物联网信道相干条件对用户容量上限和用户速率的影响,然后根据有限反馈带来的残留干扰推导得到低复杂度的用户速率表达式。针对信道相干条件下的多用户MIMO有限反馈系统,本文提出了一种基于强化学习的用户选择方法。所提的选择方法可以避免在每个周期内重复计算可达速率,因此大幅降低了计算复杂度。当系统处于信道相干环境下时,本文算法提高了吞吐量。

关键词: 多用户MIMO, 有限反馈, 信道相干, 强化学习, 用户选择

Abstract: Most of the existing user scheduling algorithms are based on the incoherence of Multiple Input Multiple Output (MIMO) channels, while there may be coherence among channels in practical systems. Meanwhile, in the Multi-user MIMO (MU-MIMO) system, in order to reduce the overhead of the uplink channel, the user only needs to feedback part of Channel State Information (CSI) to the base station, so the multi-user interference is inevitable. This paper analyzes the influence of the coherence conditions of MIMO channels on the upper limit of user capacity and transmission rates, and then deduces the low-complexity transmission rate based on the residual interference caused by limited feedback. For MU-MIMO limited feedback systems with channel coherence, a user selection method based on reinforcement learning is proposed. The proposed selection method can avoid recalculating the achievable rate in each cycle, thus greatly reducing the computational complexity. When the system is in the coherent channel environment, the proposed algorithm improves the throughput.

Key words: Multi-user MIMO, Limited feedback, Channel coherence, Reinforcement learning, User selection

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