Adaptive beamforming and user association in heterogeneous cloud radio access networks: A mobility-aware performance-cost trade-off
Adaptive beamforming and user association in heterogeneous cloud radio access networks: A mobility-aware performance-cost trade-off
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DOI：
10.1016/j.comnet.2019.05.005
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
D. Ha;L. Boukhatem;Megumi Kaneko;Nhan Nguyen-Thanh;Steven Martin
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文献类型：
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作者：
D. Ha;L. Boukhatem;Megumi Kaneko;Nhan Nguyen-Thanh;Steven Martin
异构云无线接入网络（H-CRAN）是未来5G移动通信系统一种有前景的网络架构，可满足日益增长的移动数据流量需求。在这项工作中，我们考虑在 H-CRAN 下行链路中设计高效的联合波束成形和用户聚类（用户到远程无线电头端 (RRH) 关联），其中用户具有不同的移动性配置文件。考虑到这种无线环境随时间快速变化的特性，在不产生大量信道状态信息（CSI）和信令开销的情况下实现优化的波束成形和用户聚类变得非常具有挑战性。这项工作的主要目标是调查和评估系统吞吐量与复杂性和信令开销方面产生的成本之间的权衡，包括给定不同用户移动性配置文件的不同 CSI 反馈策略的影响。我们提出了自适应波束成形和用户聚类（ABUC）算法，该算法根据用户移动性调整其反馈参数，即动态用户聚类的周期和CSI反馈的类型。此外，我们设计了一个强化学习框架，使所提出的 ABUC 算法能够在给定每个用户移动性配置文件的情况下动态优化其调度参数。基于计算机模拟，分析了移动性对系统性能指标的影响，并就该算法针对不同移动场景进行适当的参数调整得出了结论。1
Heterogeneous Cloud Radio Access Network (H-CRAN) is a promising network architecture for the future 5G mobile communication system to address the increasing demand for mobile data traffic. In this work, we consider the design of efficient joint beamforming and user clustering (user-to-Remote Radio Head (RRH) association) in the downlink of a H-CRAN where users have different mobility profiles. Given the rapidly time-varying nature of such wireless environment, it becomes very challenging to enable optimized beamforming and user clustering without incurring large Channel State Information (CSI) and signaling overheads. The main objective of this work is to investigate and evaluate the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of different CSI feedback strategies given different user mobility profiles. We propose the Adaptive Beamforming and User Clustering (ABUC) algorithm which adapts its feedback parameters, namely the period of dynamic user clustering and the type of CSI feedback, in function of user mobility. Furthermore, we design a reinforcement-learning framework which enables the proposed ABUC algorithm to optimize its scheduling parameters on-the-fly, given each user mobility profile. Based on computer simulations, an analysis of the effect of mobility on system performance metrics is presented and conclusions are drawn regarding the algorithm’s adequate parameter tuning for different mobility scenarios.1