A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO
A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO
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DOI：
10.1109/jsac.2023.3336154
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发表时间：
2023-01
影响因子：
16.4
通讯作者：
M. Oh;A. Das;Seyyedali Hosseinalipour;Taejoon Kim;D. Love;Christopher G. Brinton
中科院分区：
文献类型：
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作者：
M. Oh;A. Das;Seyyedali Hosseinalipour;Taejoon Kim;D. Love;Christopher G. Brinton
单片架构中的无线电接入网络（RAN）对于支持不同的网络场景具有有限的适应性。最近，开放RAN（O-RAN）技术已经开始为RAN实现增加巨大的灵活性。O-RAN是无小区大规模多输入多输出（CFmMIMO）系统的自然架构，其中采用许多地理上分布的接入点（AP）来实现无处不在的覆盖和增强的用户性能。在本文中，我们解决了分散式导频分配（PA）问题的可扩展O-RAN为基础的CFmMIMO系统。我们提出了一种低复杂度的PA方案，使用多代理深度强化学习（MA-DRL）框架，其中多个学习代理在O-RAN通信架构上执行分布式学习以抑制导频污染。我们的方法不需要事先的信道知识，而是依赖于在学习过程中与环境进行的实时交互。此外，我们设计了一个码本搜索（CS）计划，利用我们的O-RAN CFmMIMO架构的分散，其中不同的码本集可以用来进一步提高PA性能，而没有任何显着的额外的复杂性。数值评估验证，我们提出的方案提供了大量的计算可扩展性的优势和改善信道估计性能相比，国家的最先进的。
Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios. Recently, open-RAN (O-RAN) techniques have begun adding enormous flexibility to RAN implementations. O-RAN is a natural architectural fit for cell-free massive multiple-input multiple-output (CFmMIMO) systems, where many geographically-distributed access points (APs) are employed to achieve ubiquitous coverage and enhanced user performance. In this paper, we address the decentralized pilot assignment (PA) problem for scalable O-RAN-based CFmMIMO systems. We propose a low-complexity PA scheme using a multi-agent deep reinforcement learning (MA-DRL) framework in which multiple learning agents perform distributed learning over the O-RAN communication architecture to suppress pilot contamination. Our approach does not require prior channel knowledge but instead relies on real-time interactions made with the environment during the learning procedure. In addition, we design a codebook search (CS) scheme that exploits the decentralization of our O-RAN CFmMIMO architecture, where different codebook sets can be utilized to further improve PA performance without any significant additional complexities. Numerical evaluations verify that our proposed scheme provides substantial computational scalability advantages and improvements in channel estimation performance compared to the state-of-the-art.