Reinforcement Learning-based Interference Coordination for Distributed MU-MIMO
Reinforcement Learning-based Interference Coordination for Distributed MU-MIMO
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基于强化学习的分布式 MU-MIMO 干扰协调
DOI：
10.1109/wpmc52694.2021.9700445
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
F. Adachi
中科院分区：
文献类型：
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作者：
Chang Ge;Sijie Xia;Qiang Chen;F. Adachi
针对分布式多用户多输入多输出（MU-MIMO）系统的干扰协调问题，提出了一种基于启发式的图着色算法（GCA）。在本文中，沿着机器学习的最新进展，我们提出了一种基于强化学习（RL）的GCA分簇分布式MU-MIMO。计算机仿真结果表明，我们新提出的RL-GCA可以显着提高下行链路容量相比，其他非智能GCA。此外，一个有趣的结论已经获得的色数（所需的最小数量的颜色）。结果表明，较少的色数并不一定导致更好的干扰协调。在本文假设的传播环境下，最大化可达链路容量的最佳色数为4。
In our previous studies, we proposed a graph coloring algorithm (GCA) based on heuristics to solve the interference coordination problem for distributed multi-user multi-input multi-output (MU-MIMO). In this paper, along with the recent advances of machine learning, we propose a reinforcement learning (RL) based GCA for cluster-wise distributed MU-MIMO. The computer simulation confirms that our newly proposed RL-GCA can significantly improve the downlink link capacity compared with other non-intelligent GCAs. Also, an interesting conclusion has been obtained in terms of chromatic number (required minimum number of colors). It is shown that the less chromatic number does not necessarily lead to a better interference coordination. Under the propagation environment assumed in this paper, the best chromatic number which maximizes the achievable link capacity is shown to be 4.