In TDD massive MISO systems, user equipments (UEs) send channel measurement pilots to the BS for beamforming. Frequently sending these pilots, although improving beamforming, could consume significant communication resources. In this letter, we investigate how frequent these pilots should be sent for each UE so as to increase overall throughput performance for TDD massive MISO downlink. This real-time resource allocation problem is challenging due to non-trivial performance metric and boundary conditions. Assuming that instantaneous speed and location information of UEs can be obtained by the BS, we propose a reinforcement learning framework in which the BS acts as a learning agent to decide pilot intervals. Simulation results show that, for this multi-terminal setting where UEs compete for resources, using this centralized reinforcement learning framework, performance can be improved by choosing pilot intervals and transmission rates based on the UE information.

中文翻译：

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具有用户信息的TDD大规模MISO试点策略：强化学习方法

在TDD大规模MISO系统中，用户设备（UE）将信道测量导频发送到BS以进行波束成形。尽管改善了波束成形，但经常发送这些导频可能会消耗大量的通信资源。在这封信中，我们调查了应该为每个UE发送这些导频的频率，以提高TDD大规模MISO下行链路的总体吞吐量性能。由于非平凡的性能指标和边界条件，这种实时资源分配问题具有挑战性。假设BS可以获取UE的瞬时速度和位置信息，我们提出了一种增强学习框架，其中BS充当学习代理来决定导频间隔。仿真结果表明，对于这种UE竞争资源的多终端设置，