This work demonstrates the potential of deep reinforcement learning
techniques for transmit power control in wireless networks. Existing techniques
typically find near-optimal power allocations by solving a challenging
optimization problem. Most of these algorithms are not scalable to large
networks in real-world scenarios because of their computational complexity and
instantaneous cross-cell channel state information (CSI) requirement. In this
paper, a distributively executed dynamic power allocation scheme is developed
based on model-free deep reinforcement learning. Each transmitter collects CSI
and quality of service (QoS) information from several neighbors and adapts its
own transmit power accordingly. The objective is to maximize a weighted
sum-rate utility function, which can be particularized to achieve maximum
sum-rate or proportionally fair scheduling. Both random variations and delays
in the CSI are inherently addressed using deep Q-learning. For a typical
network architecture, the proposed algorithm is shown to achieve near-optimal
power allocation in real time based on delayed CSI measurements available to
the agents. The proposed scheme is especially suitable for practical scenarios
where the system model is inaccurate and CSI delay is non-negligible.

本文提出了一种分布式执行的动态功率分配方案，基于无模型深度强化学习技术，通过收集 CSI 和服务质量（QoS）信息，每个发射机适应自己的发射功率，旨在最大化加权和速效用函数，可特化为实现最大总速率或比例公平调度。本方案特别适用于系统模型不精确，CSI 延迟不可忽略的实际场景，能够在真实时间内实现几乎最优的功率分配。