Modern communication systems need to fulfill multiple and often conflicting objectives at the same time. In particular, new applications require high reliability while operating at low transmit powers. Moreover, reliability constraints may vary over time depending on the current state of the system. One solution to address this problem is to use joint transmissions from a number of base stations (BSs) to meet the reliability requirements. However, this approach is inefficient when considering the overall total transmit power. In this work, we propose a reinforcement learning-based power allocation scheme for an unmanned aerial vehicle (UAV) communication system with varying communication reliability requirements. In particular, the proposed scheme aims to minimize the total transmit power of all BSs while achieving an outage probability that is less than a tolerated threshold. This threshold varies over time, e.g., when the UAV enters a critical zone with high-reliability requirements. Our results show that the proposed learning scheme uses dynamic power allocation to meet varying reliability requirements, thus effectively conserving energy.

翻译：现代通信系统需要同时满足多重且往往相互冲突的目标。具体而言，新型应用要求在高可靠性与低发射功率下运行。此外，可靠性约束可能随系统当前状态随时间变化。解决该问题的一种方案是采用多基站联合传输以满足可靠性需求，但该方法在考虑总发射功率时效率较低。本文针对具有时变通信可靠性需求的无人机通信系统，提出一种基于强化学习的功率分配方案。该方案旨在最小化所有基站的总发射功率，同时保证中断概率低于可容忍阈值。该阈值会动态变化，例如当无人机进入高可靠性要求的临界区域时。实验结果表明，所提学习方案通过动态功率分配满足时变可靠性需求，从而有效节省能量。