In this letter, the outage performance of a two-user downlink non-orthogonal multiple access (NOMA) time-varying network without any knowledge on the channel state or distribution at the base station (BS) is investigated. Building on reinforcement learning techniques and, more precisely, on multi-armed bandits (MAB), we propose a novel adaptive NOMA scheme that optimally tunes which user should perform successive interference cancellation (SIC) jointly with the power allocation at the BS. Remarkably, our proposed scheme requires only a single bit (ACK-type) of feedback from each user and is still able to outperform OMA, as demonstrated by the numerical results in many settings of interest including stochastic and even non-stationary (adversarial) ones.

中文翻译：

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无 CSIT/CDIT 的时变无线网络中依赖 1 位反馈的自适应 NOMA


在这封信中，研究了在不了解基站（BS）的信道状态或分布的情况下两用户下行链路非正交多址（NOMA）时变网络的中断性能。基于强化学习技术，更准确地说，基于多臂老虎机 (MAB)，我们提出了一种新颖的自适应 NOMA 方案，该方案可以优化调整哪个用户应与 BS 处的功率分配联合执行连续干扰消除 (SIC)。值得注意的是，我们提出的方案只需要来自每个用户的单个比特（ACK 类型）反馈，并且仍然能够优于 OMA，正如许多感兴趣的设置（包括随机甚至非平稳（对抗性）设置）中的数值结果所证明的那样。