Deep Reinforcement Learning Based Adaptive Modulation With Outdated CSI

Abstract

The problem of adaptive modulation with outdated channel state information (CSI) is considered. Best existing approach to tackle this problem relies on using a (non-)linear auto-regressive moving average (ARMA) model to predict current CSI from outdated values. This approach is valid only if the wireless channel variations over time behave in a linear or smooth enough nonlinear fashion, which is not necessarily the case. We propose a deep reinforcement learning based adaptive modulation (DRL-AM) approach that can handle this limitation. While DRL-AM is more complex than (non-)linear AR(MA), it performs significantly better as corroborated via numerical results on real channel measurements. Furthermore, compared to capacity-achieving codes, complexity is moved from receiver to transmitter making this approach suitable for receiving nodes with limited resources such as internet of things (IoT) devices.