Optimizing Wireless Discontinuous Reception via MAC Signaling Learning

TL;DR

This paper introduces a reinforcement learning method to optimize DRX policies in cellular networks by timing MAC signaling, achieving significant energy savings and latency improvements over traditional timer-based approaches.

Contribution

It proposes a novel RL-based approach to control MAC signaling for DRX, focusing on energy efficiency without tuning DRX timers, applicable to 5G and beyond networks.

Findings

Nearly 50% reduction in active time for single UE

20% active time reduction for 9 UEs

Improved latency-energy trade-off

Abstract

We present a Reinforcement Learning (RL) approach to the problem of controlling the Discontinuous Reception (DRX) policy from a Base Transceiver Station (BTS) in a cellular network. We do so by means of optimally timing the transmission of fast Layer-2 signaling messages (a.k.a. Medium Access Layer (MAC) Control Elements (CEs) as specified in 5G New Radio). Unlike more conventional approaches to DRX optimization, which rely on fine-tuning the values of DRX timers, we assess the gains that can be obtained solely by means of this MAC CE signalling. For the simulation part, we concentrate on traffic types typically encountered in Extended Reality (XR) applications, where the need for battery drain minimization and overheating mitigation are particularly pressing. Both 3GPP 5G New Radio (5G NR) compliant and non-compliant ("beyond 5G") MAC CEs are considered. Our simulation results show that our proposed technique strikes an improved trade-off between latency and energy savings as compared to conventional timer-based approaches that are characteristic of most current implementations. Specifically, our RL-based policy can nearly halve the active time for a single User Equipment (UE) with respect to a na\"ive MAC CE transmission policy, and still achieve near 20% active time reduction for 9 simultaneously served UEs.