Optimum Power Allocation for Low Rank Wi-Fi Channels: A Comparison with Deep RL Framework

TL;DR

This paper develops an optimal power allocation algorithm for low-rank Wi-Fi channels, comparing a Lagrangian optimization method with a deep reinforcement learning approach, to enhance data rates and efficiency in AR/VR systems.

Contribution

It introduces a novel near-optimal DRL-based power allocation method for low-rank channels, significantly improving real-time performance over traditional optimization techniques.

Findings

minPMAC outperforms NOMA and OMA by 28-39% in data rates

DRL-minPMAC runs 5 times faster than minPMAC

DRL-minPMAC achieves 83% of the global optimum data rates

Abstract

Upcoming Augmented Reality (AR) and Virtual Reality (VR) systems require high data rates ($\geq$ 500 Mbps) and low power consumption for seamless experience. With an increasing number of subscribing users, the total number of antennas across all transmitting users far exceeds the number of antennas at the access point (AP). This results in a low rank wireless channel, presenting a bottleneck for uplink communication systems. The current uplink systems that use orthogonal multiple access (OMA) and the proposed non-orthogonal multiple access (NOMA), fail to achieve the required data rates / power consumption under predominantly low rank channel scenarios. This paper introduces an optimal power sub carrier allocation algorithm for multi-carrier NOMA, named minPMAC, and an associated time-sharing algorithm that adaptively changes successive interference cancellation decoding orders to maximize sum data rates in these low rank channels. This Lagrangian based optimization technique, although globally optimum, is prohibitive in terms of runtime, proving inefficient for real-time scenarios. Hence, we propose a novel near-optimal deep reinforcement learning-based energy sum optimization (DRL-minPMAC) which achieves real-time efficiency. Extensive experimental evaluations show that minPMAC achieves 28\% and 39\% higher data rates than NOMA and OMA baselines. Furthermore, the proposed DRL-minPMAC runs ~5 times faster than minPMAC and achieves 83\% of the global optimum data rates in real time