Optimal Power Allocation and Time Sharing in Low Rank Multi-carrier Wi-Fi Channels

TL;DR

This paper introduces a novel power allocation and time-sharing algorithm for low-rank multi-carrier Wi-Fi channels, significantly improving data rates over existing OMA and NOMA methods in uplink scenarios with multiple users.

Contribution

It develops an optimal power and time-sharing algorithm that adaptively varies decoding orders, outperforming traditional methods in low-rank multi-user Wi-Fi channels.

Findings

Achieves up to 39% higher sum data rates than OMA.

Outperforms NOMA and multi-carrier NOMA baselines in low-rank channels.

Effectively adapts to varying user numbers and antenna configurations.

Abstract

The ever-evolving landscape of distributed wireless systems, e.g. multi-user AR/VR systems, demands high data rates (up to 500 Mbps per user) and low power consumption. With increasing number of participating users, uplink data transmission in the situation where the number of transmitter user antennas exceeds the number of access point (AP) antennas presents a low-rank channel problem. Current Wi-Fi standards using orthogonal multiple access (OMA) fail to address these requirements. Non-orthogonal multiple access (NOMA)-based systems, while outperforming the OMA methods, still fall short of the requirement in low-rank channel uplink transmission, because they adhere to a single decoding order for successive interference cancelation (SIC). This paper proposes and develops a novel optimal power-subcarrier allocation algorithm to maximize the achieved data rates for this low rank channel scenario. Additionally, the proposed algorithm implements a novel time-sharing algorithm for simultaneously participating users, which adaptively varies the decoding orders to achieve higher data rates than any single decoding order. Extensive experimental validations demonstrate that the proposed algorithm achieves 39%, 28%, and 16% higher sum data rates than OMA, NOMA, and multi-carrier NOMA baselines respectively, under low-rank channel conditions, under varying SNR values. We further show that the proposed algorithm significantly outperforms the baselines with varying numbers of users or AP antennas, showing the effectiveness of the optimal power allocation and time-sharing.