Rate Selection and Power Adaptation using Maximal Ratio Combining for the Random Access Gaussian Channel

TL;DR

This paper proposes a novel rate and power adaptation scheme for random access Gaussian channels using maximal ratio combining, significantly improving power efficiency and average rate compared to traditional IRSA methods.

Contribution

It introduces a new adaptive scheme that accounts for physical layer limitations, enhancing IRSA performance in Gaussian channels with low SNR.

Findings

Substantial power efficiency improvements observed.

Increased average data rates achieved.

Effective adaptation to physical constraints demonstrated.

Abstract

With the emergence of machine-driven communi- cation, there is a renewed interest in the design of random multiple access schemes for networks with large number of active devices. Many of the recently proposed access paradigms are enhancements to slotted ALOHA. One of the popular schemes, irregular repetition slotted ALOHA (IRSA), is based on an analogy between multiple access with successive interference cancellation and message-passing decoding on bipartite graphs. Most of the results on IRSA and its variants focus on the collision channel and they ignore physical limitations such as transmit power constraints and additive Gaussian noise at the physical layer. As such, naive extensions of IRSA to the Gaussian multiple access channel are not power efficient in the low signal-to-noise- ratio regime. This work introduces a novel paradigm whereby devices adapt their rates and/or transmit powers based on their chosen repetition profiles. The receiver performs maximal ratio combining over all the replicas prior to decoding a message. Numerical results for finite number of users show that the proposed scheme can provide substantial improvements in terms of power efficiency and average rate over standard IRSA.