Asymptotically Optimal Multiple-access Communication via Distributed Rate Splitting

TL;DR

This paper introduces Distributed Rate Splitting, a scheme enabling multiple users to achieve optimal communication rates in distributed settings for Gaussian and discrete channels, with lower complexity and asymptotic optimality.

Contribution

It proposes a novel distributed rate splitting scheme that attains information-theoretic capacity bounds with reduced complexity and coordination, applicable to both Gaussian and discrete channels.

Findings

Achieves optimal rates asymptotically as virtual users increase.

Converges to maximum equal rate point in symmetric settings.

Accommodates differential user rate requirements in a distributed manner.

Abstract

We consider the multiple-access communication problem in a distributed setting for both the additive white Gaussian noise channel and the discrete memoryless channel. We propose a scheme called Distributed Rate Splitting to achieve the optimal rates allowed by information theory in a distributed manner. In this scheme, each real user creates a number of virtual users via a power/rate splitting mechanism in the M-user Gaussian channel or via a random switching mechanism in the M-user discrete memoryless channel. At the receiver, all virtual users are successively decoded. Compared with other multiple-access techniques, Distributed Rate Splitting can be implemented with lower complexity and less coordination. Furthermore, in a symmetric setting, we show that the rate tuple achieved by this scheme converges to the maximum equal rate point allowed by the information-theoretic bound as the number of virtual users per real user tends to infinity. When the capacity regions are asymmetric, we show that a point on the dominant face can be achieved asymptotically. Finally, when there is an unequal number of virtual users per real user, we show that differential user rate requirements can be accommodated in a distributed fashion.