Information Theory vs. Queueing Theory for Resource Allocation in Multiple Access Channels

TL;DR

This paper compares information theory and queueing theory approaches for resource allocation in multiple access channels, revealing their equivalence and demonstrating the effectiveness of a simple greedy policy over queue-based methods.

Contribution

It establishes the equivalence between the information theoretic capacity region and the stability region of the channel, and shows the superiority of a greedy policy in certain metrics.

Findings

Capacity and stability regions are equivalent.

Greedy policy outperforms queue-based policies in convergence and fairness.

Simple policies can be more effective than complex queue-based methods.

Abstract

We consider the problem of rate allocation in a fading Gaussian multiple-access channel with fixed transmission powers. The goal is to maximize a general concave utility function of the expected achieved rates of the users. There are different approaches to this problem in the literature. From an information theoretic point of view, rates are allocated only by using the channel state information. The queueing theory approach utilizes the global queue-length information for rate allocation to guarantee throughput optimality as well as maximizing a utility function of the rates. In this work, we make a connection between these two approaches by showing that the information theoretic capacity region of a multiple-access channel and its stability region are equivalent. Moreover, our numerical results show that a simple greedy policy which does not use the queue-length information can outperform queue-length based policies in terms of convergence rate and fairness.