On Resource Allocation in Fading Multiple Access Channels - An Efficient Approximate Projection Approach

TL;DR

This paper develops efficient algorithms for rate and power allocation in fading multiple access channels, maximizing utility without queue-length info, and introduces approximate projection methods with polynomial complexity.

Contribution

It proposes an approximate projection algorithm for utility maximization in multiple access channels, exploiting polymatroid structure for efficiency, and introduces greedy and approximate policies for fading scenarios.

Findings

The iterative gradient projection algorithm converges efficiently.

The greedy rate allocation policy performs close to optimal in fading channels.

Approximate policies reduce computational complexity while maintaining performance.

Abstract

We consider the problem of rate and power allocation in a multiple-access channel. Our objective is to obtain rate and power allocation policies that maximize a general concave utility function of average transmission rates on the information theoretic capacity region of the multiple-access channel. Our policies does not require queue-length information. We consider several different scenarios. First, we address the utility maximization problem in a nonfading channel to obtain the optimal operating rates, and present an iterative gradient projection algorithm that uses approximate projection. By exploiting the polymatroid structure of the capacity region, we show that the approximate projection can be implemented in time polynomial in the number of users. Second, we consider resource allocation in a fading channel. Optimal rate and power allocation policies are presented for the case that power control is possible and channel statistics are available. For the case that transmission power is fixed and channel statistics are unknown, we propose a greedy rate allocation policy and provide bounds on the performance difference of this policy and the optimal policy in terms of channel variations and structure of the utility function. We present numerical results that demonstrate superior convergence rate performance for the greedy policy compared to queue-length based policies. In order to reduce the computational complexity of the greedy policy, we present approximate rate allocation policies which track the greedy policy within a certain neighborhood that is characterized in terms of the speed of fading.