On resource allocation in fading multiple-access channels-an efficient approximate projection approach

Authors:
Ali ParandehGheibi;Atilla Eryilmaz;Asuman Ozdaglar;Muriel Médard
Affiliations:
Laboratory for Information and Decision Systems, Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA;Electrical and Computer Engineering Department, The Ohio State University, Columbus, OH;Laboratory for Information and Decision Systems, Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA;Laboratory for Information and Decision Systems, Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA
Venue:
IEEE Transactions on Information Theory
Year:
2010

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Abstract

In this paper, we consider the problem of rate and power allocation in a multiple-access channel (MAC). 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 MAC without using queue-length information. First, we address the utility maximization problem in a nonfading channel and present a gradient projection algorithm with approximate projections. 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 present optimal rate and power allocation policies in a fading channel where channel statistics are known. For the case that channel statistics are unknown and the transmission power is fixed, we propose a greedy rate allocation policy and characterize the performance difference of this policy and the optimal policy in terms of channel variations and structure of the utility function. The numerical results 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.