Optimal Power Control in Decentralized Gaussian Multiple Access Channels

TL;DR

This paper develops a numerical method to optimize power control in decentralized Gaussian MACs where transmitters only know their own channel states, aiming to maximize average throughput.

Contribution

It introduces an alternating-maximization approach to compute optimal power policies and capacity for decentralized MACs with various fading distributions.

Findings

AM method effectively computes capacity for Rayleigh and Rician fading.

Optimal decentralized power policies differ from centralized solutions.

Method provides a practical tool for capacity estimation in decentralized settings.

Abstract

We consider the decentralized power optimization problem for Gaussian fast-fading multiple access channel (MAC) so that the average sum-throughput is maximized. In our MAC setup, each transmitter has access to only its own fading coefficient or channel state information (CSI) while the receiver has full CSI available at all instants. Unlike centralized MAC (full CSIT MAC) where the optimal powers are known explicitly, the analytical solution for optimal decentralized powers does not seem feasible. In this letter, we specialize alternating-maximization (AM) method for numerically computing the optimal powers and ergodic capacity of the decentralized MAC for general fading statistics and average power constraints. For illustration, we apply our AM method to compute the capacity of MAC channels with fading distributions such as Rayleigh, Rician etc.