Learning Equilibria of a Stochastic Game on Gaussian Interference Channels with Incomplete Information

TL;DR

This paper introduces distributed learning algorithms for power control and rate adaptation in Gaussian interference channels modeled as a stochastic game, aiming to optimize transmission success, fairness, and overall network performance.

Contribution

It proposes novel distributed algorithms to find correlated, coarse correlated, Pareto, and Nash bargaining equilibria in a stochastic game setting for interference channels.

Findings

Distributed algorithms effectively find various equilibria and Pareto points.

The algorithms improve sum rate and fairness compared to existing methods.

Multiple rate transmission strategies enhance network performance.

Abstract

We consider a wireless communication system in which $N$ transmitter-receiver pairs want to communicate with each other. Each transmitter transmits data at a certain rate using a power that depends on the channel gain to its receiver. If a receiver can successfully receive the message, it sends an acknowledgment (ACK), else it sends a negative ACK (NACK). Each user aims to maximize its probability of successful transmission. We formulate this problem as a stochastic game and propose a fully distributed learning algorithm to find a correlated equilibrium (CE). In addition, we use a no regret algorithm to find a coarse correlated equilibrium (CCE) for our power allocation game. We also propose a fully distributed learning algorithm to find a Pareto optimal solution. In general Pareto points do not guarantee fairness among the users, therefore we also propose an algorithm to compute a Nash bargaining solution which is Pareto optimal and provides fairness among users. Finally, under the same game theoretic setup, we study these equilibria and Pareto points when each transmitter sends data at multiple rates rather than at a fixed rate. We compare the sum rate obtained at the CE, CCE, Nash bargaining solution and the Pareto point and also via some other well known recent algorithms.