Cost-Efficient Throughput Maximization in Multi-Carrier Cognitive Radio Systems

TL;DR

This paper proposes a spectrum access pricing scheme for cognitive radio systems that enables secondary users to maximize throughput while respecting primary users' interference constraints, with proven convergence and scalability.

Contribution

It introduces a non-cooperative game model with a unique Nash equilibrium and a distributed power allocation algorithm suitable for dynamic, fast-fading channels.

Findings

Unique Nash equilibrium exists under mild conditions.

Distributed algorithm converges quickly using local measurements.

Scheme performs well under realistic, dynamic network conditions.

Abstract

Cognitive radio (CR) systems allow opportunistic, secondary users (SUs) to access portions of the spectrum that are unused by the network's licensed primary users (PUs), provided that the induced interference does not compromise the primary users' performance guarantees. To account for interference constraints of this type, we consider a flexible spectrum access pricing scheme that charges secondary users based on the interference that they cause to the system's primary users (individually, globally, or both), and we examine how secondary users can maximize their achievable transmission rate in this setting. We show that the resulting non-cooperative game admits a unique Nash equilibrium under very mild assumptions on the pricing mechanism employed by the network operator, and under both static and ergodic (fast-fading) channel conditions. In addition, we derive a dynamic power allocation policy that converges to equilibrium within a few iterations (even for large numbers of users), and which relies only on local signal-to-interference-and-noise measurements; importantly, the proposed algorithm retains its convergence properties even in the ergodic channel regime, despite the inherent stochasticity thereof. Our theoretical analysis is complemented by extensive numerical simulations which illustrate the performance and scalability properties of the proposed pricing scheme under realistic network conditions.