On Optimizing Cooperative Cognitive User Performance under Primary QoS Constraints

TL;DR

This paper develops optimization strategies for cognitive radio networks to improve secondary user performance while ensuring primary user QoS, transforming complex problems into solvable linear and quasiconvex programs.

Contribution

It introduces a novel approach to optimize secondary user throughput and delay under primary QoS constraints, transforming non-convex problems into linear and quasiconvex forms.

Findings

Proposed cooperation policy balances secondary and primary QoS.

Optimization problems are equivalent with identical solutions.

Numerical results validate the effectiveness of the policy.

Abstract

We study the problem of optimizing the performance of cognitive radio users with opportunistic real-time applications subject to primary users quality-of-service (QoS) constraints. Two constrained optimization problems are formulated; the first problem is maximizing the secondary user throughput while the second problem is minimizing the secondary user average delay, subject to a common constraint on the primary user average delay. In spite of the complexity of the optimization problems, due to their non-convexity, we transform the first problem into a set of linear programs and the second problem into a set of quasiconvex optimization problems. We prove that both problems are equivalent with identical feasible sets and optimal solutions. We show, through numerical results, that the proposed cooperation policy represents the best compromise between enhancing the secondary users QoS and satisfying the primary users QoS requirements.