Underlay Cognitive Radios with Capacity Guarantees for Primary Users

TL;DR

This paper develops optimal resource allocation algorithms for underlay cognitive radios that guarantee primary user capacity while maximizing secondary user throughput, considering perfect and imperfect channel information.

Contribution

It introduces a novel optimization framework with zero-duality gap for capacity-guaranteed cognitive radio resource allocation under long-term constraints.

Findings

Optimal schemes depend on CSI and Lagrange multipliers.

Efficient solutions are possible despite non-convex constraints.

Algorithms adapt to imperfect CSI scenarios.

Abstract

To use the spectrum efficiently, cognitive radios leverage knowledge of the channel state information (CSI) to optimize the performance of the secondary users (SUs) while limiting the interference to the primary users (PUs). The algorithms in this paper are designed to maximize the weighted ergodic sum-capacity of SUs, which transmit orthogonally and adhere simultaneously to constraints limiting: i) the long-term (ergodic) capacity loss caused to each PU receiver; ii) the long-term interference power at each PU receiver; and iii) the long-term power at each SU transmitter. Formulations accounting for short-term counterparts of i) and ii) are also discussed. Although the long-term capacity constraints are non-convex, the resultant optimization problem exhibits zero-duality gap and can be efficiently solved in the dual domain. The optimal allocation schemes (power and rate loadings, frequency bands to be accessed, and SU links to be activated) are a function of the CSI of the primary and secondary networks as well as the Lagrange multipliers associated with the long-term constraints. The optimal resource allocation algorithms are first designed under the assumption that the CSI is perfect, then the modifications needed to accommodate different forms of imperfect CSI (quantized, noisy, and outdated) are analyzed.