Wideband Spectrum Sensing in Cognitive Radio Networks

TL;DR

This paper presents a novel multiband joint detection technique for wideband spectrum sensing in cognitive radio networks, improving spectrum utilization and reducing interference through optimized detection strategies.

Contribution

It introduces a new multiband joint detection method with an optimization framework that exploits hidden convexity for efficient wideband spectrum sensing.

Findings

Significant improvement in spectrum sensing performance.

Effective reduction of interference to primary users.

Optimization solutions derived for practical scenarios.

Abstract

Spectrum sensing is an essential enabling functionality for cognitive radio networks to detect spectrum holes and opportunistically use the under-utilized frequency bands without causing harmful interference to legacy networks. This paper introduces a novel wideband spectrum sensing technique, called multiband joint detection, which jointly detects the signal energy levels over multiple frequency bands rather than consider one band at a time. The proposed strategy is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. The spectrum sensing problem is formulated as a class of optimization problems in interference limited cognitive radio networks. By exploiting the hidden convexity in the seemingly non-convex problem formulations, optimal solutions for multiband joint detection are obtained under practical conditions. Simulation results show that the proposed spectrum sensing schemes can considerably improve the system performance. This paper establishes important principles for the design of wideband spectrum sensing algorithms in cognitive radio networks.