Channel-Adaptive Sensing Strategy for Cognitive Radio Ad Hoc Networks

TL;DR

This paper introduces a channel-aware sensing strategy for cognitive radio ad hoc networks that adapts to fading channel conditions, significantly improving throughput and robustness over traditional methods.

Contribution

It proposes a novel CSI-adaptive sensing approach at MAC and physical layers, enhancing spectrum utilization and network throughput in CR networks.

Findings

Significant throughput gains demonstrated in practical scenarios.

Robustness to CSI mismatch, sensing errors, and spatial correlation.

Outperforms conventional adaptive transmission strategies.

Abstract

In Cognitive Radio (CR) ad hoc networks, secondary users (SU) attempt to utilize valuable spectral resources without causing significant interference to licensed primary users (PU). While there is a large body of research on spectrum opportunity detection, exploitation, and adaptive transmission in CR, most existing approaches focus only on avoiding PU activity when making sensing decisions. Since the myopic sensing strategy results in congestion and poor throughput, several collision-avoidance sensing approaches were investigated in the literature. However, they provide limited improvement. A channel-aware myopic sensing strategy that adapts the reward to the fading channel state information (CSI) of the SU link is proposed. This CSI varies over the CR spectrum and from one SU pair to another due to multipath and shadow fading, thus randomizing sensing decisions and increasing the network throughput. The proposed joint CSI adaptation at the medium access control (MAC) and physical layers provides large throughput gain over randomized sensing strategies and/or conventional adaptive transmission methods. The performance of the proposed CSI-aided sensing strategy is validated for practical network scenarios and demonstrated to be robust to CSI mismatch, sensing errors, and spatial channel correlation.