Optimal Channel Sensing Strategy for Cognitive Radio Networks with Heavy-Tailed Idle Times

TL;DR

This paper develops an optimal sensing strategy for cognitive radio networks considering heavy-tailed idle times of primary users, using Markov decision processes to improve spectrum sensing efficiency.

Contribution

It introduces a novel optimal sensing policy framework for heavy-tailed idle times modeled by Hyper-exponential distribution, extending beyond exponential assumptions.

Findings

Proposes a new multishot sensing interval policy.

Demonstrates improved performance over existing policies.

Reduces interference and sensing frequency.

Abstract

In Cognitive Radio Network (CRN), the secondary user (SU) opportunistically access the wireless channels whenever they are free from the licensed / Primary User (PU). Even after occupying the channel, the SU has to sense the channel intermittently to detect reappearance of PU, so that it can stop its transmission and avoid interference to PU. Frequent channel sensing results in the degradation of SU's throughput whereas sparse sensing increases the interference experienced by the PU. Thus, optimal sensing interval policy plays a vital role in CRN. In the literature, optimal channel sensing strategy has been analysed for the case when the ON-OFF time distributions of PU are exponential. However, the analysis of recent spectrum measurement traces reveals that PU exhibits heavy-tailed idle times which can be approximated well with Hyper-exponential distribution (HED). In our work, we deduce the structure of optimal sensing interval policy for channels with HED OFF times through Markov Decision Process (MDP). We then use dynamic programming framework to derive sub-optimal sensing interval policies. A new Multishot sensing interval policy is proposed and it is compared with existing policies for its performance in terms of number of channel sensing and interference to PU.