Inter-Sensing Time Optimization in Cognitive Radio Networks

TL;DR

This paper develops an optimization framework for inter-sensing times in cognitive radio networks, enhancing secondary throughput while limiting interference, by adapting sensing intervals based on channel activity and sensing outcomes.

Contribution

It introduces a novel method for dynamically optimizing sensing intervals in unslotted primary channels, improving secondary throughput and reducing interference.

Findings

Optimized sensing intervals increase secondary throughput.

Adaptive sensing reduces false alarms and misdetections.

Proposed scheme outperforms fixed sensing period approach.

Abstract

We consider a set of primary channels that operate in an unslotted fashion, switching activity at random times. A secondary user senses the primary channels searching for transmission opportunities. If a channel is sensed to be free, the secondary terminal transmits, and if sensed to be busy, the secondary transmitter remains silent.We solve the problem of determining the optimal time after which a primary channel needs to be sensed again depending on the sensing outcome. The objective is to find the inter-sensing times such that the mean secondary throughput is maximized while imposing a constraint over the maximum tolerable interference inflicted on the primary network. Our numerical results show that by optimizing the sensing-dependent inter-sensing times, our proposed scheme reduces the impact of sensing errors caused by false alarm and misdetection and outperforms the case of a single sensing period.