Cooperative Sequential Spectrum Sensing Based on Level-triggered Sampling

TL;DR

This paper introduces a cooperative spectrum sensing framework using event-triggered sampling and sequential detection, which efficiently combines local decisions from secondary users to improve detection performance in cognitive radio networks.

Contribution

It presents a novel event-triggered sampling scheme for cooperative spectrum sensing, outperforming traditional uniform sampling methods with fewer bits and asynchronous updates.

Findings

Outperforms uniform sampling schemes with fewer bits

Achieves better detection accuracy under various SNR conditions

Reduces communication overhead with event-triggered updates

Abstract

We propose a new framework for cooperative spectrum sensing in cognitive radio networks, that is based on a novel class of non-uniform samplers, called the event-triggered samplers, and sequential detection. In the proposed scheme, each secondary user computes its local sensing decision statistic based on its own channel output; and whenever such decision statistic crosses certain predefined threshold values, the secondary user will send one (or several) bit of information to the fusion center. The fusion center asynchronously receives the bits from different secondary users and updates the global sensing decision statistic to perform a sequential probability ratio test (SPRT), to reach a sensing decision. We provide an asymptotic analysis for the above scheme, and under different conditions, we compare it against the cooperative sensing scheme that is based on traditional uniform sampling and sequential detection. Simulation results show that the proposed scheme, using even 1 bit, can outperform its uniform sampling counterpart that uses infinite number of bits under changing target error probabilities, SNR values, and number of SUs.