Spectrum Sensing in Cooperative Cognitive Radio Networks with Partial CSI

TL;DR

This paper presents an efficient cooperative spectrum sensing algorithm for cognitive radio networks with partial channel information, using Laplace approximation to improve detection performance over traditional energy detection methods.

Contribution

It introduces a novel likelihood ratio test-based spectrum sensing method that handles partial CSI using Laplace approximation, enhancing detection accuracy.

Findings

Proposed scheme outperforms energy detection in simulations.

Laplace approximation effectively estimates intractable likelihoods.

Method improves spectrum sensing reliability with partial CSI.

Abstract

We develop an efficient algorithm for cooperative spectrum sensing in a relay based cognitive radio network. We consider a stochastic model where data is sent from the Base Station (BS) of the Primary User (PU). The data is relayed by the Secondary Users (SU) to the SU BS. The SU BS has only partial CSI knowledge of the wireless channels. In order to obtain the optimal decision rule based on Likelihood Ratio Test (LRT), the marginal likelihood under each hypothesis needs to be evaluated pointwise. These, however, cannot be obtained analytically due to the intractability of the integrals. Instead, we approximate these quantities by utilising the Laplace method. Performance is evaluated via numerical simulations and it is shown that the proposed spectrum sensing scheme can achieve superior results to the energy detection scheme.