Unilateral left‐tail Anderson Darling test‐based spectrum sensing with Laplacian noise

Abstract

This paper focuses on spectrum sensing under Laplacian noise. To remit the negative effects caused by heavytailed behavior of Laplacian noise, the fractional lower order moments (FLOM) technology is employed to pre-process the received samples before spectrum sensing. Via exploiting the asymmetrical difference between the distribution for the FLOM of received samples in the absence and presence of primary users, we formulate the spectrum sensing problem under Laplacian noise as a unilateral goodness-of-fit (GoF) test problem. Based on this test problem, we propose a new GoF-based detector which is called unilateral left-tail Anderson Darling (ULAD) detector. The analytical expressions for the theoretical performance, in terms of false-alarm and detection probabilities, of the ULAD are derived. Moreover, a closed-form expression for the optimal detection threshold is also derived to minimize the total error rate. Simulation results are provided to validate the theoretical analyses and to demonstrate the superior performance of the proposed detector than others.

Norm‐based spectrum sensing for cognitive radios under generalised Gaussian noise

“…A novel GoFT based detector is proposed in ref [17] and optimal detection threshold is calculated. Nevertheless, in ref [17] the efficacy of the proposed detector is decided under heavy-tailed Laplacian noise distribution.…”

“…However, in a practical scenario, channel noise is not strictly Gaussian and follows a heavy-tailed nature. To address this challenge, channel noise is noted to be modelled following a Laplacian distribution [15][16][17]. One can note, the GGD family of distribution is characterised by a symmetric unimodal density characteristic and can support a variable tail-length based on the shape parameter, β of GGD [33].…”

“…However, this work is limited to the study of IED [8] and does not calculate the p ‐norm of the received samples. A novel GoFT based detector is proposed in ref [17] and optimal detection threshold is calculated. Nevertheless, in ref [17] the efficacy of the proposed detector is decided under heavy‐tailed Laplacian noise distribution.…”

Generalized Energy Detection Under Generalized Noise Channels

“…In the presence of detrimental yet unavoidable effect of uncertain noise power estimation, the corresponding uncertainty factor can be modeled such that σ 2 w ∈ {σ 2 w /ρ, ρσ 2 w } withσ 2 w standing for the estimated noise power and ρ ≥ 1 [14]. In practice, the distribution of the actual uncertainty factor is quite difficult to obtain.…”