Wideband signal detection for cognitive radio applications with limited resources

Abstract

Wideband signals are expected to be used to achieve the required quality of service (QoS) in the next generation of wireless communications, civil and military radar, and many wireless sensor network (WSN) scenarios. Wideband signal detection has been identified as one of the most challenging problems in the proliferation of the cognitive radio technology. Moreover in many applications, spectrum sensing in cognitive radio (CR) is expected to be performed with limited resources in terms of time, computation, and complexity. This paper is dedicated to the detection of a wideband signal with small sample size. Aiming at using small sample size, a statistical model of samples is given based on Student's t distribution. However, the limited number of channel observations brings a reduction of confidence in the decision. A set of new basic probability assignments associated with the hypothesis of the occupied or vacant channel are then proposed to perform the Dempster-Shafer (D-S) decision process. Simulation results show that the proposed method has much higher sensitivity to sense an occupied channel than the traditional energy detection method (ED) and the decision fusion method when small sample size is used.

Dynamic Robust Spectrum Sensing Based on Goodness-of-Fit Test Using Bilateral Hypotheses

“…Then, the samples are acquired with a sampling rate several times faster than the baseband frequency. Thus, s t can be considered as a constant, s t = 1, as shown in [39]. The sensing problem in this case can be considered as a standard scenario with same variance and different mean value Gaussian distributions with corresponding hypotheses.…”

“…Hence, we further assume that the detected signal is a wide-band signal. As shown in [39], the bandwidth to be detected can be divided into n subbands, where each subband has equal bandwidth. For each subband, the m collected samples are limited to small numbers.…”

“…In fact, the performance of ED using Gaussian approximation becomes good only when sufficiently large sample size is available [37]. It has been shown that the Student's t-test is the optimal test in spectrum sensing given a small number of samples [38,39]. Then, taking into account the limitations of the traditional GoF test (e.g., AD test and KS test) under a small number of samples, the powerful GoF test [40][41][42][43][44] is introduced to precisely evaluate the distance between common cumulative distribution and the empirical distribution of observation.…”

A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram

“…It is also the basic task of the spectrum sensing [3] and the blind signal separation [4,5]. For traditional SD methods, the energy detection has once been the most popular technique that can be classified as the thresholdbased algorithms [6][7][8][9][10][11] and the non-threshold-based algorithms [12][13][14][15][16].…”

Analysis study and SDR implementation of GoF‐based spectrum sensing for cognitive radio

“…Theses works address experimental results of such spectrum sensing methods and highlight the impact of some critical parameters on the detection performance. Recently, a robust GoF wideband spectrum sensing method is proposed under small sample size constraint, resulting in reduced processing time [17]. In this work, the spectrum sensing problem is reformulated into Student's t distribution test.…”