Fuzzy Hypothesis Testing for Cooperative Sequential Spectrum Sensing Under Noise Uncertainty

Abstract

Efficient and reliable spectrum sensing is extremely significant, especially in the presence of noise uncertainty in low SNR environment below which conventional detectors fail to be robust. In this letter, by development of a sequential probability ratio test (SPRT) for the fuzzy hypothesis testing (FHT), we propose a novel cooperative sequential detector to deal with the effect of noise power uncertainty. In this approach, for every measurement, FHT is computed by each cognitive radio. Subsequently, fusion center (FC) sequentially accumulates these fuzzy test statistics and decides about the sensing time. Simulation results are illustrated to show the effectiveness and robustness of the proposed sequential FHT detector. The significant reduction in sample complexity is demonstrated for our scheme in comparison with energy detector, sequential crisp hypothesis testing detector, and fixed sample size FHT detector.

Improved Energy Detector for Spectrum Sensing Using Neuro-Fuzzy Double Threshold Technique

“…Alternatively different from the above approaches researchers have tried to solve this limitation of energy detector by adopting soft decision method using Fuzzy logic [18,27,19,20,9] as there is great deal of ambiguity, uncertainty or fuzziness in the 'confused region' as shown in Figure 1 for a cooperative sensing scenario. The disadvantages cooperative based sensing are the increased hardware overhead as well as sensing latency.…”

“…is the Q-function, γ is the received SNR of the signal, P D is probability of detection, P FA is the probability of false alarm, P MD is the probability of miss detection. The threshold computation as per Neymen-Pearson criterion [20] is,…”

Joint cooperative spectrum sensing and primary user emulation attack detection in cognitive radio networks using fuzzy conditional entropy maximization

“…The existing policy of spectrum allocation works on static mode, where the available frequency spectra are divided into subbands. Each subband is exclusively assigned to the specific service provider, known as licensed user or primary user (PU) . However, based on the recent published reports of Federal Communications Commission in USA and few other regulatory bodies, eg, Office of Communications in UK, it is found that major portions of the allocated RF spectra are underutilized by the licensed users .…”

“…Each subband is exclusively assigned to the specific service provider, known as licensed user or primary user (PU) . However, based on the recent published reports of Federal Communications Commission in USA and few other regulatory bodies, eg, Office of Communications in UK, it is found that major portions of the allocated RF spectra are underutilized by the licensed users . Cognitive radio (CR) communication mitigates this spectrum underutilization problem by allowing the CR/secondary users (SUs) either to access the spectrum opportunistically (when PU is not transmitting) or share the same frequency band with PU subject to meeting its tolerable interference .…”

“…Cognitive radio (CR) communication mitigates this spectrum underutilization problem by allowing the CR/secondary users (SUs) either to access the spectrum opportunistically (when PU is not transmitting) or share the same frequency band with PU subject to meeting its tolerable interference . Research issues on CR include spectrum sensing (SS), spectrum sharing, spectrum management, spectrum mobility, and dynamic spectrum access . Among these, SS is key in CR system design as it provides SUs the information about PU's spectrum use and, consequently, the available spectrum for the use of SUs …”