An Adaptive Cooperative Spectrum Sensing Scheme Based on the Optimal Data Fusion Rule

Abstract

Spectrum sensing is a key technology in Cognitive Radio (CR). It provides the CR system with available vacant channels list by sensing the spectrum environment over a wide frequency band. However, local sensing by single node will be compromised if the sensing node experiences shadowing or fading effect. Cooperative spectrum sensing is derived to solve this problem. In this paper, we propose an adaptive cooperative spectrum sensing scheme by using the optimal data fusion rule. To implement such a rule, the probabilities of detection and false alarm of each sensing node must be known, but these probabilities are not readily available in practice. So we develop an iterative algorithm to estimate the probabilities of detection and false alarm. Two different methods are also developed to enhance the adaptability of the proposed estimating algorithm in time-varying environment. Simulation results show that the proposed scheme can approach the ideal optimal performance but do not need any prior information of the Primary Users' (PU) signal.

Adaptive Cooperative Spectrum Sensing Using Group Intelligence

“…Hidden node conditions can be better handled using spatial diversity or the so-called cooperative or distributed spectrum sensing [8,13]. In the CAN scenario under consideration, SUs in the network broadcast their spectral decisions over the common control channel ( Figure 1).…”

“…This information would be used to combine the data using log-likelihood (LR) data fusion rule [13,15]. With N active SUs in the network, log-likelihood ratio test is The local decision of the i th SU is denoted by d i and S + indicates subset with occupancy result d i =1 and S -with result d i =-1.…”

“…With N active SUs in the network, log-likelihood ratio test is The local decision of the i th SU is denoted by d i and S + indicates subset with occupancy result d i =1 and S -with result d i =-1. P 1 and P 0 are the prior probability of the presence & absence of PU signals [13]. Decision from each SU is weighed based on its reliability, i.e.…”

“…This technique is considered the optimum technique for data fusion provided correct estimates of the local probabilities are available. As a result, distributed spectrum sensing with adaptive weighing of the local decisions before fusion has been the general approach so far for performance improvements [13,14]. However, it has a major drawback of the necessity of computing P d and P f. which is an overhead and can lead to its own uncertainties.…”

A cluster-based selective cooperative spectrum sensing scheme in cognitive radio

“…However, the major disadvantage of energy detection is the hidden node problem, in which the sensing node cannot distinguish between an idle and a deeply faded or shadowed band [1]. Cooperative spectrum sensing (CSS) which uses a distributed detection model has been considered to overcome that problem [2][3][4][5][6][7][8][9][10][11][12].…”