Performance Analysis of Cooperative Spectrum Sensing under Guaranteed Throughput Constraints for Cognitive Radio Networks

Abstract

One of the main challenges in cognitive radio networks is the ability of secondary users to detect the primary user presence with high probability of detection. In previous research, optimizing cooperative sensing in cognitive radio networks is performed for either a targeted probability of detection or a false alarm. After setting one of the probabilities as an optimization constraint, the other is optimized. In this paper, a guaranteed constant throughput at the secondary users is introduced as a target while optimizing probability of detection for cooperative sensing. Both sensing time values and number of cooperated cognitive radio secondary users are investigated to maximize the probability of detection of primary user. AND and OR hard decision schemes are considered and compared with soft decision scheme which is weighted modified deflection coefficient scheme (W-MDC). It is illustrated that cooperation of all users and utilizing full frames for sensing time will not provide maximum probability of detection. A tradeoff between performances of cognitive radio networks with and without optimization is presented. The effects of varying network sizes, normalized target throughput, maximum frame duration times, and received signal-to-noise ratio at the fusion center are investigated for different fusion rules.

Joint spectrum sensing and resource allocation optimization using genetic algorithm for frequency hopping–based cognitive radio networks

“…We denote the traffic demand of node n as T D (n), and it can have any bounded value as in (10). We denote the traffic demand of node n as T D (n), and it can have any bounded value as in (10).…”

“…The traffic demand of node n also can be different with those of other member nodes. We denote the traffic demand of node n as T D (n), and it can have any bounded value as in (10). We assume that a CR network has a predetermined maximum data rate for each member node.…”

“…By considering different user contributions, a new weighted collaborative spectrum sensing method has been discussed in previous studies. 9,10 In Balieiro et al, 11 adaptive multiobjective genetic algorithm (GA) is formulated to maximize the spectrum opportunities while keeping the sensing overhead less than the defined maximum value. A weighted collaborative spectrum sensing strategy using GA is presented in Kamran and Moessner 12 to enhance spectrum sensing reliability and accuracy.…”

Performance measurement of one-bit hard decision fusion scheme for cooperative spectrum sensing in CR

“…To address this issue, multiple cognitive radios can be coordinated to perform spectrum sensing cooperatively. Several works [1][2][3][4][5][6][7] have shown that cooperative spectrum sensing can greatly increase the probability of detection in fading channels. In general, cooperative spectrum sensing is performed as follows: -Step 1: Every cognitive radio performs local spectrum measurements independently and then makes a binary decision.…”

Guided joint spectrum sensing and resource allocation using a novel random walk grey wolf optimization for frequency hopping cognitive radio networks

“…8 For maximizing the throughput for SUs avoiding the collision trade-off between sensing accuracy and transmission of data is described in Zhang et al 9 and Al-Doseri. 10 For maximizing the utilities and many multiobjective functions, 11 scientists took help of metaheuristic algorithms [12][13][14] like genetic algorithm (GA) in Balieiro et al, Kamran and Moessner, and Khalid and Anpalagan. [15][16][17][18] To solve the problem of channel allocation, game theory 19 technique has been proposed.…”