Premium
Optimization of multiband cooperative spectrum sensing with particle swarm optimization
Author(s) -
Hei Yongqiang,
Wei Ran,
Li Wentao,
Zhang Cong,
Li Xiaohui
Publication year - 2017
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3226
Subject(s) - particle swarm optimization , benchmark (surveying) , mathematical optimization , throughput , aggregate (composite) , convergence (economics) , computer science , interference (communication) , multi swarm optimization , swarm behaviour , optimization problem , algorithm , mathematics , channel (broadcasting) , wireless , telecommunications , materials science , geodesy , composite material , economic growth , economics , geography
In this paper, the multiband linear cooperative spectrum sensing optimization problem related to weight coefficient, decision threshold, and sensing time is investigated to maximize the aggregated opportunistic throughput under the constraints of the aggregate interference, the subband interference, and the subband utilization. Theoretical analysis is carried out to investigate the effects of the interference, the sensing time, and the decision threshold on the throughput. Due to the nonconvex characteristic of the optimization problem, a particle swarm optimization (PSO) algorithm integrated with the golden section search (GSS) method (PSO‐GSS) is proposed. Simulation results show that the PSO‐GSS has a powerful searching ability and yields a 16.0%, 15.8%, and 1.23% increase in the aggregate throughput when compared with artificial bee colony, genetic algorithm, and particle swarm optimization, respectively. Moreover, PSO‐GSS has a rapid convergence with only 15 iterations in reaching the maximum aggregate throughput, which is much less than the 80 iterations required by PSO. Additionally, with the PSO‐GSS, the optimal sensing time is around 6 ms, and it is almost insensitive to the number of subchannels. Due to these advantages, it can provide a benchmark function for the other existing methods when dealing with the cooperative spectrum sensing problem.