An optimal solution to the "Philadelphia" channel assignment problem

1999 ◽  
Vol 48 (3) ◽  
pp. 1012-1014 ◽  
Author(s):  
J.C.M. Janssen ◽  
K. Kilakos
Keyword(s):  
Channel Assignment ◽  
Assignment Problem ◽  
Optimal Solution ◽  
Channel Assignment Problem

Comments on "An Optimal Solution for the Channel-Assignment Problem"

1981 ◽  
Vol C-30 (6) ◽  
pp. 454-454
Author(s):  
Persky ◽  
Tien ◽  
Ting
Keyword(s):  
Channel Assignment ◽  
Assignment Problem ◽  
Optimal Solution ◽  
Channel Assignment Problem

An Optimal Solution for the Channel-Assignment Problem

1979 ◽  
Vol C-28 (11) ◽  
pp. 807-810 ◽  
Author(s):  
Gupta ◽  
Lee ◽  
Leung
Keyword(s):  
Channel Assignment ◽  
Assignment Problem ◽  
Optimal Solution ◽  
Channel Assignment Problem

On the Use of Particle Swarm Optimization Techniques for Channel Assignments in Cognitive Radio Networks

2012 ◽  
pp. 202-214 ◽  
Author(s):  
Hisham M. Abdelsalam ◽  
Haitham S. Hamza ◽  
Abdoulraham M. Al-Shaar ◽  
Abdelbaset S. Hamza
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Channel Assignment ◽  
Assignment Problem ◽  
Pso Algorithm ◽  
Radio Networks ◽  
Optimization Techniques ◽  
Secondary Users ◽  
Swarm Optimization ◽  
Channel Assignment Problem

Efficient utilization of open spectrum in cognitive radio networks requires appropriate allocation of idle spectrum frequency bands (not used by licensed users) among coexisting cognitive radios (secondary users) while minimizing interference among all users. This problem is referred to as the spectrum allocation or the channel assignment problem in cognitive radio networks, and is shown to be NP-hard. Accordingly, different optimization techniques based on evolutionary algorithms were needed in order to solve the channel assignment problem. This chapter investigates the use of particular swarm optimization (PSO) techniques to solve the channel assignment problem in cognitive radio networks. In particular, the authors study the definitiveness of using the native PSO algorithm and the Improved Binary PSO (IBPSO) algorithm to solve the assignment problem. In addition, the performance of these algorithms is compared to that of a fine-tuned genetic algorithm (GA) for this particular problem. Three utilization functions, namely, Mean-Reward, Max-Min-Reward, and Max-Proportional-Fair, are used to evaluate the effectiveness of three optimization algorithms. Extensive simulation results show that PSO and IBPSO algorithms outperform that fine-tuned GA. More interestingly, the native PSO algorithm outperforms both the GA and the IBPSO algorithms in terms of solution speed and quality.

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