scholarly journals Tight Lower Bound for the Channel Assignment Problem

2016 ◽  
Vol 12 (4) ◽  
pp. 1-19
Author(s):  
Arkadiusz Socała
Keyword(s):  
Lower Bound ◽  
Channel Assignment ◽  
Assignment Problem ◽  
Channel Assignment Problem

A new lower bound for the channel assignment problem

2000 ◽  
Vol 49 (4) ◽  
pp. 1265-1272 ◽  
Author(s):  
D.H. Smith ◽  
S. Hurley ◽  
S.M. Allen
Keyword(s):  
Lower Bound ◽  
Channel Assignment ◽  
Assignment Problem ◽  
Channel Assignment Problem

scholarly journals Improved Bounds for Radiok-Chromatic Number of HypercubeQn

2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
Laxman Saha ◽  
Pratima Panigrahi ◽  
Pawan Kumar
Keyword(s):  
Lower Bound ◽  
Chromatic Number ◽  
Graph Coloring ◽  
Upper Bound ◽  
Channel Assignment ◽  
Assignment Problem ◽  
Communication Problem ◽  
Channel Assignment Problem

A number of graph coloring problems have their roots in a communication problem known as the channel assignment problem. The channel assignment problem is the problem of assigning channels (nonnegative integers) to the stations in an optimal way such that interference is avoided as reported by Hale (2005). Radiok-coloring of a graph is a special type of channel assignment problem. Kchikech et al. (2005) have given a lower and an upper bound for radiok-chromatic number of hypercubeQn, and an improvement of their lower bound was obtained by Kola and Panigrahi (2010). In this paper, we further improve Kola et al.'s lower bound as well as Kchikeck et al.'s upper bound. Also, our bounds agree for nearly antipodal number ofQnwhenn≡2(mod 4).

ON RADIO NUMBER OF POWER OF CYCLES

2011 ◽  
Vol 04 (03) ◽  
pp. 523-544 ◽  
Author(s):  
Laxman Saha ◽  
Pratima Panigrahi ◽  
Pawan Kumar
Keyword(s):  
Lower Bound ◽  
Graph Coloring ◽  
Upper Bound ◽  
Channel Assignment ◽  
Assignment Problem ◽  
Arbitrary Graph ◽  
Communication Problem ◽  
Channel Assignment Problem ◽  
Radio Number

A number of graph coloring problems have their roots in a communication problem known as the channel assignment problem. The channel assignment problem is the problem of assigning channels (non-negative integers) to the stations in an optimal way such that interference is avoided, see Hale [4]. The radio coloring of a graph is a special type of channel assignment problem. Here we develop a technique to find an upper bound for radio number of an arbitrary graph and also we give a lower bound for the same. Applying these bounds we have obtained radio number of [Formula: see text], r ⩾ 3, for several values of n and r. Moreover for diameter 2 or 3 radio number of [Formula: see text] have been determined completely for all values of n and r.

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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