Improved Algorithm for Minimum Cost Range Assignment Problem for Linear Radio Networks

2004 ◽  
pp. 412-423 ◽  
Author(s):  
Gautam K. Das ◽  
Sasthi C. Ghosh ◽  
Subhas C. Nandy
Keyword(s):  
Assignment Problem ◽  
Minimum Cost ◽  
Radio Networks ◽  
Range Assignment ◽  
Improved Algorithm

IMPROVED ALGORITHM FOR MINIMUM COST RANGE ASSIGNMENT PROBLEM FOR LINEAR RADIO NETWORKS

2007 ◽  
Vol 18 (03) ◽  
pp. 619-635 ◽  
Author(s):  
GAUTAM K. DAS ◽  
SASTHI C. GHOSH ◽  
SUBHAS C. NANDY
Keyword(s):  
Power Consumption ◽  
Assignment Problem ◽  
Minimum Cost ◽  
Radio Networks ◽  
Incremental Algorithm ◽  
Total Power ◽  
Radio Stations ◽  
Range Assignment ◽  
Total Power Consumption ◽  
Optimum Solution

In the unbounded version of the range assignment problem for all-to-all communication in 1D, a set of n radio-stations are placed arbitrarily on a line; the objective is to assign ranges to these radio-stations such that each of them can communicate with the others (using at most n - 1 hops) and the total power consumption is minimum. A simple incremental algorithm for this problem is proposed which produces optimum solution in O(n3) time and O(n2) space. This is an improvement in the running time by a factor of n over the best known existing algorithm for the same problem.

The Power Range Assignment Problem in Radio Networks on the Plane

2000 ◽  
pp. 651-660 ◽  
Author(s):  
Andrea E. F. Clementi ◽  
Paolo Penna ◽  
Riccardo Silvestri
Keyword(s):  
Assignment Problem ◽  
Radio Networks ◽  
Range Assignment

The Minimum Range Assignment Problem on Linear Radio Networks

Algorithmica ◽  
2003 ◽  
Vol 35 (2) ◽  
pp. 95-110 ◽  
Author(s):  
E. F. Clementi ◽  
Penna ◽  
Ferreira ◽  
Perennes ◽  
Silvestri
Keyword(s):  
Assignment Problem ◽  
Radio Networks ◽  
Range Assignment

scholarly journals On the approximability of the range assignment problem on radio networks in presence of selfish agents

2005 ◽  
Vol 343 (1-2) ◽  
pp. 27-41 ◽  
Author(s):  
Christoph Ambühl ◽  
Andrea E.F. Clementi ◽  
Paolo Penna ◽  
Gianluca Rossi ◽  
Riccardo Silvestri
Keyword(s):  
Assignment Problem ◽  
Radio Networks ◽  
Range Assignment ◽  
Selfish Agents

The Minimum Range Assignment Problem on Linear Radio Networks

2000 ◽  
pp. 143-154 ◽  
Author(s):  
A. E. F. Clementi ◽  
A. Ferreira ◽  
P. Penna ◽  
S. Perennes ◽  
R. Silvestri
Keyword(s):  
Assignment Problem ◽  
Radio Networks ◽  
Range Assignment

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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