Radio Channel Allocations With Global Optimality and Bounded Computational Scale

The radio channel assignment (RCA) in wireless networks is an optimization problem that is often found NP-complete. For networks of practical sizes, various heuristic algorithms are used to solve it. However, there are two major issues: finding a globally optimized solution without relying on specif...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 63; no. 9; pp. 4670 - 4680
Main Authors: Yu, Ming, Ma, Xiaoguang, Zhou, Mengchu
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Channel allocation
Channel estimation
Channels
Complexity
Computation
Convergence
Estimating
Heuristic
Interference
Linear programming
Mathematical analysis
Mathematical models
Networks
Optimization
Vectors
Wireless networks
wireless networks
radio channel allocation
simulated annealing (SA)
Gibbs sampling
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary:The radio channel assignment (RCA) in wireless networks is an optimization problem that is often found NP-complete. For networks of practical sizes, various heuristic algorithms are used to solve it. However, there are two major issues: finding a globally optimized solution without relying on specific interference models and estimating the computational complexity of general heuristic algorithms. In this paper, we propose a new simulated annealing (SA)-based RCA (SRCA) algorithm to find the globally optimized channel assignment in a distributed way but with bounded computational complexity. We propose using effective channel utilization (ECU) as the evaluation vector, whereas the objective function is to maximize the total ECU in a neighborhood. The ECU can be easily calculated by an access point (AP). The impact of interference is included in the ECU. We propose a hybrid method for estimating the algorithm's computational scale (CS), i.e., the number of channel reallocations until the network reaches a convergence state, by combining analytical and experimental methods. The resulting algorithm is a dynamic and distributed algorithm. Our extensive simulation results have demonstrated that it quickly achieves 99% of the global maximum with a chance over 95%, whereas its complexity is linear with the number of routers in the network.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2014.2311922