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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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 63; H. 9; S. 4670 - 4680
Hauptverfasser: Yu, Ming, Ma, Xiaoguang, Zhou, Mengchu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Abstract 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.
AbstractList 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.
Author Xiaoguang Ma
Mengchu Zhou
Ming Yu
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CitedBy_id crossref_primary_10_1109_TWC_2018_2859416
crossref_primary_10_1109_TNNLS_2015_2415257
crossref_primary_10_1109_TCYB_2016_2574766
crossref_primary_10_1109_TSMC_2015_2421484
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SubjectTerms Algorithms
Channel allocation
Channel estimation
Channels
Complexity
Computation
Convergence
Estimating
Heuristic
Interference
Linear programming
Mathematical analysis
Mathematical models
Networks
Optimization
Vectors
Wireless networks
Title Radio Channel Allocations With Global Optimality and Bounded Computational Scale
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