A Cooperative Co-evolutionary Approach for Large Scale Frequency Assignment Problem in TD-SCDMA
Authors: 
Chao Yang, Bin Jiang, Shuming PengAuthors Info & Claims
Chao Yang, Bin Jiang, Shuming PengAuthors Info & ClaimsPages 129 - 136
Abstract
This paper studies the large scale frequency assignment problem (FAP) in TD-SCDMA network of mobile communications industry in China. The problem considers finding the optimal frequency allocation scheme for carriers with a limited frequency resource in a fast time, such that the entire network interference is minimized. Furthermore, the allocation of frequencies needs to satisfy some constraints to avoid the effect of call interference within the same cell or adjacent cell. Given the formula for calculation of the network interference, we take the FAP as a constrained optimization problem and propose a cooperative co-evolutionary hyper-heuristic genetic algorithm (CC-HHGA) to optimize the assignment of frequencies. In this algorithm, the whole problem is divided into many sub-problems and each sub-problem is dealt with HHGA in parallel. Intensive experiments have been conducted on a real instance of TD-SCDMA network, and the obtained solutions show low interference, robustness and high utilization efficiency of frequencies, thus it is a high quality solution.
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Index Terms
- A Cooperative Co-evolutionary Approach for Large Scale Frequency Assignment Problem in TD-SCDMA
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Information & Contributors
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Published In
September 2017
269 pages
ISBN:9781450353526
DOI:10.1145/3127404
- General Chairs:
- Zili Zhang,
- Ning Gu,
- Program Chairs:
- Shaozi Li,
- Tun Lu,
- Li Li
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Association for Computing Machinery
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Publication History
Published: 22 September 2017
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- Research-article
- Research
- Refereed limited
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- the Natural Science Foundation of Hunan Province, China
Conference
ChineseCSCW '17
ChineseCSCW '17: Chinese Conference on Computer Supported Cooperative Work and Social Computing
September 22 - 23, 2017
Chongqing, China
Acceptance Rates
ChineseCSCW '17 Paper Acceptance Rate 21 of 84 submissions, 25%;
Overall Acceptance Rate 21 of 84 submissions, 25%
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