A three-stage heuristic combined neural-network algorithm for channel assignment in cellular mobile systems

• Published in: IEEE Trans. Vehicular Technology Vol. 49; no. 2; pp. 397 - 403
• Main Authors: Funabiki, N., Okutani, N., Nishikawa, S.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2000; Institute of Electrical and Electronics Engineers (IEEE); Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Benchmarking; Cellular; Cellular networks; Cellular neural networks; Channels; Equipments and installations; Exact sciences and technology; Frequency; Heuristic; Heuristic algorithms; Intelligent networks; Interference constraints; Intervals; Iterative algorithms; Lower bounds; Mobile communication; Mobile communication systems; Mobile radiocommunication systems; Neural networks; NP-complete problem; Radiocommunications; Studies; Telecommunications; Telecommunications and information theory; Mobile radiocommunication; Wireless telecommunication; Resource allocation; Heuristic method; Transmission channel; Cell network; Neural network; Algorithm
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/25.832970

A three-stage algorithm of combining sequential heuristic methods into a parallel neural network is presented for the channel assignment problem in cellular mobile communication systems in this paper. The goal of this NP-complete problem is to find a channel assignment to requested calls with the minimum number of channels subject to interference constraints between channels. The three-stage algorithm consists of: (1) the regular interval assignment stage; (2) the greedy assignment stage; and (3) the neural-network assignment stage. In the first stage, the calls in a cell determining the lower bound on the total number of channels are assigned channels at regular intervals. In the second stage, the calls in a cell with the largest degree and its adjacent cells are assigned channels by a greedy heuristic method. In the third stage, the calls in the remaining cells are assigned channels by a binary neural network. The performance is verified through solving well-known benchmark problems. Especially for Sivarajan's benchmark problems, our three-stage algorithm first achieves the lower bound solutions in all of the 13 instances, while the computation time is comparable with existing algorithms.