TY - GEN

T1 - A proposal of a quasi-solution state evolution algorithm for channel assignment problems

AU - Funabiki, Nobuo

AU - Nakanishi, Toru

AU - Yokohira, Tokumi

AU - Tajima, Shigeto

AU - Higashino, Teruo

N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.

PY - 2002

Y1 - 2002

N2 - The channel assignment problem (CAP) in a cellular network requires finding a channel assignment to the call requests from cells such that three types of interference constraints are not only satisfied, but also the number of channels (channel span) is minimized. This paper presents a three-stage iterative algorithm, called the Quasi-solution state evolution algorithm for CAP (QCAP). QCAP evolutes quasi-solution states where a subset of call requests is assigned channels and no more request can be satisfied without violating the constraint. The first stage computes the lower bound on the channel span. After the second stage greedily generates an initial quasi-solution state, the third stage evolutes them for a feasible solution by iteratively generating best neighborhoods, with help of the dynamic state jump and the gradual span expansion for global convergence. The performance is evaluated through solving benchmark instances in literature, where QCAP always finds the optimum or near-optimum solution in very short time.

AB - The channel assignment problem (CAP) in a cellular network requires finding a channel assignment to the call requests from cells such that three types of interference constraints are not only satisfied, but also the number of channels (channel span) is minimized. This paper presents a three-stage iterative algorithm, called the Quasi-solution state evolution algorithm for CAP (QCAP). QCAP evolutes quasi-solution states where a subset of call requests is assigned channels and no more request can be satisfied without violating the constraint. The first stage computes the lower bound on the channel span. After the second stage greedily generates an initial quasi-solution state, the third stage evolutes them for a feasible solution by iteratively generating best neighborhoods, with help of the dynamic state jump and the gradual span expansion for global convergence. The performance is evaluated through solving benchmark instances in literature, where QCAP always finds the optimum or near-optimum solution in very short time.

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U2 - 10.1007/3-540-45801-8_4

DO - 10.1007/3-540-45801-8_4

M3 - Conference contribution

AN - SCOPUS:84937434967

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 32

EP - 41

BT - Information Networking

A2 - Chong, Ilyoung

PB - Springer Verlag

T2 - International Conference on Information Networking, ICOIN 2002

Y2 - 30 January 2002 through 1 February 2002

ER -