Improvement of borrowing channel assignment for patterned traffic load by online cellular probabilistic self-organizing map

This paper describes an improvement of borrowing channel assignment (BCA) for patterned traffic load by using the short-term traffic prediction ability of cellular probabilistic self-organizing map (CPSOM). The fast growing cellular mobile systems demand more efficient and faster channel allocation techniques today. In case of patterned traffic load, the traditional BCA methods are not efficient to further enhance the performance because heavy-traffic cells cannot borrow channels from their neighboring cells with light or medium traffic that may have unused nominal channels. The performance can be increased if the short-term traffic load can be predicted. The predicted results can then be used for channel re-assignment. Therefore, the unused nominal channels of the light-or-medium-traffic cells can be transferred to the heavy-traffic cells that need more nominal channels. In this paper, CPSOM is used online for traffic prediction. In this sense, the proposed CPSOM-based BCA method is able to enhance the performance for patterned traffic load compared with the traditional BCA methods. Simulation results corroborate that the proposed method enables the system to work with better performance for patterned traffic load than the traditional BCA methods.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[3]  Demessie Girma,et al.  A Hopfield neural-network-based dynamic channel allocation with handoff channel reservation control , 2000, IEEE Trans. Veh. Technol..

[4]  Lipo Wang,et al.  A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[6]  Harilaos G. Sandalidis,et al.  Borrowing channel assignment strategies based on heuristic techniques for cellular systems , 1999, IEEE Trans. Neural Networks.

[7]  Seong-Lyun Kim,et al.  A two-phase algorithm for frequency assignment in cellular mobile systems , 1994 .

[8]  Henry Leung,et al.  Prediction of noisy chaotic time series using an optimal radial basis function neural network , 2001, IEEE Trans. Neural Networks.

[9]  Ming Zhang,et al.  Comparisons of channel-assignment strategies in cellular mobile telephone systems , 1989 .

[10]  D. Kunz,et al.  Channel assignment for cellular radio using neural networks , 1991 .

[11]  Dietmar Kunz,et al.  Channel assignment for cellular radio using simulated annealing , 1993 .

[12]  William C. Y. Lee,et al.  Mobile Cellular Telecommunications Systems , 1989 .

[13]  Rudolf Mathar,et al.  Channel assignment in cellular radio networks , 1993 .

[14]  Alexander H. Waibel,et al.  Modular Construction of Time-Delay Neural Networks for Speech Recognition , 1989, Neural Computation.

[15]  Yoshiyasu Takefuji,et al.  A neural network parallel algorithm for channel assignment problems in cellular radio networks , 1992 .

[16]  Marimuthu Palaniswami,et al.  Static and Dynamic Channel Assignment Using Neural Networks , 1997, IEEE J. Sel. Areas Commun..

[17]  R. Montemanni,et al.  An ANTS algorithm for the minimum-span frequency-assignment problem with multiple interference , 2002, IEEE Trans. Veh. Technol..

[18]  Abraham O. Fapojuwo,et al.  A Viterbi-like algorithm with adaptive clustering for channel assignment in cellular radio networks , 2002, IEEE Trans. Veh. Technol..

[19]  Roberto Battiti,et al.  A randomized saturation degree heuristic for channel assignment in cellular radio networks , 2001, IEEE Trans. Veh. Technol..

[20]  James V. Hansen,et al.  Neural networks and traditional time series methods: a synergistic combination in state economic forecasts , 1997, IEEE Trans. Neural Networks.

[21]  C. Lee Giles,et al.  Learning Chaotic Attractors by Neural Networks , 2000, Neural Computation.

[22]  Y.-Y. Hsu,et al.  Short term load forecasting using a multilayer neural network with an adaptive learning algorithm , 1992 .

[23]  Antonio Capone,et al.  Channel assignment problem in cellular systems: a new model and a tabu search algorithm , 1999 .

[24]  E. Del Re,et al.  A dynamic channel allocation technique based on Hopfield neural networks , 1994, Proceedings of 1994 3rd IEEE International Conference on Universal Personal Communications.

[25]  Tommy W. S. Chow,et al.  Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong , 1997, Neural Computing & Applications.

[26]  Yen-Chang Chen,et al.  Flood forecasting using radial basis function neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[27]  T. Chow,et al.  Nonlinear autoregressive integrated neural network model for short-term load forecasting , 1996 .

[28]  Min Han,et al.  Prediction of chaotic time series based on the recurrent predictor neural network , 2004, IEEE Transactions on Signal Processing.

[29]  Romano Fantacci,et al.  A dynamic channel allocation technique based on Hopfield neural networks , 1996 .

[30]  Masakazu Sengoku,et al.  A new coloring problem of networks related to a channel assignment problem in cellular mobile systems , 1991, 1991., IEEE International Sympoisum on Circuits and Systems.

[31]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[32]  S.M. Elnoubi,et al.  A new frequency channel assignment algorithm in high capacity mobile communication systems , 1982, IEEE Transactions on Vehicular Technology.

[33]  Les E. Atlas,et al.  Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.

[34]  Marimuthu Palaniswami,et al.  Neural network-based dynamic channel assignment for cellular mobile communication systems , 1994 .

[35]  K. N. Chang.  An efficient borrowing channel assignmnet scheme for cellular mobile systems , 1998 .

[36]  Tommy W. S. Chow,et al.  Neural network based short-term load forecasting using weather compensation , 1996 .

[37]  M. M. Peritsky,et al.  Statistically-Optimum Dynamic Server Assignment in Systems with Interfering Servers , 1972, IEEE Trans. Commun..

[38]  Tommy W. S. Chow,et al.  An online cellular probabilistic self-organizing map for static and dynamic data sets , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[39]  Mahmoud Naghshineh,et al.  Channel assignment schemes for cellular mobile telecommunication systems: A comprehensive survey , 1996 .

[40]  Nobuo Funabiki,et al.  A three-stage heuristic combined neural-network algorithm for channel assignment in cellular mobile systems , 2000, IEEE Trans. Veh. Technol..

[41]  Ming-Wei Chang,et al.  Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 , 2004, IEEE Transactions on Power Systems.

[42]  Roberto Montemanni,et al.  An improved tabu search algorithm for the fixed-spectrum frequency-assignment problem , 2003, IEEE Trans. Veh. Technol..

[43]  T. Kahwa,et al.  A Hybrid Channel Assignment Scheme in Large-Scale, Cellular-Structured Mobile Communication Systems , 1978, IEEE Trans. Commun..

[44]  Victor O. K. Li,et al.  Fixed channel assignment in cellular radio networks using a modified genetic algorithm , 1998 .

[45]  Paris A. Mastorocostas,et al.  A recurrent fuzzy-neural model for dynamic system identification , 2002, IEEE Trans. Syst. Man Cybern. Part B.