Abstract
In this chapter, we investigate the application of Mean Field Annealing (MFA) neural networks to the optimum multiuser detection in a direct-sequence code-division multiple-access (DS/CDMA) system over the additive white Gaussian noise (AWGN) channel. Although the optimum receiver for multi-user detection is superior to the conventional matched filter receiver when the relative powers of the interfering signals are large, the optimum receiver obtained by the maximization of a log-likelihood function has a complexity that is exponential in the number of users. This prohibitive complexity has spawned the area of research on employing neural network techniques to develop an optimum detector with moderate complexity. In this chapter, it is shown that the energy function of the neural network can be derived from and is then expressed in terms of the likelihood function of the optimum multi-user detection for both the synchronous and asynchronous CDMA systems. An MFA network, which combines characteristics of the simulated annealing algorithm and the Hopfield neural network is proposed to seek out the global optimum solution of this energy function. Additionally, MFA exhibits the rapid convergence of the neural network while preserving the solution quality afforded by the stochastic simulated annealing algorithm. This would lead to a cost-effective and efficient minimization mechanism for CDMA multiuser detection. Computer simulation carry out performance comparisons among optimum detection, matched filter detection and MFA detection.
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References
A. J. Viterbi: “CDMA, Principle of Spread Spectrum Communication”, Addison-Wesley Company, NY, 1995.
A. Duel-Hallen, J. Holtzman, Z. Zvonar: “Multiuser detection CDMA systems”, IEEE Personal Communication, vol. 2, no. 2, pp. 46–58, April 1995.
R. Lupas, S. Verdu: “Linear multiuser detectors for synchronous code-division multiple access channels”, IEEE Trans. Inform. Theory, vol. 35, no. 1, pp. 123–136, Jan. 1989.
R. Lupas, S. Verdu: “Near-far resistance of multiuser detectors in asynchronous channels”, IEEE Trans. Commun., vol. 38, no. 4, pp. 496508, April 1990.
M. K. Varanasi, B. Aazhang: “Near-optimum detection in synchronous code-division multiple-access systems”, IEEE Trans. Commun., vol. 39, no. 5, pp. 725–736, May 1991.
S. Verdu: “Computational complexity of optimum multiuser detection”, Algorithmica, vol. 4, no. 3, pp. 303–312, 1989.
B. Aazhang et al.: “Neural networks for multiuser detection in code-division multiple-access communications”, IEEE Trans. Commun., vol. 40, no. 7, pp. 1211–1222, July 1992.
U. Mitra, H. V. Poor: “Neural network techniques for adaptive multiuser demodulation”, IEEE J. Select. Areas on Commun., vol. 12, no. 9, pp. 1460–1470, Dec. 1994.
T. Miyajima et al.: “On the multiuser detection using a neural network in code-division multiple-access communications”, IEICE Trans. Commun., vol. E76-B, no. 8, August 1993.
G. Kechriotis, E. S. Manolakos: “Implementing the optimum CDMA multiuser detector with Hopfield neural networks”, Proc. of the Intl Workshop on Applications of Neural Networks to Telecommunications, pp. 60–67, Princeton, NJ, Oct. 1993.
J. J. Hopfield, D. W. Tank: “Neural computation of decisions in optimization problems”, Biol. Cybernetics, vol. 52, pp. 141–152, 1985.
P. J. M. Van Laarhoven, E. H. L. Arts: “Simulated Annealing: Theory and Application”, Dordrecht, Holland: Reidel, 1987.
D. E. Goldberg: “Genetic Algorithms in Search, Optimization, and Machine Learning”, Reading, MA: Addison-Wesley, 1989.
D. E. Van den Bout, T. K. Miller III: “Graph partition using annealing networks”, Complex System, vol. 1, no. 2, pp. 192–203, June, 1990.
C. Peterson: “A mean field theory learning algorithm for neural networks”, Complex System, vol. 1, pp. 995–1019, 1987.
C. Peterson: “Neural networks and NP-complete optimization problem: a performance study on the graph bisection problem”, Complex System, vol. 2, pp. 59–89, 1988.
C. Peterson: “A new method for mapping optimization problem onto neural networks”, Int’l J. Neural System, vol. 1, no. 1, pp. 3–22, 1989.
C. Peterson, E. Hartman: “Explorations of the mean field theory learning algorithm”, Neural Networks, vol. 2, pp. 475–494, 1989.
G. Bilbro, R. Mann et al.: “Optimization by mean field annealing”, Advances in Neural Information Processing System 1.
G. H. Golub, C. F. V. Load: “Matrix Computation”, 2nd edition, The Johns Hopkins University Press, Baltimore, 1989.
A. Cichocki, R. Unbenauen: “Neural Networks for Optimization and Signal Processing”, John Wiley and Son, NY, 1993.
W. C. Y. Lee: “Power control in CDMA”, Proc. Veh. Technol. Conference, 1991, pp. 77–80.
M. B. Pursley, H. F. Roefs: “Numerical evaluation of correlation parameters for optimum phases of binary register sequences”, IEEE Trans. Commun., vol. COM-27, pp. 1593–1604, Oct. 1979.
J. A. Alspector et al.: “Experimental evaluation of learning in a neural microsystem”, in Advances in Neural Information Processing System 1. (J. E. Moody et al. ed.), pp. 871–878, San Mateo CA, Morgan Kaufmanns, 1992.
T. Miyajima, T. Hasegawa: “Multiuser detection using a Hopfield network for asynchronous code-division multiple-access systems”, IEICE Trans. on Fundamentals, vol. E-79 A, no. 12, Dec. 1996.
R. J. Glauber: “Time-dependent statistics of the Ising model”, Journal of Mathematical Physics, vol. 4, pp. 481–485, 1963.
M. Mezard, G. Parisi, M. Virasoro: “Spin glass theory and beyond”, Singapore: World Scientific, 1987.
C. Mead: “Analog VLSI and VLSI system”, New York: Addison-Wesly, 1989.
B. W. Lee, B. J. Sheu: “General-purpose neural chips with electrically programmable synapses and gain-adjust neurons”, IEEE Journal of Solid-State Circuits, vol. 27, no. 9, pp. 1299–1302, Sept. 1992.
A. D. Culhane, M. C. Peckerar, C. R. K. Marrian: “A neural net approach to discrete Hartley and Fourier transform”, IEEE Trans. on Circuits and Systems, vol. 36, pp. 695–702, May 1989.
J. F. Wakerly: “Digital Design, Principles and Practices”, 2nd edition, New Jersey: Prentice-Hall Inc., 1994.
N. H. E. Weste, K. Eshraghian: “Principles of CMOS VLSI Design”, 2nd edition, MA: Addison-Wesley, 1993.
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Chang, PR., Wang, BC., Tan, TH. (2001). Optimum Multiuser Detection for CDMA Systems Using the Mean Field Annealing Neural Network. In: Teodorescu, HN., Jain, L.C., Kandel, A. (eds) Hardware Implementation of Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 74. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1816-1_6
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DOI: https://doi.org/10.1007/978-3-7908-1816-1_6
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