Abstract
In this paper, we consider an improved and efficient algorithm for the multiuser detection (MUD) in direct sequence/code division multiple access (DS/CDMA) communication systems. The optimum detector for MUD is the maximum likelihood (ML) detector, but its complexity is very high and it calls for an optimization problem that involves an exhaustive search. Consequently, there has been considerable interest in suboptimal multiuser detectors with less complexity and reasonable performance. The idea of reducing the complexity by sub-optimum detectors was efficient and applicable but had some defects. The idea proposed in this paper is the restriction of the search space. This idea comprises using a sub-optimum detector such as ordinary genetic algorithm for which the size of the search space is confined to a predetermined region that is smaller than the whole search space. This limited region includes the objective bit sequence. Then by using depth-first tree search algorithm, we could find the optimum bit stream. We compare our algorithm to the ML detector, the genetic algorithm with conventional detector, and the ant colony optimization (ACO) detector which have been used for MUD in DS/CDMA. Simulation results show that the performance of this algorithm is near the optimal detector with very low complexity, and works better in comparison to other algorithms.
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References
Lupas, R., & Verdu, S. (1989). Linear multiuser detectors for synchronous code division multiple-access channels. IEEE Transactions on Information Theory, 35, 123–136.
Verdu, S. (1986). Minimum probability of error for asynchronous Gaussian multiple access channels. IEEE Transactions on Information Theory, 32, 85–96.
Juntti, M. J., Schlosser, T., & Lilleberg, J. O. (1997). Genetic algorithms for multiuser detection in synchronous CDMA. In Proceedings of IEEE international symposium on information theory. (p. 492).
Ergun, C., & Hacioglu, K. (2000). Multiuser detection using a genetic algorithm in CDMA communications systems. IEEE Transactions on Communications, 48, 522–561.
Yan-Fei, Y., & Yuan-Ping, Zh. (2008). Immune-endocrine genetic algorithm for multi-user detector problem. In International conference on computer science and software engineering (pp. 447–450).
Zahedi, A., Bakhshi, H. R., Jafari, S., Abdolmohammadi, H. R., & Rajati, M. R. (2013). A novel low complexity multiuser detector based on modified genetic algorithm in direct sequence-code division multiple access communication systems. International Journal of Science and Technology (Scientia Iranica), 20(6), 2015–2023.
Tang, P. Y., Li, Z. H., & Huang, S. J. (2004). Multiuser detector based on genetic algorithm and Tabu search. Journal of University of Electronic Science and Technology of China, Chengdu, 5, 499–509.
Jiang, M., Li, Ch., Yuan, D., & Lagunas, M. A. (2007). Multiuser detection based on wavelet packet modulation and artificial fish swarm algorithm. In IET conference on wireless, mobile and sensor network, (CCWMSN07), China (pp. 117–120).
Lin, L. (2008). A novel genetic multi-user receiver based on wavelet transform and Hamming sphere solution space. In 4th IEEE international conference on circuits and systems for communications, ICCSC 2008 (pp. 260–264).
Zhao, Y., & Zheng, J. (2005). Multiuser detection employing particle swarm optimization in space-time CDMA systems. In Proceedings of ISCIT2005 (pp. 940–942).
El-Mora, H. H., Sheikh, A. U., & Zerguine, A. (2005). Application of particle swarm optimization algorithm to multiuser detection in CDMA. In Proceedings of IEEE 16th PIMRC (vol. 4, pp. 2522–2526).
Hongwu, L. (2009). An ACO based multiuser detection for receive-diversity aided STBC systems. In ISECS international colloquium on computing, communication, control, and management, CCCM 2009 (pp. 250–253).
Chong, X., Maunder, R. G., Yang, L. L., & Hanzo, L. (2009). Near-optimum multiuser detectors using soft-output ant-colony-optimization for the DS-CDMA uplink. IEEE Signal Processing Letters, 16, 137–140.
Chen, S., Samingan, A. K., & Hanzo, L. (2005). Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems. Signal processing, 85(7), 1435–1448.
Mohammadi, M., Ardebilipour, M., Moussakhani, B., & Mobini, Z. (2008). Performance comparison of RLS and LMS channel estimation techniques with optimum training sequences for MIMO–OFDM Systems. In 5th IEEE International conference on wireless and optical communication networks (WOCN’2008), Surabaya, Indonesia (pp. 1–5).
Coulon, M., & Roviras, D. (2008). Adaptive detection for a differential Chaos-based multiple access system on unknown multipath fading channels. In International conference on acoustics, speech, and signal processing (ICASSP 2008), Las Vegas, Nevada, USA (pp. 3485–3488).
Gajbhiye, M. R. ( 2010). Adaptive MMSE-MRC multiuser detector with mixed phase channel for DS-CDMA system. In IEEE 4th international symposium on advanced network and telecommunication systems (ANTS) (pp. 100–102).
D‘Orazio, L., Sacchi, C., Louveaux, J., & Vandendorpe, L. (2010). Adaptive minimum conditional bit-error-rate linear multiuser detection for STBC-MC-CDMA systems transmitting over mobile radio channels. In Proceedings of 3rd international workshop on multiple access communications (MACOM 2010), Springer (Berlin) (pp. 36–46).
Dietl, G., & Utschick, W. (2007). Complexity reduction of iterative receivers using low-rank equalization. IEEE Transactions on Signal Processing, 55(3), 1035–1046.
Honig, M. L., Woodward, G. K., & Sun, Y. (2004). Adaptive iterative multi-user decision feedback detection. IEEE Transactions on Wireless Communications, 3(2), 477–485.
Lončar, M., Müller, R. R., Wehinger, J., Mecklenbräuker, C. F., & Abe, T. (2004). Iterative channel estimation and data detection in frequency selective fading MIMO channels. European Transactions on Telecommunications, 15(5), 459–470.
Wehinger, J. (2005). Iterative Multi-User Receivers for CDMA Systems. In PhD thesis, Vienna University of Technology, Vienna, Austria.
Zemen, T., Mecklenbrauker, C. F., Wehinger, J., & Muller, R. R. (2006). Iterative joint time-variant channel estimation and multi-user detection for MC-CDMA. IEEE Transactions on Wireless Communications, 5(6), 1469–1478.
Agrell, E., Eriksson, T., Vardy, A., & Zeger, K. (2002). Closest point search in lattices. IEEE Transactions on Information Theory, 48(8), 2201–2214.
Damen, M. O., El Gamal, H., & Caire, G. (2003). On maximum-likelihood detection and the search for the closest lattice point. IEEE Transactions on Information Theory, 49(10), 2389–2402.
Wong, K. W., Tsiu, C. Y., Cheng, R. S. K., & Mow, W. H. (2002). A VLSI architecture of a K-best lattice decoding algorithm for MIMO channels. In IEEE international symposium on circuits and systems (ISCAS), Scottsdale, AZ, USA.
Murugan, A. D., Gamal, H. E., Damen, M. O., & Caire, G. (2006). A unified framework for tree search decoding: Rediscovering the sequential decoder. IEEE Transactions on Information Theory, 52(3), 933–953.
Brunel, L., & Boutros, J. J. (2003). Lattice decoding for joint detection in direct-sequence CDMA systems. IEEE Transactions on Information Theory, 49(4), 1030–1037.
Proakis, J. G. (1995). Digital Communications (3rd ed.). Singapore: McGraw-Hill.
Verdu, S. (1998). Multiuser detection. Cambridge: Cambridge University Press.
Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms (2nd ed.). London: Wiley.
Goldberg, D., & Reading, M. (1989). Genetic algorithm in search optimization and machine learning. Boston: Addison-Wesley.
Sivanandam, S. N., & Deepa, S. N. (2008). An Introduction to genetic algorithms. Heidelberg: Springer.
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Zahedi, A., Rajamand, S., Jafari, S. et al. A Novel Multiuser Detector Based on Restricted Search Space and Depth-First Tree Search Method in DS/CDMA Communication Systems. Wireless Pers Commun 82, 1531–1545 (2015). https://doi.org/10.1007/s11277-015-2297-2
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DOI: https://doi.org/10.1007/s11277-015-2297-2