Approximate and exact ML detectors for CDMA and MIMO systems: a tree detection approach
Abstract
This paper deals with Direct-Sequence Code Division Multiple Access (DS-CDMA) transmissions over mobile radio channels. Different detection techniques have been proposed in the past years, among which approximate ML detectors. In this paper we propose an exact ML detector with a low complexity. We show that a QR factorization of the matrix of users’ signatures is appropriate: the upper triangular form of the R matrix makes it possible to state the ML detection in terms of a shortest path detection in a tree diagram. Different algorithms based on the tree diagram are compared: the stack algorithm (exact ML), and the feedback decoding algorithm (approximate ML). The numerical complexity of the proposed techniques is studied in detail; in particular, the low complexity of the stack algorithm at high SNR is pointed out. Simulations show that the performance of the stack algorithm in terms of Bit Error Rate (BER) are very close to the single user bound. Furthermore, we point out the fact that the same kind of approach can be used to perform ML detection in Multiple Input Multiple Output (MIMO) systems.
Keywords
Binary Error Rate Minimum Mean Square Error Multiple Input Multiple Output Successive Interference Cancellation Numerical ComplexityPreview
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References
- [1]G. Foschini. Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas. Bell Labs Technical Journal, pages 41–59, 1996.Google Scholar
- [2]R. V. G.D. Golden, G.J. Foschini and P. Wolniansky. Detection algorithm and initial laboratory results using v-blast space-time communication architecture. Electronic Letters, 35: 14–16, January 1999.CrossRefGoogle Scholar
- [3]G. Golub and C. Loan. Matrix Computations. The Johns Hopkins University Press, Baltimore, Maryland, 1984.Google Scholar
- [4]J. Heller. Advances in Communication Systems, volume 4, chapter Feedback decoding of convolutional codes. A.J. Viterbi, New York, academic edition, 1975.Google Scholar
- [5]F. Jelinek. Fast sequential decoding algorithm using a stack. IBM Jour. Res. Dev., 13: 675–685, November 1969.MathSciNetzbMATHCrossRefGoogle Scholar
- [6]M. Junti. Multiuser demodulation for DS-CDMA systems in fading channels. PhD thesis, Oulu University, Department of Electrical Engineering, Oulu, Finland, 1998.Google Scholar
- [7]I. Medvedev and V. Tarokh. A channel shortening multiuser detector for dscdma systems. In VTC’01, Rhodes, April 2001.Google Scholar
- [8]J. G. Proakis. Digital Communication. New York McGraw-Hill, second edition, 1989. 193Google Scholar
- [9]L. Rasmussen, T. Lim, and T. Aulin Breadth-first maximum likelihood detection in multiuser cdma. IEEE Trans. on Comm.,45(10):1176–1178, oct. 1997.Google Scholar
- [10]L. Rasmussen, T. Lim, and A. Johanson. A matrix-algebraic approach to successive interference cancellation in CDMA. IEEE Transactions on Communications, 48 (1): 145–151, January 2000.CrossRefGoogle Scholar
- [11]S. Verdi. Minimum probability of error for asynchronous gaussian multiple-access channels. IEEE Trans. Information Theory, IT-32:85–96, Januray 1986.Google Scholar
- [12]S. Verdú. Optimum multiuser asymptotic efficiency. IEEE Trans. Communications, COM-34:890–897, sept 1986.Google Scholar
- [13]S. Verdu. Multiuser Detection. Cambridge University Press, 1998.Google Scholar
- [14]A. Wittneben. A new bandwidth efficient transmit antenna modulation diversity scheme for linear digital modulation. In ICC’98, pages 1630–1634, 1993.Google Scholar
- [15]Z. Xie, C. Rushforth, and R. Short. Multiuser signal detection using sequential decoding. IEEE Trans. on Comm.,38(5):578–583, may 1990.Google Scholar
- [16]K. Zigangirov. Some sequential decoding procedures. Probl. Peredach. Inform., 2: 13–25, 1966.Google Scholar