Modified leaky LMS algorithm for channel estimation in DS-CDMA systems

• Published in: IEEE communications letters Vol. 6; no. 5; pp. 202 - 204
• Main Authors: Sungkwon Jo, Jihoon Choi, Lee, Y.H.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Algorithms; Applied sciences; Channel estimation; Channels; Computer simulation; Convergence; Detection, estimation, filtering, equalization, prediction; Equipments and installations; Exact sciences and technology; Filtering algorithms; Finite impulse response filter; Information, signal and communications theory; Least squares approximation; Mean square error methods; Mean square values; Mobile radiocommunication systems; Multiaccess communication; Optimization; Radiocommunications; Signal and communications theory; Signal, noise; Statistics; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transmission and modulation (techniques and equipments); Wiener filter; Wiener filtering; Performance evaluation; Wireless telecommunication; Transmission channel; Algorithm; Adaptive method; Wiener filter; Simulation; Channel estimation; Code division multiple access; Finite impulse response filter; System identification; Direct sequence; Least mean squares methods
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/4234.1001664

A simple adaptive least mean square (LMS) type algorithm for channel estimation is developed based on certain modifications to finite-impulse response (FIR) Wiener filtering. The proposed algorithm is nearly blind since it does not require any training sequence or channel statistics, and it can be implemented using only noise variance knowledge. A condition guaranteeing the convergence of the algorithm and theoretical mean square error (MSE) values are also derived. Computer simulation results demonstrate that the proposed algorithm can yield a smaller MSE than existing techniques, and that its performance is close to that of optimal Wiener filtering.