Wiener LMS Research Articles

Real-time DSP and FPGA Implementation of Wiener LMS Based Multipath Channel Estimation in 3G CDMA Systems

This paper investigates real-time DSP and FPGA implementations of a low complexity technique for asynchronous multiuser delay acquisition and time varying channel tracking for multipath channels in WCDMA and cdma2000 systems. A multiuser-LMS-like structure along with smoothing/prediction filters to improve tracking quality is reviewed. We investigate an efficient implementation based on FFT/IFFT technique, under fixed-point data representation and computation constraint. The measured BER reveals that fixed-point implementation is feasible at possibly no performance degradation. Based on real time execution made on a fixed-point high performance DSP, the maximum number of users is 15 and 17 for the proposed method and correlator, respectively. Due to the inherent parallelism and regular data flow FPGA implementation is suggested wherein a maximum number of users more than 80 can be afforded in Xilinx Virtex? II Pro device.

Adaptive algorithm for ternary filtering

Despite their major advantage of hardware simplicity, ternary filters have limited usability in practice owing to their unresolved problem of adaptivity. This challenging problem is tackled by introducing an adaptive ternary LMS-like algorithm. Performance assessment using a sinusoidal input distorted by additive white Gaussian noise showed that the proposed algorithm is comparable to the traditional multi-bit Wiener LMS algorithm. This approach is expected to open the door for ternary systems to be ready for replacing multi-bit signal processing systems.

Tracking of time-varying mobile radio channels. II. A case study

For pt.I see ibid., vol.49, p.2207-17 (2001). Low-complexity Wiener LMS (WLMS) adaptation algorithms, of use for channel estimation, have been derived in Lindbom et al. (2001). They are here evaluated on the fast fading radio channels encountered in IS-136 TDMA systems, with the aim of clarifying several issues: How much can channel estimation performance be improved with these tools, as compared to LMS adaptation? When can an improved tracking MSE be expected to result in a meaningful reduction of the bit error rate? Will optimal prediction of future channel estimates significantly improve the equalization? Can one single tracker with fixed gain be used for all encountered Doppler frequencies and SNRs, or must a more elaborate scheme be adopted? These questions are here investigated both analytically and by simulation. An exact analytical expression for the tracking MSE on two-tap FIR channels is presented and utilized. With this tool, the MSE performance and robustness of WLMS algorithms based on different statistical models can be investigated. A simulation study then compares the uncoded bit error rate of detectors, where channel trackers are used in decision directed mode in conjunction with Viterbi algorithms. A Viterbi detector combined with WLMS, based on second order autoregressive fading models possibly combined with integration, provides good performance and robustness at a reasonable complexity.

Tracking of time-varying mobile radio channels .1. The Wiener LMS algorithm

Adaptation algorithms with constant gains are designed for tracking smoothly time-varying parameters of linear regression models, in particular channel models occurring in mobile radio communications. In a companion paper, an application to channel tracking in the IS-136 TDMA system is discussed. The proposed algorithms are based on two key concepts. First, the design is transformed into a Wiener filtering problem. Second, the parameters are modeled as correlated ARIMA processes with known dynamics. This leads to a new framework for systematic and optimal design of simple adaptation laws based on prior information. The algorithms can be realized as Wiener filters, called learning filters, or as "LMS/Newton" updates complemented by filters that provide predictions or smoothing estimates. The simplest algorithm, named the Wiener LMS, is presented. All parameters are here assumed governed by the same dynamics and the covariance matrix of the regressors is assumed known. The computational complexity is of the same order of magnitude as that of LMS for regressors which are either white or have autoregressive statistics. The tracking performance is, however, substantially improved.