Lars Lindbom

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.

Robust Wiener Design of Adaptation Laws with Constant Gains

Abstract Filters can be introduced into LMS-like adaptation algorithms to improve their tracking performance. This paper discusses the systematic model-based design of such filters. Parameter variations in coefficients of linear regression models are modeled as ARIMA-processes. The aim is to provide high performance filtering, prediction or fixed lag smoothing estimates for arbitrary lags. The properties of the time-varying parameters are in general not known exactly, so a robust design for a set of possible models will be of interest. We minimize the average tracking MSE, based on probabilistic descriptions of the model uncertainty. The method is based on a novel signal transformation that recasts the algorithm design into a robust Wiener filtering problem. The performance is illustrated on the tracking of mobile radio channels in IS-136 systems, based on a model of the time-variations affected by parametric uncertainty.

Automatic tuning of the step size in WLMS algorithms: applications to EDGE

Adaptation laws that estimate time-varying communication channels can be tuned for efficient tracking, by adjusting filters and gains within their structure. However, this requires knowledge of statistical properties that may be poorly known or time-varying. A possible approach is then to adjust the gains of the adaptation law adaptively. The Wiener LMS adaptation algorithm (WLMS) attains efficient tracking by incorporating filters that are attuned to the fading statistics. It includes a gain parameter that will be adjusted adaptively on line. A WLMS channel predictor with adaptively adjusted gain is then evaluated as a component of receivers for EDGE systems, which use delayed decision feedback sequence estimation. The channel variations for such cases have a wide variety of properties that are tracked by gain adaptation.