Least Mean Square Estimator Research Articles

Capacity and BER performance improvement in integrated MIMO‐OFDM system using optimal power allocation, channel estimation, and turbo coding

SummaryAmong communication systems, multiple input multiple output (MIMO) system has the capability of providing spectral efficiency, high data rates, diversity gain, and improved reliability. Suitably designed orthogonal frequency division multiplexing (OFDM) transmits the signal without inter symbol interference and multipath fading. Hence, MIMO‐OFDM is a strong candidate for 4G and 5G wireless systems. However, variations in signal‐to‐noise ratio (SNR) due to the random nature of the channel and poor capacity at low SNR are critical issues in conventional MIMO‐OFDM system. This paper explains how optimal power allocation (OPA), channel estimation, and coding improve the capacity and bit error rate (BER) performance of MIMO‐OFDM system. In OPA, the transmitter power is distributed adaptively to the channels based on the SNR statistics using singular value decomposition and water filling algorithm. In case of low SNR, less number of channels are selected for power allocation. Least squares (LS), minimum mean square error (MMSE), and least mean square (LMS) estimators are considered in comb type pilot channel estimation while turbo code is used with code rates of 1/2 and 1/3. From the simulation results, capacity improvement of 5 b/s/Hz and 11 b/s/Hz is observed for MIMO‐OFDM systems with four antennas and eight antennas, respectively. It is also found that performance of LMS estimator improved by 3.5‐dB SNR over LS and 1 dB SNR over MMSE. In this paper is shown how LMS estimator and 1/3 turbo code have been integrated with MIMO‐OFDM, and this has resulted in around 4‐dB SNR improvement over conventional MIMO‐OFDM.

Blind Frequency Offset Estimation Using Adaptive Step-Size LMS Algorithm for OFDM Communications

In this Letter, we propose a least mean square (LMS) with adaptive step-size (AS) algorithm for adaptive blind carrier frequency offset (CFO) estimation in the orthogonal frequency division multiplexing system. In conjunction with the closed-loop estimate structure, the proposed algorithm eliminates the inter-carrier interference caused by time varying CFO. To improve the convergence performance of the fixed step-size LMS estimator, the regular AS LMS algorithm offers faster convergence speed and more accuracy to the CFO estimate. Several computer simulation examples are presented for illustrating the effectiveness of the proposed algorithm.

A performance bound for the LMS estimator

The least-mean-square (LMS) estimator is a nonlinear estimator with information dependencies spanning the entire set of data fed into it. The traditional analysis techniques used to model this estimator obscure these dependencies; to simplify the analysis they restrict the estimator to the finite set of data sufficient to span the length of its filter. Thus the finite Wiener filter is often considered a bound on the performance of the LMS estimator. Several papers have reported the performance of the LMS filter exceeding that of the finite Wiener filter. We derive a bound on the LMS estimator which does not exclude the contributions from data outside its filter length. We give examples of this bound in cases where the LMS estimator outperforms the finite Wiener filter.

Analysis of LMS-adaptive MLSE equalization on multipath fading channels

We consider a practical maximum-likelihood sequence estimation (MLSE) equalizer on multipath fading channels in conjunction with an adaptive channel estimator consisting of a least mean square (LMS) estimator and a linear channel predictor, instead of assuming perfect channel estimates. A new LMS estimator model is proposed which can accurately characterize the statistical behavior of the LMS estimator over multipath fading channels. Based on this model, a new upper-bound on block error rate is derived under the consideration of imperfect channel estimates. Computer simulations verify that our analytical results can correctly predict the real system performance and are applicable over a wide range of the step size parameter of the LMS estimator.

Analysis of adaptive LMS estimator with cyclic sequences in complex frequency domain

Cyclic sequences are usually employed as the reference sequences in the adaptive filtering systems for signal or channel estimation. In this situation, the stability of an adaptive filter can be analyzed by an equivalent transfer function in complex frequency domain. For several special classes of cyclic reference sequences, the stability bounds of the adaptive filter with least mean-square (LMS) algorithm and its variants, leaky-LMS (L-LMS) and minimum output variance LMS (MOV-LMS), are obtained. Effects of these algorithms on the resultant transfer functions and the stability bounds are also investigated.

An error bound for a noise canceller

The degradation in performance of the least-mean-square estimation (LMSE) is evaluated after replacing the covariance matrices with estimates. Taking advantage of the statistical properties of the complex Wishart matrix and its inverse, the decrease in performance stemming from this substitution is investigated. The performance criterion is based on the computation of the final output error, which includes covariance deviations. This is carried out for a certain class of linear observation models, involving an underlying problem of noise cancelling using noise alone references (NARs), namely, array processing techniques using a known array manifold. Performance limits of the adaptive noise canceller are investigated for the case of a spectral filter to illustrate the ideas. It is shown that its performance depends on the quadratic coherency between the corrupted signal and the NAR. It is preferable to run no processing when this coherency is not strong enough. A rule is proposed in order to decide when to switch to the no-processing scheme. Contrary to some previous results, the results given are available without any approximations, provided the statistical properties are satisfied. In the case of a special noise canceller, these properties are asymptotically satisfied. In the case of a transversal noise canceller acting in the time domain, these results are valid for white signals and noises.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>