LMS Based Adaptive Channel Estimation for LTE Uplink

In this paper, a variable step size based least mean squares (LMS) channel estimation (CE) algorithm is presented for a single carrier frequency division multiple access(SC-FDMA) system under the umbrella of the long term evolution (LTE). This unbiased CE method can automatically adapts the weighting coefficients on the channel condition. Therefore, it does not require knowledge of channel,and noise statistics. Furthermore, it uses a phase weighting scheme to eliminate the signal fluctuations due to noise and decision errors. Such approaches can guarantee the convergence towards the true channel coefficient. The mean and mean square behaviors of the proposed CE algorithm are also analyzed. With the help of theoretical analysis and simulation results, we prove that the proposed algorithm outperforms the existing algorithms in terms of mean square error (MSE) and bit error rate (BER) by more than around 2.5dB

Underwater acoustic communication over Doppler spread channels

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 297-307).by Trym H. Eggen.Ph.D

Estimation and tracking of rapidly time-varying broadband acoustic communication channels

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2006This thesis develops methods for estimating wideband shallow-water acoustic communication channels. The very shallow water wideband channel has three distinct features: large dimension caused by extensive delay spread; limited number of degrees of freedom (DOF) due to resolvable paths and inter-path correlations; and rapid fluctuations induced by scattering from the moving sea surface. Traditional LS estimation techniques often fail to reconcile the rapid fluctuations with the large dimensionality. Subspace based approaches with DOF reduction are confronted with unstable subspace structure subject to significant changes over a short period of time. Based on state-space channel modeling, the first part of this thesis develops algorithms that jointly estimate the channel as well as its dynamics. Algorithms based on the Extended Kalman Filter (EKF) and the Expectation Maximization (EM) approach respectively are developed. Analysis shows conceptual parallels, including an identical second-order innovation form shared by the EKF modification and the suboptimal EM, and the shared issue of parameter identifiability due to channel structure, reflected as parameter unobservability in EKF and insufficient excitation in EM. Modifications of both algorithms, including a two-model based EKF and a subspace EM algorithm which selectively track dominant taps and reduce prediction error, are proposed to overcome the identifiability issue. The second part of the thesis develops algorithms that explicitly find the sparse estimate of the delay-Doppler spread function. The study contributes to a better understanding of the channel physical constraints on algorithm design and potential performance improvement. It may also be generalized to other applications where dimensionality and variability collide.Financial support for this thesis research was provided by the Office of Naval Research and the WHOI Academic Program Office

A channel subspace post-filtering approach to adaptive equalization

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2002One of the major problems in wireless communications is compensating for the time-varying intersymbol interference (ISI) due to multipath. Underwater acoustic communications is one such type of wireless communications in which the channel is highly dynamic and the amount of ISI due to multipath is relatively large. In the underwater acoustic channel, associated with each of the deterministic propagation paths are macro-multipath fluctuations which depend on large scale environmental features and geometry, and micro-multipath fluctuations which are dependent on small scale environmental inhomogeneities. For arrivals which are unsaturated or partially saturated, the fluctuations in ISI are dominated by the macro-multipath fluctuations resulting in correlated fluctuations between different taps of the sampled channel impulse response. Traditional recursive least squares (RLS) algorithms used for adapting channel equalizers do not exploit this structure. A channel subspace post-filtering algorithm that treats the least squares channel estimate as a noisy time series and exploits the channel correlation structure to reduce the channel estimation error is presented. The improvement in performance of the algorithm with respect to traditional least squares algorithms is predicted theoretically, and demonstrated using both simulation and experimental data. An adaptive equalizer structure that explicitly uses this improved estimate of the channel impulse response is discussed. The improvement in performance of such an equalizer due to the use of the post-filtered estimate is also predicted theoretically, and demonstrated using both simulation and experimental data.This research was supported by an ONR Graduate Traineeship Award Grant #N00014-00-10049

Analysis of diversity and equalization techniques applied to M-QAM digital mobile radio systems

Presents an investigation of diversity and/or equalization techniques applied to 4 and 16-QAM TDMA radio systems for rapid fading dispersive channels. In particular, typical urban (TU) environments have been considered. A least sum of squared errors (LSSE) channel estimator has been introduced in order to allow the analytic calculation of the equalizer coefficients. By means of simulation methods the authors have found that a degradation of 4 dB can be expected in relation to ideal estimation. Furthermore, the degrading effects of non-linear power amplifiers have been analyzed and a degradation of 2 dB has been found.Peer ReviewedPostprint (published version

An adaptive correlator receiver for combined suppression of co-channel interference and narrow-band jammers in a slowly fading channel

This work deals with the adaptive correlation of a direct sequence spread spectrum signal in the presence of narrow-band, multipath and multiple user interference. The Least Mean Square and Recursive Least Square algorithms are employed for the adaptive convergence of the correlator receiver to minimize the mean squared error. The performance of the adaptive correlator is compared with the matched filter correlator receiver and the conventional prediction filter for the suppression of narrow-band interference by calculating the bit error probability rate. The adaptive correlator is also compared with the RAKE receiver for multipath suppression and compared to the decorelating detector for the suppression of multiple user interference. It is shown that the adaptive correlator is capable of suppressing interference when the spread spectrum signal is corrupted by a combination of disturbances, such as narrow-band jammers and multipath or multiple users on the same channel