Blind channel identification and equalization with modulation-induced cyclostationarity

The blind adaptive equalization and identification of communication channels is a problem of important current theoretical and practical concerns. Previously proposed solutions for this problem exploit the diversity induced by sensor arrays or time oversampling, leading to the so-called second-order algebraic/statistical techniques. The prediction error method is one of them, perhaps the most appealing in practice, due to its inherent robustness to ill-defined channel lengths as well as for its simple adaptive implementation. Unfortunately, the performance of prediction error methods is known to be severely limited in noisy environments, which calls for the development of noise (bias) removal techniques. We present a low-cost algorithm that solves this problem and allows the adaptive estimation of unbiased linear predictors in additive noise with arbitrary autocorrelation. This algorithm does not require the knowledge of the noise variance and relies on a new constrained prediction cost function. The technique can be applied in other noisy prediction problems. Global convergence is established analytically. The performance of the denoising technique is evaluated over GSM test channels.

A new blind channel identification and equalization method is proposed that exploits the cyclostationarity of oversampled communication signals to achieve identification and equalization of possibly nonminimum phase (multipath) channels without using training signals. Unlike most adaptive blind equalization methods for which the convergence properties are often problematic, the channel estimation algorithm proposed here is asymptotically ex-set. Moreover, since it is based on second-order statistics, the new approach may achieve equalization with fewer symbols than most techniques based only on higher-order statistics. Simulations have demonstrated promising performance of the proposed algorithm for the blind equalization of a three-ray multipath channel.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Pulse shaping OFDM systems based on offset QAM (OFDM/OQAM) are particularly attractive for wireless high-data-rate applications. Since OFDM/OQAM schemes do not employ a cyclic prefix, equalization has to be performed. In this paper, we introduce an algorithm for blind channel identification in pulse shaping OFDM/OQAM systems. Our approach assumes that the receiver knows the transmitter pulse shaping filter. Exploiting cyclostationarity of the received OFDM signal blind channel identification is accomplished using second-order statistics only. The proposed method exhibits low noise sensitivity, is computationally efficient, and does not require knowledge of the channel order. Finally, we provide simulation results demonstrating the performance of the new algorithm.

The equalization of digital communication channel is an important task in high speed data transmission techniques. The multipath channels cause the transmitted symbols to spread and overlap over successive time intervals. The distortion caused by this problem is called inter-symbol interference (ISI) and is required to be removed for reliable communication of data over communication channels. In this task of ISI removal, the signals are complex-valued and processing has to be done in a complex multidimensional space. The growing interest in complex-valued neural networks has spurred the development of many new algorithms for equalization of communication channels in the recent past. This chapter illustrates the application of various types of complex-valued neural networks such as radial basis function networks (RBFN), multilayer feedforward networks and recurrent neural networks for training sequence-based as well as blind equalization of communication channels. The structures and algorithms for these equalizers are presented and performances based on simulation studies are analyzed highlighting their advantages and the important issues involved.

This paper addresses the problem radio mobile channel identification and equalization by blind algorithms. For this, we have proposed two algorithms, which are compared with: cumulants based algorithms, and adaptive filter algorithms (such us the constant modulus algorithm (CMA), Fractional Space CMA (FSCMA) and Sign Kurtosis Maximization Adaptive Algorithm (SKMAA)). However, The simulation results in noisy environment and for different SNR values with QPSK or 4QAM modulation shows that the proposed algorithms gives a good identification and equalization versus to the cumulants and the adaptive filter algorithms, for radio mobile channel.

A new hybrid higher-order statistics (HOS) and second-order statistics (SOS) based approach to improve the performance of the standard Bussgang algorithm for blind equalisation of digital communication channels is presented. An additional term based on SOS has been added to the conventional cost function and the Bayesian estimator has been replaced with an adaptive activation function. This is shown to give faster convergence by an order of magnitude, both in terms of epochs and the mean square error between input and output symbols at different signal-to-noise ratios.

In this paper, we show a new blind identification algorithm which is based on second order statistics and exploits a single-input double-output (SIDO) model. It is suitable for a real-time processing system because of lower operation and high-speed convergence. The proposed blind identification algorithm is superior to conventional algorithms in view of simple structure and the uniqueness of solution. We also verify its efficiency by computer simulation.

Relying upon the diversity introduced by fractional-sampling or by multiple sensors, two different approaches for blind adaptive channel identification are proposed based on existing batch algorithms. We show that the adaptive approach allows tracking of time-variations in the channel and provides robustness to nearly common channel roots. Existing second-order adaptive procedures typically employ suboptimal linear equalizers whereas the adaptive channel estimators we propose can be used in an optimal maximum likelihood sequence estimator. Simulations are provided that demonstrate the performance of the proposed channel estimators when used in the Viterbi algorithm for symbol detection.

This paper studies the blind identification problem for multiple-input multiple-output (MIMO) FIR channels, in which the source signals are modulated by high order periodic precoders. A set of new design criteria of the periodic precoders is proposed from the channel blind identification viewpoint for multiuser channels. It is shown that, due to the fact that high order periodic precoders are used in the modelling problem, certain simple orthogonality among user’s signals can be introduced by a set of proper designed periodic precoders. This results in a simple blind identification algorithm and the impulse response of every subchannel can also be determined uniquely up to a unitary scalar based on the received signals’ second order statistics (SOS) and a priori information of the periodic precoders. Simulation results illustrate that the developed blind identification algorithm is efficient.

This paper describes a blind crosstalk channel identification method and its application to discrete multitone (DMT)-based digital subscriber line (DSL) systems. Cyclostationarity inherited in these systems is exploited to isolate effects of different sources, which allows separate selection and identification of crosstalk channels. Second-order statistics-based source separation is performed by controlling part of the cyclic-extension in the transmitted DMT block. The minimum changes required by this method facilitate its implementation in current DSL systems. All mentioned theoretical derivations are illustrated by numerical results obtained with measured DSL channels.

Blind ZF equalization with controlled delay robust to order over estimation

This work focuses on the forward link in a CDMA based multi-user communication system. The mobile handset is assumed to have two antennas, either spatially separated or having diverse polarizations. Blind channel identification via the cross-relation method (CRM) of Xu, Liu, Tong, and Kailath (1995) is possible when there is a common input into at least two distinct channels (not having common spectral nulls.) The common input in the forward link is the superposition of all active users: desired user plus multi-user access interference (MAI). Although each user's signal is typically below the noise floor prior to cross-correlating with it's respective code, the power of the sum signal is above the noise floor-well above the noise floor in the case of many active users which is the goal of third generation cellular systems. We present simulations assessing the quality of the channel estimates for two types of equalizers: a zero-forcing equalizer and a RAKE receiver. Initial simulation studies indicate that CR-based blind channel estimation may need to be supplemented by additional a-priori information (such as training sequences or the pilot channel) in order to more closely approach the BER performance obtained when the channels are known exactly.

In this paper, we present new approaches to blind channel estimation and equalization based on second order statistics (SOS). We first consider the case of minimum phase channels where the equalizer is designed based on the criterion of autocorrelation matching. We cast the problem as a convex optimization program that can be efficiently solved using interior point methods. Then we consider the equalization of single-input multiple-output (SIMO) channels. Due to oversampling, the equivalent channel matrix possesses a particular structure which enables us to estimate the channel based only on the information contained in the covariance matrix at zero delay. Simulation examples are provided to demonstrate the performance advantage of the proposed algorithms compared to existing techniques.

Equalization of communication channels still remains a challenge. Radial Basis Function Neural Network (RBFNN) based equalizers are also well known in the literature. However, Design of RBFNN using traditional hit and trial is time-consuming and suboptimal in nature. Hence, this work proposes the optimal design of RBFNN equalizers using Genetic Algorithms (GA). Also, methods used in literature deals equalization problem as an optimization problem. However, this work deals the same as a classification problem. Simulation results prove the better performance of proposed equalizer.

Blind identification of discrete-time single-user FIR channels with nonminimum phase is studied here. Exploiting higher order cumulants of output signals of unknown channels, a new closed-form solution to the FIR channel impulse response is derived. The algorithm is simple and fast. It relies only on nullspace decomposition of some cumulant matrices. This method neither involves the difficult task of iterative global minimization of nonunimodal cost functions, nor does it require overparametrization, which poses consistency difficulties. It can be used either as the final channel estimate or as a good initial point in nonlinear cumulant matching techniques. The application of this identification method is broad and not limited to the use of any fixed-order cumulants. Its application in identifying data communication systems shows great potential and promise.