Blind Adaptive Channel Equalization Research Articles

Blind Adaptive Channel Equalization Using Modified CMA

The constant modulus algorithm (CMA) equalizer is perhaps the best known and the most popular scheme for blind adaptive channel equalization. In this paper, a modified constant modulus algorithm (modified CMA or MCMA) is proposed by modifying its error function. We have discussed the MCMA to blind channel equalization for baud-rat sampling in single-user case. Computer simulations are provided for 8PSK signals in noise environments under frequency selective channels. Results demonstrate that the MCMA displays much superior performance to the CMA for both convergence-time and intersymbol interference (ISI) or mean square error (MSE).

Robust Blind Adaptive Channel Equalization in ChaoticCommunication Systems

Based on the bounded property and statistics ofchaotic signal and the idea of set-membership identification, we propose aset-membership generalized least mean square (SM-GLMS) algorithm with variable stepsize for blind adaptive channel equalization in chaotic communication systems. Thesteady state performance of the proposed SM-GLMS algorithm is analysed, andcomparison with an extended Kalman filter (EKF)-based adaptive algorithm andvariable gain least mean square (VG-LMS) algorithm is performed for blind adaptivechannel equalization. Simulations show that the proposed SM-GLMS algorithm canprovide more significant steady state performance improvement than the EKF-basedadaptive algorithm and VG-LMS algorithm.

Blind Adaptive Channel Equalization with Performance Analysis

A new adaptive multiple-shift correlation (MSC)-based blind channel equalizer (BCE) for multiple FIR channels is proposed. The performance of the MSC-based BCE under channel order mismatches due to small head and tail channel coefficient is investigated. The performance degradation is a function of the optimal output SINR, the optimal output power, and the control vector. This paper also proposes a simple but effective iterative method to improve the performance. Simulation examples are demonstrated to show the effectiveness of the proposed method and the analyses.

Blind adaptive channel equalization using multichannel linear prediction-based cross-correlation vector estimation

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequences, nor does it require a priori channel information. In this paper, an adaptive blind MMSE channel equalization technique based on second-order statistics is investigated. We present an adaptive blind MMSE channel equalization using multichannel linear prediction error method for estimating cross-correlation vector. They can be implemented as RLS or LMS algorithms to recursively update the cross-correlation vector. Once cross-correlation vector is available, it can be used for MMSE channel equalization. Unlike many known subspace methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch. Performance of our algorithms and comparison with existing algorithms are shown for real measured digital microwave channel.

FPEF-based blind adaptive channel equalization with arbitrary decision delay

Blind equalization of the communication channel is important because it does not need a training signal, nor does it require a priori channel information to increase bandwidth efficiency. The linear prediction error method is perhaps the most attractive in practice due to its sensitivity to blind channel equalizer length mismatch and simple adaptive implementation. Unfortunately, the previous one-step prediction error method has a limited arbitrary decision delay. In this paper, we propose a method for fractionally spaced blind equalizer with arbitrary decision delay using one-step forward prediction error filter from the second-order statistics of the received signals for SIMO channel. Our algorithm utilizes the forward prediction error as the training signal and computes the best decision delay from all the possible decision delay. Simulation results are presented to demonstrate the performance of our proposed algorithm.

Unbiased blind adaptive channel identification and equalization

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 family of blind adaptive equalization algorithms

New blind adaptive channel equalization techniques based on a deterministic optimization criterion are presented. A family of nonlinear functions is proposed which constitutes a generic class of blind algorithms. They have been shown to have better performance than the conventional constant modulus algorithm (CMA)-like approaches. The advantages include the relaxed stability range on the step size and that an automatic gain control unit which estimates the gain of the channel, is no longer of crucial importance.

Global convergence of fractionally spaced Godard (CMA) adaptive equalizers

The Godard (1980) or constant modulus algorithm (CMA) equalizer is perhaps the best known and the most popular scheme for blind adaptive channel equalization. Most published works on blind equalization convergence analysis are confined to T-spaced equalizers with real-valued inputs. The common belief is that analysis of fractionally spaced equalizers (FSEss) with complex inputs is a straightforward extension with similar results. This belief is, in fact, untrue. We present a convergence analysis of Godard/CMA FSEs that proves the important advantages provided by the FSE structure. We show that an FSE allows the exploitation of the channel diversity that supports two important conclusions of great practical significance: (1) a finite-length channel satisfying a length-and-zero condition allows Godard/CMA FSE to be globally convergent, and (2) the linear FSE filter length need not be longer than the channel delay spread. Computer simulation demonstrates the performance improvement provided by the adaptive Godard FSE.

Blind equalization based on joint minimum MSE criterion

A joint minimum mean square error (MSE) criterion is presented for blind adaptive channel equalization of QAM signals. By jointly estimating the transmitted sequence and the equalizer parameters through the minimization of MSE, the equalizer parameters can be optimized and the channel input sequence can be restored at the equalizer output. To improve computational efficiency, a reduced constellation recursive least squares (RLS) algorithm is introduced for blind equalisation. Simulation results show the success of both the least square and the reduced constellation RLS algorithms. >

Local convergence of the Sato blind equalizer and generalizations under practical constraints

An early use of recursive identification in blind adaptive channel equalization is an algorithm developed by Y. Sato (1975). An important generalization of the Sato algorithm with extensive analysis appears in the work of A. Benveniste et al. (1980). These generalized algorithms have been shown to possess a desirable global convergence property under two idealized conditions. The convergence properties of this class of blind algorithms under practical constraints common to a variety of channel equalization applications that violate these idealized conditions are studied. Results show that, in practice, when the equalizer is finite-dimensional and/or the input is discrete (as in digital communications) the equalizer parameters may converge to parameter settings that fail to achieve the objective of approximating the channel inverse. It is also shown that a center spike initialization is insufficient to guarantee avoiding such ill-convergence. Simulations verify the analytical results. >

Admissibility in blind adaptive channel equalization

A tutorial survey is presented of an open problem, namely, the admissibility of the memory-less-error-function class of blind adaptive channel equalizers. The problem is treated in a manner that stresses control engineering, rather than communications engineering, terminology. A simple, simulated experiment is set up and referred to throughout. It is used to illustrate intersymbol interference and its removal by a cascaded (fixed) equalizer approximating the inverse channel dynamics followed by a nearest-element decision device. Then, a training signal is assumed to be available at the receiver, and a traditional, prediction-error-based adaptive algorithm (i.e. LMS) is applied, and its parameter estimates observed. The two most widely studied adaptive algorithms for blind channel equalization are then considered. >