Decision-Feedback Equalization Achieves

We consider a SIMO or SISO communication link. In the case of white noise, the Matched Filter Bound (MFB) is proportional to the total channel energy. Hence all diversity sources present in the channel show up in the MFB. The MFB usually is a close approximation for the performance of Maximum Likelihood Sequence Detection (MLSD) and represents an upper bound for the performance of any receiver (Rx). In this paper we consider the diversity performance of suboptimal Rx’s of the decision feedback equalization (DFE) type. Two DFE designs are considered: Minimum Mean Squared Error (MMSE) or MMSE Zero-Forcing (MMSE-ZF). The SNR at the detection point of a MMSE(-ZF) DFE exhibits a performance loss w.r.t. the MFB, a loss that is determined by the energy in the feedback filter. It is shown that there exists an upperbound for this loss that is channel independent. Hence the DFE enjoys as much diversity as the MFB

Adaptation of an All-Pass Equalizer

In this paper,we describe an adaptive algorithm for iteratively determining the optimal transfer function of the all-pass filter. Adaptation is based on estimating the gradient of the mean-square error with respect to the poles of the filter.

A New Block Adaptive Filtering Algorithm

This brief presents a modified general optimum block adaptive(MGOBA) algorithm for block adaptive decision-feedback equalization (DFE) of multipath fading channels

C hannel-Est imation-Based Adaptive Equalization of Underwater

To reduce computational complexity of signal processing and improve performance of data detection, receiver structures that are matched to the physical channel characteristics are investigates. A decision-feedback equalizer is designed which relies on an adaptive channel estimator to compute its parameters. The channel estimate is reduced in size by selecting only the significant components, whose delay span is often much shorter than the multipath spread of the channel. This estimate is used to cancel the post-cursor IS1 prior to linear equalization. Optimal coefficient selection (sparsing) is performed by truncation in magnitude. The advantages of this approach are reduction in the number of receiver parameters, optimal implementation of sparse feedback, and efficient parallel implementation of adaptive algorithms for the multichannel pre-combiner, the fractionally-spaced channel estimators and the short feedforward equalizer filters. Receiver algorithm is demonstrated using real data transmitted at 10 kbps over 3 km in shallow water.

Channel Estimation for Adaptive

Abstract—Frequency-domain equalization (FDE) is an effective technique for high-rate wireless communications because of its reduced complexity compared to conventional time-domain equalization (TDE). In this paper, we consider adaptive FDE for single-carrier (SC) systems with explicit channel and noise-power estimation. The channel response is estimated in the frequency domain following two different approaches. The first operates independently on each frequency bin while the second exploits the fading correlation across the signal bandwidth. Leastmean- square (LMS) and recursive-least-square (RLS) algorithms are employed to update the channel estimates. The noise power is estimated using a low-complexity algorithm based on ad hoc reasoning. Compared to other existing receivers employing adaptive FDE, the proposed schemes have better error-rate performance and can be used even in the presence of relatively fast fading.