In this paper, we propose a serial equalizer for doubly-selective channels. We use a Basis Expansion Model (BEM) Finite Impulse Response (FIR) to model the doubly-selective channel and to design the serial equalizer. In contrast to our previous works, we decrease the modeling error of the BEM FIR by fitting the BEM FIR to the true doubly-selective channel over a time-window that is independent ...

We develop a Turbo equalization-based receiver for serial-tone HF waveforms, where the conventional decision feedback equalizer is replaced by a soft-in soft-out (SISO) equalizer, and the receiver iterates soft information between the equalizer and the decoder. To keep the computational burden of the iterative equalization and decoding procedure as low as possible, the SISO equalizer is designe...

We present an approximate analysis approach to the computation of probability of error and mean burst error length for a decision feedback equalizer (DFE) that takes into account feedback of decision errors. The method uses a reduced-state Markov model of the feedback process and is applicable to linear modulation formats. We use this technique to analyze a DFE design that mitigates the effects...

We propose a finite impulse response (FIR) minimum mean squared error (MMSE) decision feedback equalizer (DFE) for orthogonal multipulse modulated signals received through a multipath channel. Our system model accounts for both the loss of orthogonality caused by the multipath channel as well as intersymbol interference (ISI). First, we review previous work on the subject which used the zero-fo...

This paper investigates computationally-efficient methods for blind decision feedback equalization (DFE) that reduce the complexity and power requirements of blind equalization algorithms while maintaining their steady-state characteristics for quadrature amplitude modulation (QAM) signals. These include the power-of-two error (POT), selective coefficient update (SCU), and frequency-domain bloc...

Equalization of satellite communication using complex-bilinear recurrent neural network (C-BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used in modeling highly nonlinear systems with time-series characteristics more effectively than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C-BLRNN) for dea...

We consider a time varying wireless fading channel, equalized by an LMS Decision Feedback equalizer (DFE). We study how well this equalizer tracks the optimal MMSEDFE (Wiener) equalizer. We model the channel by an Autoregressive (AR) process. Then the LMS equalizer and the AR process are jointly approximated by the solution of a system of ODEs (ordinary differential equations). Using these ODEs...