Home         Authors   Papers   Year of conference   Themes   Organizations        To MES conference

Fractionally Spaced Feed-Backward Equalizers, based on Fast RLS Adaptive Filtering Algorithms
Authors
	Djigan V.I.
Date of publication
	2020
DOI
	10.31114/2078-7707-2020-2-126-131
Abstract
	Adaptive signal processing is an important part of modern digital signal processing. Today adaptive filters are widely used in applications, where filters with fixed weights cannot be designed in advance. The well-known examples of these applications are adaptive antenna and acoustic arrays, active noise control, acoustic and electrical echo cancellation, digital predistortion of power amplifiers and channel equalization. Channel equalizer is an essential part of modern communication system. Its role is to equalize the amplitude-frequency response of a non-flat communication channel, that allows to receive digital data, sent via the channel, without intersymbol interference. There are two sort of adaptive equalizers: Feed-Forward (FF) and Feed-Backward (FB). Actually, sampling rate of the equalizer input signal is the same as data-rate. This allows to save implementation resources, but does not satisfy sampling theorem. Due to aliasing, the sampled signal becomes noisy, but the noise is tolerable, if data symbols and samples are well synchronized. Equalizers, with the same sampling rate as symbol rate, are called Symbol-Spaced (SS) ones. Another sort of equalizers are Fractionally-Spaced (FS) ones. They use input signal sampling rate, which a few times (actually an integer value) higher of symbols rate. FS equalizers do not suffer of aliasing problem and do not require a precise synchronization of data symbols and sampling. The price of the solution is a higher arithmetic complexity because FF part of FS equalizer has to contain a larger number of weights. However today, the achievements in modern microelectronic technologies and progress in integration circuit design allow to manufacture high performance Digital Signal Processors (DSP), Field-Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuits (ASIC), which ensure efficient implementation of different signal processing algorithms, including algorithms for adaptive filtering. Adaptive filtering algorithms are conditionally separated into two groups: gradient search based and least squares based. The most efficient are last ones, called Recursive Least Squares (RLS). The RLS adaptive filters are characterized by a quadratic arithmetic complexity. However, these algorithms also exist in fast, i.e. computationally efficient form with a linear arithmetic complexity, that allows to implement simultaneously efficient and low complexity adaptive filters. The paper considers the peculiarities of the fast RLS algorithms application in FS FB equalizers. Such equalizers are viewed as the multichannel adaptive filters with unequal number of weights in channels, where the number of the channels equals the value of the input signal oversampling parameter plus one. The architecture and computational procedure of such equalizer, based on fast Kalman adaptive filtering algorithm, are presented. The simulation results demonstrate the equalizer efficiency.
Keywords
	equalizer, fractionally-spaced, Decision Deed-back (DFB), multichannel adaptive filter, fast Recursive Least Squares (RLS) algorithms, Fast Kalman (FK) algorithm.
Library reference
	Djigan V.I. Fractionally Spaced Feed-Backward Equalizers, based on Fast RLS Adaptive Filtering Algorithms // Problems of Perspective Micro- and Nanoelectronic Systems Development - 2020. Issue 2. P. 126-131. doi:10.31114/2078-7707-2020-2-126-131
URL of paper
	http://www.mes-conference.ru/data/year2020/pdf/D057.pdf