Channel Identification Based on Cumulants, Binary Measurements, and Kernels

Hicham Oualla,Mathieu Pouliquen,Said Safi,Rachid Fateh,Miloud Frikel,Anouar Darif

doi:10.3390/systems9020046

Abstract

In this paper, we discuss the problem of channel identification by using eight algorithms. The first three algorithms are based on higher-order cumulants, the next three algorithms are based on binary output measurement, and the last two algorithms are based on reproducing kernels. The principal objective of this paper is to study the performance of the presented algorithms in different situations, such as with different sizes of the data input or different signal-to-noise ratios. The presented algorithms are applied to the estimation of the channel parameters of the broadband radio access network (BRAN). The simulation results confirm that the presented algorithms are able to estimate the channel parameters with different accuracies, and each algorithm has its advantages and disadvantages for a given situation, such as for a given SNR and data input. Finally, this study provides an idea of which algorithms can be selected in a given situation. The study presented in this paper demonstrates that the cumulant-based algorithms are more adequate if the data inputs are not available (blind identification), but the kernel- and binary-measurement-based methods are more adequate if the noise is not important (SNR≥16 dB).

Highlights

The identification of single-input single-output (SISO) systems in the blind case has been well studied by using higher-order statistics [1,2,3,4,5], without employing any restrictive hypotheses about channel zeros, channelorder overestimation errors, or additive noise color, as well as without increasing the data stream transmission rate
We focused on a comparison of the results of channel identification using some algorithms based on higher-order cumulants (HOCs), binary measurement, or kernel-positive methods
Three algorithms based on higher-order cumulants (HOCs) are presented

Summary

Introduction

The identification of single-input single-output (SISO) systems in the blind case (without knowledge of the input system data) has been well studied by using higher-order statistics [1,2,3,4,5], without employing any restrictive hypotheses about channel zeros, channelorder overestimation errors, or additive noise color, as well as without increasing the data stream transmission rate. The shared characteristic of this category of techniques [4] is that they use a periodically time-varying precoder to provoke cyclo-stationary statistics at the transmitter (i.e., a simple periodic modulator or a filter bank) and take advantage of the received samples’ second-order cyclo-stationary statistics. Those algorithms have been called transmitter-induced multi-stationary-based techniques. Iterative strategies are used in some types of algorithms for blind channel identification In these algorithms, an initial channel estimation is employed by using a symbol predictor to obtain provisional soft estimates of the symbol sequence as it is transmitted. The second technique that will be discussed here deals with the identification of the system based on quantized measurement and is used in a wide range of applications, such as microfabricated devices [6,7,8]

Objectives

Methods

Results