A Novel Approach to Evaluate Reduced Inter Symbol Interference in UFMC Systems

The UFMC modulation scheme has been proposed as a solid competitive framework for future portable fifth generation communication. UFMC can be considered as a candidate waveform for 5G communications since it gives strength against Inter Symbol Interference (ISI) [1]. Inter-symbol interference prompted error can make the receiver neglect to reproduce the original data. Equalizers in the receivers, which are extraordinary sorts of filters, moderate the direct twisting created by the channel [2]. On the off chance that the channel’s time-fluctuating qualities are known from the earlier, at that point, the ideal setting for equalizers can be worked out. But in practical systems the channel’s time-changing attributes are not known from the earlier, so adaptive equalization method is applied in this paper based on the LMS algorithms. Adaptive equalizers are adjusted, or change the estimation of its taps as time advances [3].

Adaptive Equalizer Design for Unmanned Aircraft Vehicle Image Transmission over Relay Channels

A novel length adaptive method is proposed for time domain equalizer by taking the channel attenuation ratio between different multipath components into account in UAV-UAV and UAV-ground channels. Then, considering received image quality, the minimum bit error ratio (MBER) criterion is exploited to design adaptive equalizers for both amplify-and-forward (AF) and decode-and-forward (DF) relaying systems by the proposed length adaptive method. Results show that proposed MBER adaptive equalizers outperform the traditional ones in both AF relaying and DF relaying as channel attenuation ratio in UAV-ground channel increases. Moreover, DF outperforms AF as channel attenuation ratio in UAV-UAV channel increases. Furthermore, bit error ratio (BER) and peak signal-to-noise ratio (PSNR) performances in both AF and DF are evaluated to show the enhancement by the proposed MBER adaptive equalizers.

Convolutional Noise PDF at the Convergence State of a Blind Adaptive Equalizer

In the literature, the convolutional noise obtained at the output of a blind adaptive equalizer, is often modeled as a Gaussian process during the latter stages of the deconvolution process where the process is close to optimality. However, up to now, no strong mathematical basis was given supporting this phenomenon. Furthermore, no closed-form or closed-form approximated expression is given that shows what are the constraints on the system’s parameters (equalizer’s tap-length, input signal statistics, channel power, chosen equalization method and step-size parameter) for which the assumption of a Gaussian model for the convolutional noise holds. In this paper, we consider the two independent quadrature carrier input case and type of blind adaptive equalizers where the error that is fed into the adaptive mechanism which updates the equalizer’s taps can be expressed as a polynomial function of the equalized output up to order three. We show based on strong mathematical basis that the convolutional noise pdf at the latter stages of the deconvolution process where the process is close to optimality, is approximately Gaussian if complying on some constraints depending on the step-size parameter, input constellation statistics, channel power, chosen equalization method and equalizer’s tap-length. Simulation results confirm our findings.