Recent wireless standards prefer orthogonal frequency division multiplexing (OFDM) along with multiple input multiple output (MIMO) to offer high spectral efficiency services for any time anywhere environment. The full advantages of MIMO-OFDM is accessible only when there exist perfect channel information. Improper channel estimation leads to poor quality. In this work, we have developed a multi layered perceptron (MLP) based neural network (NN) which is trained with back propagation (BP) algorithm to estimate the channel characteristics of OFDM system. Monte-Carlo simulations are used to evaluate the performance of the proposed scheme with the conventional Least Mean Square (LMS) algorithm. The simulation results demonstrate that proposed schemes offers superior performance over the conventional LMS scheme under noisy environment. © 2016 IEEE.

Vinoth Babu K

Department of Communication Engineering

School of Electronics Engineering

Vellore Campus

Hiray K

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

A neural network based channel estimation scheme for OFDM system

2016 International Conference on Communication and Signal Processing (ICCSP)

Orthogonal frequency division multiplexing (OFDM) based wireless communication systems are expected to satisfy the thirst for ever increasing demand on higher spectral efficiency. But, OFDM systems suffer from peak to average power ratio (PAPR) and inter carrier interference (ICI) problems. It is observed that when OFDM is used in the uplink, PAPR problem is more severe and the relative mobility of the user equipments with respect to the base station will cause Doppler spread which leads to ICI. One of the solutions to minimize PAPR and ICI is single carrier frequency division multiple access. But there is a tradeoff in spectral efficiency. The main objective of this paper is to evaluate the performance of fractionally spaced equalizer (FSE) for blind channel estimation based on higher order statistics and to identify any better alternative to improve its performance. Dynamically varying modulus algorithm (DVMA) based FSE is proposed in this paper which is a better alternative for supervised equalization. The simulation results prove that FSE blind equalizer based on DVMA outperform the conventional supervised and blind equalizers. © 2013 Springer Science+Business Media New York.

Wireless Networks

Fractionally spaced equalizer based on dynamically varying modulus algorithm for spectrally efficient channel compensation in SC-FDMA based systems

For Multiple Input Multiple Output (MIMO) systems, antenna selection is a must since the Radio Frequency (RF) chains connected with antennas consumes a lot of power. This paper studies the effect of antenna selection at transmitter side since the base station power consumption is around 60-80% of the entire wireless system. The TAS is performed by a simple and computationally efficient NBA scheme. The NBA is employed at the receiver side, where channel estimation is performed, and the antenna indices corresponding to the selected transmit antennas are fed back to the transmitting side which in turn result in a considerable decrease in feedback data. It is seen that the reduced MIMO system provide with the same performance as the conventional full MIMO that uses all the antennas. © 2014 IEEE.

2014 International Conference on Communication and Signal Processing

A reduced MIMO (R-MIMO) system using norm based approach (NBA) for transmit antenna selection (TAS)

Digital Signal Processing

Channel estimation based on neural network in space time block coded MIMO–OFDM system

MIMO-OFDM Wireless Communications with MATLAB®

IEICE Transactions on Communications

Channel Estimation Based on Adaptive Neuro-Fuzzy Inference System in OFDM

Journal	Data powered by Typeset2016 International Conference on Communication and Signal Processing (ICCSP)
Publisher	Data powered by TypesetIEEE
Open Access	0