This paper presents adaptive channel equalization for six standard Stanford University Interim (SUI) channels using Least Mean Square Algorithm (LMS) and Multilayer Perceptron Algorithm (MLP) models. The performance analysis of the adaptive equalizers was done based on the Bit Error Rate (BER). The performance of LMS algorithm is found decent whenever there is no nonlinearity in system, whereas in presence of nonlinearity in the system, the LMS algorithm fails to perform well. Under such a case, the MLP based equalizer is found to be better alternative. In simulation analysis, BPSK signal are transmitted through various SUI channels. The results were compared and it was found that under nonlinear conditions, MLP algorithm gives better BER in comparison to LMS. © 2015 IEEE.

Kalapraveen Bagadi

Department of Communication Engineering

School of Electronics Engineering

Vellore Campus

Lavania S

Kumam B

Matey P.S

Annepu V

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.

&nbsp;

&nbsp;

VIT University

Equalization of Stanford University Interim channels using adaptive multilayer perceptron NN model

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

The space division multiple access-orthogonal frequency division multiplexing (SDMA-OFDM) wireless system has become very popular owing high spectral efficiency and high load capability. The optimal maximum likelihood multiuser detection (MUD) technique suffers from high computational complexity. On the other hand the linear minimum mean square error (MMSE) MUD techniques yields poor performance and also fails to detect users in overload scenario, where the number of users are more than that of number of receiving antennas. By contrast, the differential evolution algorithm (DEA) aided minimum symbol error rate (MSER) MUD can sustain in overload scenario as it can directly minimizes probability of error rather than mean square error. However, all these classical techniques are still complex as these do channel estimation and multiuser detection sequentially. In this paper, complex multi layer perceptron (CMLP) neural network model is suggested for MUD in SDMA-OFDM system as it do both channel approximation and MUD simultaneously. Simulation results prove that the CMLP aided MUD performs better than the MMSE and MSER techniques in terms of enhanced bit error rate performance with low computational complexity. © 2013 Springer Science+Business Media New York.

Wireless Personal Communications

Multiuser Detection in SDMA–OFDM Wireless Communication System Using Complex Multilayer Perceptron Neural Network

An adaptive multiuser detection (MUD) technique using the complex radial basis function (CRBF) network is proposed for space division multiple access-orthogonal frequency division multiplexing (SDMA-OFDM) system. Among various MUDs, the linear minimum mean-square error (MMSE) MUD suffers from poor performance and the maximum likelihood (ML) detector is restricted by high computational complexity. Hence, the cost function minimisation-based detector like minimum symbol error rate (MSER) is preferred because of significant performance gain over MMSE MUD and complexity gain over ML detector. Moreover, the MSER detector also has a potential of surviving in overload scenario, where the number of users are more than that of the number of receiving antennas. However, in all these techniques, the requirement of channel estimation adds an extra complexity whereas, the proposed CRBF detector approximates the channel parameters in training phase and detects signals in testing phase. It also has low complexity, better performance compared with MSER MUD and also supports overload scenario. Each neuron in the proposed CRBF network is assembled with 'sech' activation function, as this function can do better complex non-linear mapping than Gaussian activation. The simulation study and performance evaluation of CRBF MUD is investigated, considering both data and image transmission. © The Institution of Engineering and Technology 2013.

IET Communications

Efficient complex radial basis function model for multiuser detection in a space division multiple access/multiple-input multiple-output–orthogonal frequency division multiplexing system

Message

International Journal of Electronics

Neural network-based multiuser detection for SDMA–OFDM system over IEEE 802.11n indoor wireless local area network channel models

Neural Computing and Applications

Neural network-based adaptive multiuser detection schemes in SDMA–OFDM system for wireless application

Journal	Data powered by Typeset2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)
Publisher	Data powered by TypesetIEEE
Open Access	0