Multicarrier code division multiple access (MC–CDMA) is a promising wireless communication technology with high spectral efficiency and system performance. However, all multiple access techniques including MC–CDMA were most likely to have multiple access interference (MAI). So this paper mainly aims at designing a suitable receiver for MC–CDMA system to mitigate such MAI. The classical receivers like maximal ratio combining, minimum mean square error, and iterative block–decision feedback equalization fail to cancel MAI when the MC–CDMA is subjected to severe nonlinear distortions, which may occur due to saturated power amplifiers or arbitrary channel conditions. Being highly nonlinear structures, the neural network receivers such as multilayer perceptron and recurrent neural network could be better alternative for such a case. The feasibility, efficiency, and effectiveness of the proposed neural network receiver are studied thoroughly for MC–CDMA system under different nonlinear conditions. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Venkata Ravi Kumar C

Department of Embedded Technology

School of Electronics Engineering

Vellore Campus

Kalapraveen Bagadi

Department of Communication Engineering

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

International Journal of Communication Systems

In this paper, we propose a new algorithm called Lenstra Lenstra Lovász (LLL) assisted Likelihood Ascent Search (LAS) algorithm for signal detection in Massive MIMO which attains a near to optimum performance. This algorithm is developed by collaborating two existing algorithms to satisfy the tradeoff between performance and complexity in Massive Multiple Input Multiple Output (MIMO) systems. The Linear Detection and Lattice Reduction (LR) are some of the prominent suboptimal algorithms whose performance is far from optimal. In the proposed LLL-LAS Algorithm, the LLL algorithm is an LR based detection which serves as the initial solution to the LAS algorithm. The Simulation results substantiate the decrement in the Bit Error Rate which makes it better than the other classical detection techniques. © 2019 IEEE.

2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN)

Lenstra Lenstra Lovász (LLL) Assisted Likelihood Ascent Search (LAS) Algorithm for Signal Detection in Massive MIMO

MC-CDMA receiver design using recurrent neural networks for eliminating multiple access interference and nonlinear distortion

The space division multiple access–orthogonal frequency division multiplexing (SDMA–OFDM) technique is emerged as a most competitive technology for future wireless communication system as it can provide high spectral efficiency and resistance from inter symbol interference (ISI). The SDMA like multiple access techniques are prone to multiple access interference (MAI) because multiple users transmit their data simultaneously. Such a receiver requires appropriate multiuser detection (MUD) scheme to detect individual user's signals correctly by mitigating MAI. Further, due to non-linear behaviour of wireless channel, the signals at SDMA receiver often become linearly non-separable. As a result, MUD becomes a challenging multidimensional optimization problem. Considering these challenges, this paper reviews various MUD techniques for SDMA–OFDM system. © 2016 Elsevier B.V.

Physical Communication

Recent trends in multiuser detection techniques for SDMA–OFDM communication system

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

Journal	Data powered by TypesetInternational Journal of Communication Systems
Publisher	Data powered by TypesetWiley
ISSN	1074-5351
Open Access	0