Rashmi Sinha ¹, Arvind Choubey ¹

Affiliations
1 Department of Electronics and Communication Engineering, National Institute of Technology, Jamshedpur - 831014, Jharkhand, IN

Abstract

Objectives: In this paper, we propose to develop a discrete adaptive equalizer based on Quantum behaved Particle Swarm Optimization (QPSO) technique for noisy channel. Methods/Statistical Analysis: Equalizers have to deal with harder and more complicated problems today due to crowded communication channels and increasing interference level. Findings: This work is an effort to counterbalance Inter-Symbol Interference (ISI) and other nonlinear impairments occurring in real world channels due to nonlinear devices installed in transceivers, cross talk, presence of impulsive noise, multipath propagation and the nature of physical medium itself. A simple, yet efficient optimization algorithm QPSO which belongs to a class of bare-bones PSO family is employed for this purpose. The performance of the proposed equalizer is compared with Least Mean Square (LMS) and other popular variations of PSO, namely Constant Weight Inertia (CWI-PSO) and Linear Decay Inertia (LDI-PSO) algorithms in order to investigate its efficacy. Application/Improvements: Mean Square Error (MSE) and Bit Error Rate (BER) are evaluated for each algorithm and a comparative study of the results reveal that QPSO enjoys an improved performance over other considered algorithms. The proposed discrete equalizer model can be widely used in communication system, especially mobile and satellite communication due to its effectiveness in noisy environment.

Keywords

Bit Error Rate, Discrete Channel Equalizer, Signal to Noise Ratio.

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Rashmi Sinha ¹, Arvind Choubey ¹, Santosh Kumar Mahto ¹

Affiliations
1 Department of Electronics and Communication Engineering, National Institute of Technology, Jamshedpur, IN

Abstract

Objectives: This paper presents a novel methodology for estimating the parameters of an unknown system or a plant using Wind Driven Optimization (WDO) based adaptive identification model. Methods/Statistical Analysis: Significant endeavours are being made by researchers in the field of system identification towards building an adaptive identification model which can approximately track the plant dynamics. This work introduces the application of one of the most recently developed nature inspired WDO algorithm in system identification task. Findings: The performance of the introduced WDO based system identification technique is compared with few popular algorithms such as Least Mean Square (LMS), Bacteria Foraging Optimization (BFO), and Genetic Algorithm (GA). WDO based model is implemented for two sets of experiments at 10dB and 30dB signal to noise ratio. Normalised Mean Square Error (NMSE) obtained by the presented method at 10dB is as low as -14.71dB at 38 iterations as compared to -10.69dB at 350 iterations, -9.526dB at 192 iterations and -8.99dB at 18 iterations for LMS, BFO and GA based methods respectively. When Signal to Noise Ratio (SNR) considered is 30dB, NMSE obtained by WDO is -33.74dB at 14 iterations which is better than -30.15dB at 328 iterations, -28.66dB at 193 iterations and -28.09dB at 30 iterations for LMS, BFO and GA based methods respectively. The obtained results exhibit a very promising capability of WDO in tracking the unknown system parameters. Application/Improvements: The proposed adaptive identification model can be widely used in instrumentation, acoustic noise and vibration control, power system, telecommunication, adaptive guidance or fault tolerance etc.

Keywords

Signal to Noise Ratio, System Identification, Wind Driven Optimization.

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Rashmi Sinha ¹, Arvind Choubey ¹, Santosh Kumar Mahto ¹

Affiliations
1 Department of Electronics and Communication Engineering, National Institute of Technology, Jamshedpur − 831014, Jharkhand, IN

Abstract

Background/Objectives: We propose Wind Driven Optimization (WDO) algorithm as a novel technique to equalize the effects of non-minimum phase FIR channel in band limited high speed digital communication receivers. Methods/Statistical Analysis: This work aims at investigating the performance of WDO based channel equalizer in combating linear and nonlinear distortions caused due to Inter-Symbol Interference (ISI), Additive White Gaussian Noise (AWGN), multipath propagation etc. Findings: The non minimum phase channel is also equalized using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), which is similar class of populations based iterative soft computing optimization algorithms aiming to achieve global solution. Application/Improvements: In the end, performance comparison is done and simulation results illustrate that WDO algorithm improves the reliability of channels equalization and is more robust to various channel conditions.

Keywords

AWGN, Channel Equalization, Non-Minimum Phase, WDO.

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