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	A comparative analysis of computational intelligence algorithms for estimation of LTE channels<br />
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Pathan Siraj , Noonia Ajit , Tamboli Mujib , Sunil Pathak

2022 , VOLUME 22, NUMBER 1 ( january-february )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2022-22-1-206-216

A comparative analysis of computational intelligence algorithms for estimation of LTE channels

S. Pathan, A. Noonia, M. Tamboli, S. Pathak

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Pathan S., Noonia A., Tamboli M., Pathak S. A comparative analysis of computational intelligence algorithms for estimation of LTE channels. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2022, vol. 22, no. 1, pp. 206–216. doi: 10.17586/2226-1494-2022-22-1-206-216

Abstract

Precise modelling and accurate estimation of long-term evolution (LTE) channels are essential for numerous applications like video streaming, efficient use of bandwidth and utilization of power. This deals with the fact that data traffic is increasing continuously with advances in Internet of things. Previous works were focused mainly on designing models to estimate channel using traditional minimum mean square error (MMSE) and least squares (LS) algorithms. The proposed model enhances LTE channel estimation. The designed model combines LS and MMSE methods using Taguchi genetic (GE) and Particle Swarm Intelligence (PSO) algorithms. We consider LTE operating in 5.8 GHz range. Pilot signals are sent randomly along with data to obtain information about the channel. They help to decode a signal in a receiver and estimate LS and MMSE combined with Taguchi GA and PSO, respectively. CI-based model performance was calculated according to the bit error rate (BER), signal-to-noise ratio and mean square error. The proposed model achieved the desired gain of 2.4 dB and 5.4 dB according to BER as compared to MMSE and LS algorithms, respectively.

Keywords: genetic algorithm (GA), particle swarm intelligence (PSO), long term evolution (LTE), minimum mean square error (MMSE), least squares (LS)

Acknowledgements. We thank Amity School of Engineering & Technology of Amity University, Jaipur, India for providing material and resources for our study, we also thank Anjuman-I-Islam’s Kalsekar Technical Campus of Mumbai University Navi Mumbai, India for providing laboratory to do research.

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