Research on Computational Intelligence Algorithm in LTE Channel Estimation

Siraj Pathan; Sanjay Singh; Mujib Tamboli; Sunil Pathak

doi:10.29375/25392115.4308

Siraj Pathan Amity University, Rajasthan, Jaipur, India https://orcid.org/0000-0002-1728-3030
Dr. Sanjay Kumar Singh Amity University, Rajasthan, Jaipur, India https://orcid.org/0000-0002-4426-3895
Mujib Tamboli Kalsekar College of engineering, Mumbai, India https://orcid.org/0000-0002-5891-3713
Sunil Pathak Amity University, Rajasthan, Jaipur, India https://orcid.org/0000-0001-7409-9814

DOI: https://doi.org/10.29375/25392115.4308

Keywords: Genetic Algorithm (GA) ,Particle Swarm Intelligence(PSO) , Long Term Evolution (LTE) , Minimum Mean Square Error (MMSE) , Least Square (LS)

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Abstract

Because data traffic is growing at a rapid pace thanks to advancements in the Internet of Things, precise modelling and precisely anticipating Long-Term Evolution (LTE) Channel is critical for a variety of applications like as video streaming, effective bandwidth consumption, and power management. In this research, we propose a model based on a Computational Intelligence (CI) Algorithm that may enhance Channel Estimation based on received signal. Two Algorithms are considered. In contrast to previous work that focused solely on designing models to estimate channel using traditional Minimum Mean Square Error (MMSE) and Least Square (LS) algorithms, we used 1) GA (Genetic Algorithm) and 2) PSO (Particle Swarm Optimization Algorithm) to work on Discrete and Continuous Long-Term Evolution (LTE) drive test data. We're looking at LTE in the 5.8 GHz band in particular. By lowering the mean square error of LS and the complexity of MMSE, the design model attempts to improve channel estimation. Pilots are put at random and sent with data to gather channel information, which aids the receiver in decoding and estimating the channel using LS, MMSE, Taguchi GA, and PSO. The Bit Error Rate (BER), Signal to Noise Ratio, and Mean Square Error of a CI-based model have all been estimated. In comparison to the MMSE and LS algorithms, the proposed model BER achieves the target gain of 2.4 dB and 5.4 dB.

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How to Cite

Pathan, S., Singh, S., Tamboli, M., & Pathak, S. (2022). Research on Computational Intelligence Algorithm in LTE Channel Estimation. Revista Colombiana De Computación, 23(2), 17–28. https://doi.org/10.29375/25392115.4308