Deep Learning based MIMO detection in 6G Wireless Communication System

Priyanka Mishra, Mehboob Ul Amin, Ghanshyam Singh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

This paper proposes a deep learning algorithm to decode spheres in order to solve the multi-input multi-output (MIMO) receiver's detection problem. The modified K and K1 sphere decoders achieve high performance in terms of spectral efficiency. This in turn will improve the data rate of next generation wireless communication systems. In order to reduce the computational complexity, the number of codewords needs to be reduced, which can be achieved with a deep learning algorithm based on a neural network. Simulation results depict the effectiveness of the proposed schemes in terms of BER, spectral efficiency, and computational time. Simulations were carried out using the MATLAB tool to support the proposed algorithm and validate the analytical results.

Original languageEnglish
Title of host publicationProceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023
EditorsAnand Kumar, Ved Prakash Mishra, Vishal Naranje, Apurv Yadav
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages366-369
Number of pages4
ISBN (Electronic)9798350338263
DOIs
Publication statusPublished - 2023
Event3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023 - Dubai, United Arab Emirates
Duration: 9 Mar 202310 Mar 2023

Publication series

NameProceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

Conference

Conference3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023
Country/TerritoryUnited Arab Emirates
CityDubai
Period9/03/2310/03/23

Keywords

  • Bit error rate
  • Deep learning
  • K and K1 Sphere decoder
  • Spectral efficiency
  • Sphere decoder

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Safety, Risk, Reliability and Quality

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