Publication:
Precoded large scale multi-user-MIMO system using likelihood ascent search for signal detection

Date

2022

Authors

Bagadi, Kalapraveen
Ravikumar, Chinthaginjala V.
Alibakhshikenari, Mohammad
Challa, Naga Raju
Rajesh, Anbazhagan
Aïssa, Sonia
Dayoub, Iyad
Limiti, Ernesto

Director

Publisher

Wiley
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

European Commission/Horizon 2020 Framework Programme/801538openaire
AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-127409OB-C31
Impacto
OpenAlexGoogle Scholar
cited by count

Abstract

Multiple antennas at each user equipment (UE) and/or thousands of antennas at the base station (BS) comprise the extremely spectrum efficient large scale multi-user multiple input multiple output system (BS). Due to space constraints, the closely spaced numerous antennas at each UE may cause inter antenna interference (IAI). Furthermore, when one UE comes into contact with another UE in the same cellular network, multi-user interference (MUI) may be introduced to the received signal. To mitigate IAI, efficient precoding pre-coding is necessary at each UE, and the MUI present at the BS can be canceled by efficient Multi-user Detection (MUD) techniques. The majority of earlier literature deal with one or more of these interferences. This paper implements a joint pre-coding and MUD, Lenstra-Lovasz (LLL) based Lattice Reduction (LR) assisted likelihood accent search (LAS) (LLL-LR-LAS), to mitigate IAI and MUI simultaneously LLL-based LR pre-coding mitigates IAI at each UE, and the LAS algorithm is a neighborhood search-based MUD that cancels BS MUI. The proposed approaches' performance was evaluated using Bit Error Rate analysis, and their complexity were determined using multiplication and addition.

Description

Keywords

Inter antenna interference (IAI), Multi-user detection (MUD), Multi-user interference (MUI)

Department

Ingeniería Eléctrica, Electrónica y de Comunicación / Institute of Smart Cities - ISC / Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren

Faculty/School

Degree

Doctorate program

item.page.cita

Bagadi, K., Ravikumar, C. V., Alibakhshikenari, M., Challa, N., Rajesh, A., Aïssa, S., Dayoub, I., Falcone, F., & Limiti, E. (2022). Precoded large scale multi‐user‐mimo system using likelihood ascent search for signal detection. Radio Science, 57(12). https://doi.org/10.1029/2022RS007573

item.page.rights

© 2022. The Authors.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Licencia

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