Precoded Large Scale Multi‐User‐MIMO System Using Likelihood Ascent Search for Signal Detection

• Published in: Radio science Vol. 57; no. 12
• Main Authors: Bagadi, Kalapraveen, Ravikumar, Chinthaginjala V., Alibakhshikenari, Mohammad, Challa, Nagaraj, Rajesh, Anbazhagan, Aïssa, Sonia, Dayoub, Iyad, Falcone, Francisco, Limiti, Ernesto
• Format: Journal Article
• Language: English
• Published: Washington Blackwell Publishing Ltd 01.12.2022; American Geophysical Union
• Subjects: Algorithms; Antennas; Bit error rate; Cellular communication; Coding; Engineering Sciences; inter antenna interference (IAI); Interference; Mud; Multiplication; multi‐user detection (MUD); multi‐user interference (MUI); Searching; Signal and Image processing; Signal detection
• ISSN: 0048-6604; 1944-799X; 1944-799X
• DOI: 10.1029/2022RS007573

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. Key Points An Lenstra‐Lovasz (LLL)‐based Lattice Reduction (LR) aided likelihood accent search (LAS) detector for large‐scale multi‐user multiple input multiple output systems is suggested This work reduces the performance‐complexity gap while also mitigating inter antenna interference and multi‐user interference The suggested LLL‐based LR‐LAS approaches achieve maximum likelihood (ML) performance with a significant complexity gain over ML detector than traditional ones