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Keywords = LLR-ZF

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19 pages, 486 KiB  
Article
Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
by Samarendra Nath Sur, Rabindranath Bera, Akash Kumar Bhoi, Mahaboob Shaik and Gonçalo Marques
Information 2020, 11(6), 301; https://doi.org/10.3390/info11060301 - 4 Jun 2020
Cited by 8 | Viewed by 3613
Abstract
Massive multi-input-multi-output (MIMO) systems are the future of the communication system. The proper design of the MIMO system needs an appropriate choice of detection algorithms. At the same time, Lattice reduction (LR)-aided equalizers have been well investigated for MIMO systems. Many studies have [...] Read more.
Massive multi-input-multi-output (MIMO) systems are the future of the communication system. The proper design of the MIMO system needs an appropriate choice of detection algorithms. At the same time, Lattice reduction (LR)-aided equalizers have been well investigated for MIMO systems. Many studies have been carried out over the Korkine–Zolotareff (KZ) and Lenstra–Lenstra–Lovász (LLL) algorithms. This paper presents an analysis of the channel capacity of the massive MIMO system. The mathematical calculations included in this paper correspond to the channel correlation effect on the channel capacity. Besides, the achievable gain over the linear receiver is also highlighted. In this study, all the calculations were further verified through the simulated results. The simulated results show the performance comparison between zero forcing (ZF), minimum mean squared error (MMSE), integer forcing (IF) receivers with log-likelihood ratio (LLR)-ZF, LLR-MMSE, KZ-ZF, and KZ-MMSE. The main objective of this work is to show that, when a lattice reduction algorithm is combined with the convention linear MIMO receiver, it improves the capacity tremendously. The same is proven here, as the KZ-MMSE receiver outperforms its counterparts in a significant margin. Full article
(This article belongs to the Special Issue 5G and Wireless Networks Communications)
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