Performance Analysis of Channel Capacity of MIMO System Without CSI

2020 ◽  
pp. 529-535
Author(s):  
Divya Singh ◽  
Aasheesh Shukla
Keyword(s):  
Performance Analysis ◽  
Channel Capacity ◽  
Mimo System

Channel Capacity Improvement in Near-Field MIMO System Using Metal Wires

2013 ◽  
Vol E96.B (5) ◽  
pp. 1141-1148 ◽  
Author(s):  
Dalin ZHANG ◽  
Toshikazu HORI ◽  
Mitoshi FUJIMOTO
Keyword(s):  
Channel Capacity ◽  
Mimo System ◽  
Near Field ◽  
Capacity Improvement ◽  
Metal Wires

Distributed MIMO System Design and Channel Capacity Analysis Based on Polar Orbit LEO Constellation

2021 ◽  
Vol 55 (6) ◽  
pp. 557-567
Author(s):  
Chi Zhang ◽  
Huan-yu Li ◽  
Hui Li ◽  
Zhi-yang Lin ◽  
Ping Wang
Keyword(s):  
System Design ◽  
Channel Capacity ◽  
Mimo System ◽  
Capacity Analysis ◽  
Polar Orbit

Real-Time Performance Analysis of FOI-PD Controller for Twin Rotor MIMO System

2018 ◽  
Vol 36 (6) ◽  
pp. 547-567
Author(s):  
Debdoot Sain ◽  
Subrat Kumar Swain ◽  
Ayan Saha ◽  
Sudhansu Kumar Mishra ◽  
Sarbani Chakraborty
Keyword(s):  
Performance Analysis ◽  
Real Time ◽  
Mimo System ◽  
Pd Controller ◽  
Time Performance ◽  
Twin Rotor Mimo System ◽  
Twin Rotor

scholarly journals Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 301
Author(s):  
Samarendra Nath Sur ◽  
Rabindranath Bera ◽  
Akash Kumar Bhoi ◽  
Mahaboob Shaik ◽  
Gonçalo Marques
Keyword(s):  
Channel Capacity ◽  
Massive Mimo ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Mimo System ◽  
Correlation Effect ◽  
Performance Comparison ◽  
Lattice Reduction ◽  
Detection Algorithms ◽  
Minimum Mean Squared Error

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.

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