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

scholarly journals Frequency dependency of channel capacity improvement in near-field MIMO system with metal wire

2013 ◽  
Vol 2 (6) ◽  
pp. 257-262
Author(s):  
Dalin Zhang ◽  
Toshikazu Hori ◽  
Mitoshi Fujimoto
Keyword(s):  
Channel Capacity ◽  
Mimo System ◽  
Near Field ◽  
Metal Wire ◽  
Frequency Dependency ◽  
Capacity Improvement

scholarly journals Effect of HPBW on channel capacity in near-field MIMO system

2012 ◽  
Vol 1 (5) ◽  
pp. 177-183 ◽  
Author(s):  
Dalin Zhang ◽  
Toshikazu Hori ◽  
Mitoshi Fujimoto
Keyword(s):  
Channel Capacity ◽  
Mimo System ◽  
Near Field

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

Near-Field Radiative Nanothermal Imaging of Nonuniform Joule Heating in Narrow Metal Wires

Nano Letters ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 4220-4225 ◽  
Author(s):  
Qianchun Weng ◽  
Kuan-Ting Lin ◽  
Kenji Yoshida ◽  
Hirofumi Nema ◽  
Susumu Komiyama ◽  
...  
Keyword(s):  
Joule Heating ◽  
Near Field ◽  
Metal Wires

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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