Channel Capacity of Indoor MIMO Systems in the Presence of Spatial Diversity

2014 ◽  
pp. 53-61
Author(s):  
M. Senon ◽  
M. N. Husain ◽  
A. R. Othman ◽  
M. Z. A. Aziz ◽  
K. A. A. Rashid ◽  
...  
Keyword(s):  
Channel Capacity ◽  
Mimo Systems ◽  
Spatial Diversity

A Low-Profile Dual-Polarized Directional Antenna for Enhancing Channel Capacity in Indoor MIMO Systems

2010 ◽  
Vol E93-B (10) ◽  
pp. 2570-2577 ◽  
Author(s):  
Daisuke UCHIDA ◽  
Hiroyuki ARAI ◽  
Yuki INOUE ◽  
Keizo CHO
Keyword(s):  
Channel Capacity ◽  
Mimo Systems ◽  
Directional Antenna ◽  
Low Profile ◽  
Dual Polarized

MIMO Systems in a Composite Fading and Generalized Noise Scenario: A Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Keerti Tiwari
Keyword(s):  
System Performance ◽  
Performance Enhancement ◽  
Channel Model ◽  
Mimo Systems ◽  
Mimo System ◽  
Spatial Diversity ◽  
Spatial Multiplexing ◽  
Channel Models ◽  
Efficient System ◽  
Composite Channel

: Multiple-input multiple-output (MIMO) systems have been endorsed to enable future wireless communication requirements. The efficient system designing appeals an appropriate channel model, that considers all the dominating effects of wireless environment. Therefore, some complex or less analytically acquiescent composite channel models have been proposed typically for single-input single-output (SISO) systems. These models are explicitly employed for mobile applications, though, we need a specific study of a model for MIMO system which can deal with radar clutters and different indoor/outdoor and mobile communication environments. Subsequently, the performance enhancement of MIMO system is also required in such scenario. The system performance enhancement can be examined by low error rate and high capacity using spatial diversity and spatial multiplexing respectively. Furthermore, for a more feasible and practical system modeling, we require a generalized noise model along with a composite channel model. Thus, all the patents related to MIMO channel models are revised to achieve the near optimal system performance in real world scenario. This review paper offers the methods to improve MIMO system performance in less and severe fading as well as shadowing environment and focused on a composite Weibull-gamma fading model. The development is the collective effects of selecting the appropriate channel models, spatial multiplexing/detection and spatial diversity techniques both at the transmitter and the receivers in the presence of arbitrary noise.

scholarly journals MIMO Self-Encoded Spread Spectrum with Iterative Detection over Rayleigh Fading Channels

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Shichuan Ma ◽  
Lim Nguyen ◽  
Won Mee Jang ◽  
Yaoqing (Lamar) Yang
Keyword(s):  
Fading Channels ◽  
Spread Spectrum ◽  
Rayleigh Fading ◽  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Spatial Diversity ◽  
Iterative Detection ◽  
Channel Fading ◽  
Input Multiple Output

Self-encoded spread spectrum (SESS) is a novel communication technique that derives its spreading code from the randomness of the source stream rather than using conventional pseudorandom noise (PN) code. In this paper, we propose to incorporate SESS in multiple-input multiple-output (MIMO) systems as a means to combat against fading effects in wireless channels. Orthogonal space-time block-coded MIMO technique is employed to achieve spatial diversity, and the inherent temporal diversity in SESS modulation is exploited with iterative detection. Simulation results demonstrate that MIMO-SESS can effectively mitigate the channel fading effect such that the system can achieve a bit error rate of with very low signal-to-noise ratio, from 3.3 dB for a antenna configuration to just less than 0 dB for a configuration under Rayleigh fading. The performance improvement for the case is as much as 6.7 dB when compared to an MIMO PN-coded spread spectrum system.

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.

Sign in / Sign up

Export Citation Format

Share Document