Downlink capacity evaluation of SA-MIMO system under single-user scenario

2013 ◽  
Author(s):  
Jianxin Dai ◽  
Zhenjie Ni ◽  
Ming Chen ◽  
Yang Li
Keyword(s):  
Mimo System ◽  
Capacity Evaluation ◽  
User Scenario ◽  
Single User ◽  
Downlink Capacity

The Downlink Capacity of Single-User SA-MIMO System

2012 ◽  
Vol 69 (4) ◽  
pp. 1333-1345 ◽  
Author(s):  
Jianxin Dai ◽  
Ming Chen ◽  
Pei-Jung Chung
Keyword(s):  
Mimo System ◽  
Single User ◽  
Downlink Capacity

Precoding for Multiuser MIMO

2012 ◽  
pp. 130-156 ◽  
Author(s):  
Elsadig Saeid ◽  
Varun Jeoti ◽  
Brahim Belhaouari Samir
Keyword(s):  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Achievable Rate ◽  
Multiple Access Channels ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Single User ◽  
A New Technique ◽  
Improved Technique

Future Wireless Networks are expected to adopt multi-user multiple input multiple output (MU-MIMO) systems whose performance is maximized by making use of precoding at the transmitter. This chapter describes the recent advances in precoding design for MU-MIMO and introduces a new technique to improve the precoder performance. Without claiming to be comprehensive, the chapter gives deep introduction on basic MIMO techniques covering the basics of single user multiple input multiple output (SU-MIMO) links, its capacity, various transmission strategies, SU-MIMO link precoding, and MIMO receiver structures. After the introduction, MU-MIMO system model is defined and maximum achievable rate regions for both MU-MIMO broadcast and MU-MIMO multiple access channels are explained. It is followed by critical literature review on linear precoding design for MU-MIMO broadcast channel. This paves the way for introducing an improved technique of precoding design that is followed by its performance evaluation.

Low-Complexity Adaptive Detector in Single-User MIMO System

2019 ◽  
Vol 7 (4) ◽  
pp. 129-132
Author(s):  
Woon-Sang Lee ◽  
◽  
Jae-Hyun Ro ◽  
Hyun-Sun Hwang ◽  
Hyoung-Kyu Song
Keyword(s):  
Mimo System ◽  
Low Complexity ◽  
Single User

scholarly journals Communications using Sparse Signals

2021 ◽  
Author(s):  
Madhusudan Kumar Sinha ◽  
arun pachai kannu
Keyword(s):  
Communication Systems ◽  
Error Control ◽  
Linear Codes ◽  
Sparse Signal ◽  
Multiple Access Channel ◽  
Signal Recovery ◽  
Error Control Coding ◽  
Access Channel ◽  
User Scenario ◽  
Single User

Inspired by compressive sensing principles, we propose novel error control coding techniques for communication systems. The information bits are encoded in the support and the non-zero entries of a sparse signal. By selecting a dictionary matrix with suitable dimensions, the codeword for transmission is obtained by multiplying the dictionary matrix with the sparse signal. Specifically, the codewords are obtained from the sparse linear combinations of the columns of the dictionary matrix. At the decoder, we employ variations of greedy sparse signal recovery algorithms. Using Gold code sequences and mutually unbiased bases from quantum information theory as dictionary matrices, we study the block error rate (BLER) performance of the proposed scheme in the AWGN channel. Our results show that the proposed scheme has a comparable and competitive performance with respect to the several widely used linear codes, for very small to moderate block lengths. In addition, our coding scheme extends straightforwardly to multi-user scenarios such as multiple access channel, broadcast channel, and interference channel. In these multi-user channels, if the users are grouped such that they have similar channel gains and noise levels, the overall BLER performance of our proposed scheme will coincide with an equivalent single-user scenario.

scholarly journals A Multi-User Personal Indoor Localization System Employing Graph-Based Optimization

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 157 ◽  
Author(s):  
Michał R. Nowicki ◽  
Piotr Skrzypczyński
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Historical Data ◽  
Dead Reckoning ◽  
Office Building ◽  
Localization System ◽  
Multiple Users ◽  
User Scenario ◽  
Single User

Personal indoor localization with smartphones is a well-researched area, with a number of approaches solving the problem separately for individual users. Most commonly, a particle filter is used to fuse information from dead reckoning and WiFi or Bluetooth adapters to provide an accurate location of the person holding a smartphone. Unfortunately, the existing solutions largely ignore the gains that emerge when a single localization system estimates locations of multiple users in the same environment. Approaches based on filtration maintain only estimates of the current poses of the users, marginalizing the historical data. Therefore, it is difficult to fuse data from multiple individual trajectories that are usually not perfectly synchronized in time. We propose a system that fuses the information from WiFi and dead reckoning employing the graph-based optimization, which is widely applied in robotics. The presented system can be used for localization of a single user, but the improvement is especially visible when this approach is extended to a multi-user scenario. The article presents a number of experiments performed with a smartphone inside an office building. These experiments demonstrate that graph-based optimization can be used as an efficient fusion mechanism to obtain accurate trajectory estimates both in the case of a single user and in a multi-user indoor localization system. The code of our system together with recorded dataset will be made available when the paper gets published.

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