scholarly journals Enhanced spatial analysis of room acoustics using acoustic multiple-input multiple-output (MIMO) systems

2013 ◽  
Author(s):  
Hai Morgenstern ◽  
Boaz Rafaely
Keyword(s):  
Spatial Analysis ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Room Acoustics ◽  
Multiple Input ◽  
Input Multiple Output

PROBLEM STATEMENT OF JOINT ANTENNA SELECTION AND PRECODING IN MASSIVE MIMO COMMUNICATION SYSTEMS

2021 ◽  
Author(s):  
В.Б. КРЕЙНДЕЛИН ◽  
М.В. ГОЛУБЕВ
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Multiple Input Multiple Output ◽  
Research Areas ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Legal Restrictions ◽  
Multiple Transmission

Совместный с прекодингом автовыбор антенн на приемной и передающей стороне - одно из перспективных направлений исследований для реализации технологий Multiple Transmission and Reception Points (Multi-TRP, множество точек передачи и приема) в системах со многими передающими и приемными антеннами Massive MIMO (Multiple-Input-Multiple-Output), которые активно развиваются в стандарте 5G. Проанализированы законодательные ограничения, влияющие на применимость технологий Massive MIMO, и специфика реализации разрабатываемого алгоритма в миллиметровомдиапа -зоне длин волн. Рассмотрены алгоритмы формирования матриц автовыбора антенн как на передающей, так и на приемной стороне. Сформулирована строгая математическая постановка задачи для двух критериев работы алгоритма: максимизация взаимной информации и минимизация среднеквадратичной ошибки. Joint precoding and antenna selection both on transmitter and receiver sides is one of the promising research areas for evolving toward the Multiple Transmission and Reception Points (Multi-TRP) concept in Massive MIMO systems. This technology is under active development in the coming 5G 3GPP releases. We analyze legal restrictions for the implementation of 5G Massive MIMO technologies in Russia and the specifics of the implementation of the developed algorithm in the millimeter wavelength range. Algorithms of antenna auto-selection matrices formation on both transmitting and receiving sides are considered. Two criteria are used for joint antenna selection and precoding: maximizing mutual information and minimizing mean square error.

scholarly journals Beam division multiple access for millimeter wave massive MIMO: Hybrid zero-forcing beamforming with user selection

2022 ◽  
Vol 12 (1) ◽  
pp. 445
Author(s):  
Hong Son Vu ◽  
Kien Truong ◽  
Minh Thuy Le
Keyword(s):  
Multiple Access ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Zero Forcing ◽  
Multiuser Interference ◽  
Novel Approach ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Promising Solution

<p>Massive multiple-input multiple-output (MIMO) systems are considered a promising solution to minimize multiuser interference (MUI) based on simple precoding techniques with a massive antenna array at a base station (BS). This paper presents a novel approach of beam division multiple access (BDMA) which BS transmit signals to multiusers at the same time via different beams based on hybrid beamforming and user-beam schedule. With the selection of users whose steering vectors are orthogonal to each other, interference between users is significantly improved. While, the efficiency spectrum of proposed scheme reaches to the performance of fully digital solutions, the multiuser interference is considerably reduced.</p>

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.

scholarly journals Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems

2019 ◽  
Vol 9 (21) ◽  
pp. 4624
Author(s):  
Uzokboy Ummatov ◽  
Kyungchun Lee
Keyword(s):  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Threshold Value ◽  
Adaptive Threshold ◽  
Sphere Decoding ◽  
High Signal ◽  
Signal To Noise ◽  
Multiple Input ◽  
Input Multiple Output

This paper proposes an adaptive threshold-aided K-best sphere decoding (AKSD) algorithm for large multiple-input multiple-output systems. In the proposed scheme, to reduce the average number of visited nodes compared to the conventional K-best sphere decoding (KSD), the threshold for retaining the nodes is adaptively determined at each layer of the tree. Specifically, we calculate the adaptive threshold based on the signal-to-noise ratio and index of the layer. The ratio between the first and second smallest accumulated path metrics at each layer is also exploited to determine the threshold value. In each layer, in addition to the K paths associated with the smallest path metrics, we also retain the paths whose path metrics are within the threshold from the Kth smallest path metric. The simulation results show that the proposed AKSD provides nearly the same bit error rate performance as the conventional KSD scheme while achieving a significant reduction in the average number of visited nodes, especially at high signal-to-noise ratios.

scholarly journals An Evaluation of Successive Pilot Decontamination in Massive MIMO

2018 ◽  
Vol 39 (2) ◽  
pp. 107
Author(s):  
Victor Croisfelt Rodrigues ◽  
Taufik Abrão
Keyword(s):  
Spectral Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Pilot Training ◽  
Pilot Contamination ◽  
Fundamental Issue ◽  
Data Rates ◽  
Multiple Input ◽  
Input Multiple Output

The demand for higher data rates can be satisfied by the spectral efficiency (SE) improvement offered by Massive multiple-input multiple-output (M-MIMO) systems. However, the pilot contamination remains as a fundamental issue to obtain the paramount SE in such systems. This propitiated the research of several methods to mitigate pilot contamination. One of these procedures is based on the coordination of the cells, culminating in proposals with multiple pilot training phases. This paper aims to expand the results of the original paper, whereby the concepts of large pilot training phases were offered. The evaluation of such method was conducted through more comprehensible numerical results, in which a large number of antennas were assumed and more rigorous SE expressions were used. The channel estimation approaches relying on multiple pilot training phases were considered cumbersome for implementation and an uninteresting solution to overcome pilot contamination; contradicting the results presented in the genuine paper.

scholarly journals Partial Nulling Regularized Block Diagonalization Using Unfair Channel Selection for Post-Coding with Low-Complexity

2020 ◽  
Vol 10 (19) ◽  
pp. 6809
Author(s):  
Hyun-Sun Hwang ◽  
Jae-Hyun Ro ◽  
Young-Hwan You ◽  
Duckdong Hwang ◽  
Hyoung-Kyu Song
Keyword(s):  
Performance Enhancement ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Diversity Gain ◽  
Block Diagonalization ◽  
Time Diversity ◽  
Multiple Input ◽  
Input Multiple Output ◽  
User Interference

A number of requirements for 5G mobile communication are satisfied by adopting multi-user multiple input multiple output (MU-MIMO) systems. The inter user interference (IUI) which is an inevitable problem in MU-MIMO systems becomes controllable when the precoding scheme is used. The proposed scheme, which is one of the precoding schemes, is built on regularized block diagonalization (RBD) precoding and utilizes the partial nulling concept, which is to leave part of the IUI at the same time. Diversity gain is obtained by leaving IUI, which is made by choosing the row vectors of the channel matrix that are not nullified. Since the criterion for choosing the row vectors of the channel is the power of the channel, the number of selected row vectors of the channel for each device can be unfair. The proposed scheme achieves performance enhancement by obtaining diversity gain. Therefore, the bit error rate (BER) performance is better and the computational complexity is lower than RBD when the same data rate is achieved. When the number of reduced data streams is not enough for most devices to achieve diversity gain, the proposed scheme has better performance compared to generalized block diagonalization (GBD). The low complexity at the receiver is achieved compared to GBD by using the simple way to remove IUI.

Depth-First QRD-M Detection Algorithm for MIMO Systems

2013 ◽  
Vol 347-350 ◽  
pp. 3478-3481
Author(s):  
Li Liu ◽  
Jin Kuan Wang ◽  
Xin Song ◽  
Yin Hua Han ◽  
Yu Huan Wang
Keyword(s):  
Ordered Set ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
Detection Performance ◽  
Detection Scheme ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ml Detection

Maximum likelihood (ML) detection algorithm for multiple input multiple output (MIMO) systems provided the best bit error rate (BER) performance with heavy calculating complexity. The use of QR decomposition with M-algorithm (QRD-M) had been proposed to provide near-ML detection performance and lower calculating complexity. However, its complexity still grew exponentially with increasing dimension of the transmitted signal. To reduce the problem, an improved detection scheme was proposed here. After constructing the tree detecting model of MIMO systems, the ML search of one layer was done, the branch metrics were calculated and sorted, which gave an ordered set of the layer, then depth-first search were used to search the left layers with termination methods. The proposed algorithm provides near QRD-M detection performance.

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