Design and preliminary evaluation of a compact four-element terminal multiple-input multiple-output antenna for receiving antenna selection

2011 ◽  
Vol 5 (7) ◽  
pp. 756
Author(s):  
X. Wang ◽  
W. Chen ◽  
J. Zheng ◽  
Y. Shen ◽  
Z. Feng
Keyword(s):  
Antenna Selection ◽  
Multiple Input Multiple Output ◽  
Preliminary Evaluation ◽  
Multiple Input ◽  
Receiving Antenna ◽  
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.

A Novel Convex Optimization for Receive Antenna Selection

2011 ◽  
Vol 186 ◽  
pp. 611-615
Author(s):  
Yong Wang ◽  
Hui Li
Keyword(s):  
Convex Optimization ◽  
Antenna Selection ◽  
Linear Equations ◽  
Multiple Input Multiple Output ◽  
Optimal Choice ◽  
Mimo Channels ◽  
Newton Step ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Receive Antenna

This paper proposes a new receive antenna selection algorithm based on the theory of convex optimization that improve the system performance over Rayleigh fading multiple-input multiple-output (MIMO) channels. The algorithm is based on approximated relaxed original optimization problem. The main effort in the approximated relaxed method is computing the Newton step for the centering problem, which consists of solving sets of linear equations constraints. The method produces not only a suboptimal choice of receive antennas, but also, a bound on how well the globally optimal choice does. The Monte-Carlo simulations show that the algorithm proposed can provide the performance very close to that of the optimal selection based on exhaustive search.

scholarly journals A Fast Adaptive Receive Antenna Selection Method in MIMO System

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Chaowei Wang ◽  
Weidong Wang ◽  
Cheng Wang ◽  
Shuai Wang ◽  
Yang Yu
Keyword(s):  
Mimo Systems ◽  
Antenna Selection ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
System Capacity ◽  
Adaptive Antenna ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Hardware Costs ◽  
Receive Antenna

Antenna selection has been regarded as an effective method to acquire the diversity benefits of multiple antennas while potentially reduce hardware costs. This paper focuses on receive antenna selection. According to the proportion between the numbers of total receive antennas and selected antennas and the influence of each antenna on system capacity, we propose a fast adaptive antenna selection algorithm for wireless multiple-input multiple-output (MIMO) systems. Mathematical analysis and numerical results show that our algorithm significantly reduces the computational complexity and memory requirement and achieves considerable system capacity gain compared with the optimal selection technique in the same time.

A Comparison of Norm Based Antenna Selection and Random Antenna Selection with Regard to Energy Efficiency in Wireless System with Large Number of Users

2020 ◽  
Vol 10 (2) ◽  
pp. 189-196
Author(s):  
Ashu Taneja ◽  
Nitin Saluja
Keyword(s):  
Energy Efficiency ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Transmit Antenna Selection ◽  
Wireless System ◽  
Multiple Input ◽  
Input Multiple Output

Background: The paper considers the wireless system with large number of users (more than 50 users) and each user is assigned large number of antennas (around 200) at the Base Station (BS). Objective: The challenges associated with the defined system are increased power consumption and high complexity of associated circuitry. The antenna selection is introduced to combat these problems while the usage of linear precoding reduces computational complexity. The literature suggests number of antenna selection techniques based on statistical properties of signal. However, each antenna selection technique suits well to specific number of users. Methods: In this paper, the random antenna selection is compared with norm-based antenna selection. It is analysed that the random antenna selection leads to inefficient spectral efficiency if the number of users are more than 50 in Multi-User Multiple-Input Multiple Output (MU-MIMO) system. Results: The paper proposes the optimization of Energy-Efficiency (EE) with random transmit antenna selection for large number of users in MU-MIMO systems. Conclusion: Also the computation leads to optimization of number of transmit antennas at the BS for energy efficiency. The proposed algorithm results in improvement of the energy efficiency by 27% for more than 50 users.

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