scholarly journals A Novel Channel Model and Optimal Beam Tracking Schemes for Mobile Millimeter-Wave Massive MIMO Communications

2021 ◽  
Author(s):  
Joydev Ghosh ◽  
Huiling Zhu ◽  
Huseyin Haci
Keyword(s):  
Millimeter Wave ◽  
Channel Model ◽  
Multiple Input Multiple Output ◽  
The Novel ◽  
Mimo Communications ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Optimal Beam ◽  
Beam Tracking ◽  
The Relationship

<div>A novel channel model has been proposed for mobile millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) communications to evaluate the effect of end-user mobility. In this model the variance of number of clusters and number of rays generated from each cluster is taken into account that is novel and different from widely used channel models. Two optimum codebook based beam-tracking schemes multi-objective joint optimization codebook (MJOC) and linear hybrid combiner (LHC) have been proposed for the novel channel model and their performance for spectral efficiency (SE) is presented. Performance for the two most commonly used channel state information (CSI) estimation approaches is investigated. Finally, the relationship between the beamforming training blocks and optimal beam tracking scheme is presented.</div>

scholarly journals A Novel Channel Model and Optimal Beam Tracking Schemes for Mobile Millimeter-Wave Massive MIMO Communications

2021 ◽  
Author(s):  
Joydev Ghosh ◽  
Huiling Zhu ◽  
Huseyin Haci
Keyword(s):  
Millimeter Wave ◽  
Channel Model ◽  
Multiple Input Multiple Output ◽  
The Novel ◽  
Mimo Communications ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Optimal Beam ◽  
Beam Tracking ◽  
The Relationship

<div>A novel channel model has been proposed for mobile millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) communications to evaluate the effect of end-user mobility. In this model the variance of number of clusters and number of rays generated from each cluster is taken into account that is novel and different from widely used channel models. Two optimum codebook based beam-tracking schemes multi-objective joint optimization codebook (MJOC) and linear hybrid combiner (LHC) have been proposed for the novel channel model and their performance for spectral efficiency (SE) is presented. Performance for the two most commonly used channel state information (CSI) estimation approaches is investigated. Finally, the relationship between the beamforming training blocks and optimal beam tracking scheme is presented.</div>

scholarly journals A Novel Channel Model and Optimal Beam Tracking Schemes for Mobile Millimeter-Wave Massive MIMO Communications

2021 ◽  
Author(s):  
Joydev Ghosh ◽  
Huiling Zhu ◽  
Huseyin Haci
Keyword(s):  
Millimeter Wave ◽  
Channel Model ◽  
Multiple Input Multiple Output ◽  
The Novel ◽  
Mimo Communications ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Optimal Beam ◽  
Beam Tracking ◽  
The Relationship

<div>A novel channel model has been proposed for mobile millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) communications to evaluate the effect of end-user mobility. In this model the variance of number of clusters and number of rays generated from each cluster is taken into account that is novel and different from widely used channel models. Two optimum codebook based beam-tracking schemes multi-objective joint optimization codebook (MJOC) and linear hybrid combiner (LHC) have been proposed for the novel channel model and their performance for spectral efficiency (SE) is presented. Performance for the two most commonly used channel state information (CSI) estimation approaches is investigated. Finally, the relationship between the beamforming training blocks and optimal beam tracking scheme is presented.</div>

scholarly journals Genetic Algorithm-Based Beam Refinement for Initial Access in Millimeter Wave Mobile Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hao Guo ◽  
Behrooz Makki ◽  
Tommy Svensson
Keyword(s):  
Genetic Algorithm ◽  
Millimeter Wave ◽  
Mobile Networks ◽  
System Performance ◽  
Multiple Input Multiple Output ◽  
Hardware Impairments ◽  
Mimo Networks ◽  
Multiple Input ◽  
Input Multiple Output ◽  
High Carrier

Initial access (IA) is identified as a key challenge for the upcoming 5G mobile communication system operating at high carrier frequencies, and several techniques are currently being proposed. In this paper, we extend our previously proposed efficient genetic algorithm- (GA-) based beam refinement scheme to include beamforming at both the transmitter and the receiver and compare the performance with alternative approaches in the millimeter wave multiuser multiple-input-multiple-output (MU-MIMO) networks. Taking the millimeter wave communications characteristics and various metrics into account, we investigate the effect of different parameters such as the number of transmit antennas/users/per-user receive antennas, beamforming resolutions, and hardware impairments on the system performance employing different beam refinement algorithms. As shown, our proposed GA-based approach performs well in delay-constrained networks with multiantenna users. Compared to the considered state-of-the-art schemes, our method reaches the highest service outage-constrained end-to-end throughput with considerably less implementation complexity. Moreover, taking the users’ mobility into account, our GA-based approach can remarkably reduce the beam refinement delay at low/moderate speeds when the spatial correlation is taken into account. Finally, we compare the cases of collaborative users and noncollaborative users and evaluate their difference in system performance.

Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems

2021 ◽  
Vol 17 (11) ◽  
pp. 155014772110553
Author(s):  
Xiaoping Zhou ◽  
Haichao Liu ◽  
Bin Wang ◽  
Qian Zhang ◽  
Yang Wang
Keyword(s):  
Millimeter Wave ◽  
Manifold Learning ◽  
Multiple Input Multiple Output ◽  
Restricted Boltzmann Machine ◽  
Boltzmann Machine ◽  
Hybrid Precoding ◽  
User Clustering ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Low Dimensional

Millimeter-wave massive multiple-input multiple-output is a key technology in 5G communication system. In particular, the hybrid precoding method has the advantages of being power efficient and less expensive than the full-digital precoding method, so it has attracted more and more attention. The effectiveness of this method in simple systems has been well verified, but its performance is still unknown due to many problems in real communication such as interference from other users and base stations, and users are constantly on the move. In this article, we propose a dynamic user clustering hybrid precoding method in the high-dimensional millimeter-wave multiple-input multiple-output system, which uses low-dimensional manifolds to avoid complicated calculations when there are many antennas. We model each user set as a novel Convolutional Restricted Boltzmann Machine manifold, and the problem is transformed into cluster-oriented multi-manifold learning. The novel Convolutional Restricted Boltzmann Machine manifold learning seeks to learn embedded low-dimensional manifolds through manifold learning in the face of user mobility in clusters. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.

scholarly journals MIMO Fading Emulator Development with FPGA and Its Application to Performance Evaluation of Mobile Radio Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Yoshio Karasawa ◽  
Katsuhiro Nakada ◽  
Guijiang Sun ◽  
Rikako Kotani
Keyword(s):  
Channel Model ◽  
Multiple Input Multiple Output ◽  
New Developments ◽  
Performance Limit ◽  
Stage Scheme ◽  
Mobile Radio Systems ◽  
Multiple Input ◽  
Field Programmable ◽  
Input Multiple Output ◽  
Radio Systems

We present four new developments for a multiple-input multiple-output (MIMO) over-the-air measurement system based on our previous studies. The first two developments relate to the channel model for multipath environment generation. One is a further simplification of the circuit configuration without performance degradation by reducing the number of delay generation units, which dominate the performance limit when implementing the circuit on a field-programmable gate array (FPGA). The other is to realize spatial correlation characteristics among the input ports on the transmission side, whereas the previously proposed channel model did not consider this correlation. The third development involves the details of implementing the MIMO fading emulator on an FPGA as a two-stage scheme. The fourth is the demonstration of application examples of the developed system.

scholarly journals Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1844
Author(s):  
Minhoe Kim ◽  
Woongsup Lee ◽  
Dong-Ho Cho
Keyword(s):  
Communication Systems ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Q Learning ◽  
Multiple Input ◽  
Beam Selection ◽  
Input Multiple Output ◽  
Optimal Beam ◽  
Resource Allocation Scheme

In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

Compact four-port vertically polarized UWB monopole antenna for MIMO communications

Circuit World ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Premalatha J. ◽  
Sheela D.
Keyword(s):  
Antenna Array ◽  
Performance Metrics ◽  
Multiple Input Multiple Output ◽  
Unit Cell ◽  
Ultra Wideband ◽  
Mimo Communications ◽  
Content Type ◽  
Directional Radiation ◽  
Multiple Input ◽  
Input Multiple Output

Purpose This paper aims to present the design of a compact vertically polarized four-element UWB antenna suitable for MIMO communications. Design/methodology/approach The unit cell antenna is constructed using a square ring radiator excited through a stepped impedance feed. The proposed antenna covers the Ultra-wideband (UWB) spectrum ranging from 2.2 to 12.3 GHz. The isolation between the unit cell antennas in the array is enhanced using a simple microstrip line resonator. The decoupling element is connected to the ground through a via. Findings The proposed scheme offers at least 16 dB improvement in the port-to-port coupling. Furthermore, the four-element antenna array is constructed using a specific interlocking scheme. The proposed antenna array’s Multiple Input Multiple Output (MIMO) performance metrics are analyzed. Originality/value By suitably selecting the excitation port, directional radiation patterns can be realized. The combined radiation pattern covers 360 degrees. A prototype antenna array is fabricated, and the simulation results are verified using real-time experiments. The proposed antenna is a suitable candidate for shark fin housing in automotive communications.

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