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 Total Local Dose in Hypothetical 5G Mobile Networks for Varied Topologies and User Scenarios

2020 ◽  
Vol 10 (17) ◽  
pp. 5971 ◽  
Author(s):  
Sven Kuehn ◽  
Serge Pfeifer ◽  
Niels Kuster
Keyword(s):  
Mobile Networks ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Monte Carlo Analysis ◽  
5G Networks ◽  
Fifth Generation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Network Topologies ◽  
5G Mobile Networks

In this study, the total electromagnetic dose, i.e., the combined dose from fixed antennas and mobile devices, was estimated for a number of hypothetical network topologies for implementation in Switzerland to support the deployment of fifth generation (5G) mobile communication systems while maintaining exposure guidelines for public safety. In this study, we consider frequency range 1 (FR1) and various user scenarios. The estimated dose in hypothetical 5G networks was extrapolated from measurements in one of the Swiss 4G networks and by means of Monte Carlo analysis. The results show that the peak dose is always dominated by an individual’s mobile phone and, in the case of non-users, by the bystanders’ mobile phones. The reduction in cell size and the separation of indoor and outdoor coverage can substantially reduce the total dose by >10 dB. The introduction of higher frequencies in 5G mobile networks, e.g., 3.6 GHz, reduces the specific absorption rate (SAR) in the entire brain by an average of −8 dB, while the SAR in the superficial tissues of the brain remains locally constant, i.e., within ±3 dB. Data from real networks with multiple-input multiple-output (MIMO) were not available; the effect of adaptive beam-forming antennas on the dose will need to be quantitatively revisited when 5G networks are fully established.

scholarly journals Editorial: Special Issue “Massive MIMO and mm-Wave Communications”

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 519
Author(s):  
Gianmarco Romano
Keyword(s):  
Millimeter Wave ◽  
Massive Mimo ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Data Traffic ◽  
Special Issue ◽  
Editorial Special Issue ◽  
Multiple Input ◽  
Key Technologies ◽  
Input Multiple Output

Massive multiple-input multiple-output (mMIMO) communication systems and the use of millimeter-wave (mm-Wave) bands represent key technologies that are expected to meet the growing demand of data traffic and the explosion of the number of devices that need to communicate over 5G/6G wireless networks [...]

scholarly journals High Isolation Millimeter-Wave Wideband MIMO Antenna for 5G Communication

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Fei Wang ◽  
Zhaoyun Duan ◽  
Xin Wang ◽  
Qing Zhou ◽  
Yubin Gong
Keyword(s):  
Wireless Communications ◽  
Transmission Coefficient ◽  
Millimeter Wave ◽  
Multiple Input Multiple Output ◽  
Mimo Antenna ◽  
High Isolation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
The Stability ◽  
24 Ghz

A millimeter-wave wideband antenna is presented for the 5th generation applications. The operation band ranges from 24 GHz to 39 GHz which covers most of the Ka band. Furthermore, a 9×9 multiple-input-multiple-output (MIMO) antenna is developed. The high isolation is achieved without introducing external decoupling structures. The transmission coefficient is under −20 dB within only 0.4 mm space between antenna elements. The radiation pattern also shows the stability within the wide operation band. Both simulated and measured results show that this proposed MIMO antenna is suitable for the future wireless communications.

scholarly journals An Efficient Precoding Scheme for Millimeter-Wave Massive MIMO Systems

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 927 ◽  
Author(s):  
Alemaishat ◽  
Saraereh ◽  
Khan ◽  
Affes ◽  
Li ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Beam Control ◽  
Low Computational Complexity ◽  
Multiple Input ◽  
Input Multiple Output

Aiming at the problem of high computational complexity due to a large number of antennas deployed in mmWave massive multiple-input multiple-output (MIMO) communication systems, this paper proposes an efficient algorithm for optimizing beam control vectors with low computational complexity based on codebooks for millimeter-wave massive MIMO systems with split sub-arrays hybrid beamforming architecture. A bidirectional method is adopted on the beam control vector of each antenna sub-array both at the transmitter and receiver, which utilizes the idea of interference alignment (IA) and alternating optimization. The simulation results show that the proposed algorithm has low computational complexity, fast convergence, and improved spectral efficiency as compared with the state-of-the-art algorithms.

scholarly journals Artificial Intelligence Based LTE MIMO Antenna for 5th Generation Mobile Networks

2020 ◽  
Vol 2 (3) ◽  
pp. 155-162
Author(s):  
Dr. Abul Bashar
Keyword(s):  
Artificial Intelligence ◽  
Mobile Networks ◽  
Multiple Input Multiple Output ◽  
Long Term Evolution ◽  
Base Station ◽  
Fifth Generation ◽  
Term Evolution ◽  
Multiple Input ◽  
Input Multiple Output

Artificial intelligence based long term evolution multi in multi output antenna supporting the fifth generation mobile networks is put forth in the paper. The mechanism laid out in paper is devised using the monopole-antenna integrated with the switchable pattern. The long term evolution based multiple input and multiple output antenna is equipped with four antennas and capable of providing a four concurrent data streams quadrupling the theoretical maximum speed of data transfer allowing the base station to convey four diverse signals through four diverse transmit antennas for a single user equipment. The utilization of the long term evolution multiple input multiple output is capable of utilizing the multi-trial broadcasting to offer betterments in the signal performance as well as throughput and spectral efficiency when used along the fifth generation mobile networks. So the paper proposes the artificial intelligence based long term evolution multiple input multiple output four transmit antenna with four diverse signal transmission capacity that is operating in the frequency of 3.501 Gigahertz frequency. The laid out design is evaluated using the Multi-input Multi output signal analyzer to acquire the capacity of the passive conveyance of the various antennas with the diverse combination of patterns. The outcomes observed enables the artificial intelligence antenna to identify the choicest antenna to be integrated in the diverse environments for improving the throughput, signal performance and the data conveyance speed.

scholarly journals Massive Mimo and Beamforming Techniques of 5G Networks

2019 ◽  
Vol 9 (1S5) ◽  
pp. 139-142
Keyword(s):  
Millimeter Wave ◽  
Spectral Efficiency ◽  
Beam Steering ◽  
Multiple Input Multiple Output ◽  
Wave Band ◽  
Data Rates ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Millimeter Wave Band ◽  
Mimo Technology

The need of wireless communication is increasing day to day life and it is mostly depends on spectral efficiency and bandwidth. The current operating wireless technologies are ranging between 300MHz to 3GHz band; consequently the 5G wireless network depends up on high frequency millimeter wave band ranging between 3GHz to 300GHz. The spectral efficiency can be improved by using Massive Multiple Input Multiple Output (MIMO) Technology. In this paper we are discussing MIMO along with some emerging technologies are present in 5G, they are Millimeter Wave, Beam Forming, and Beam Steering. By using these technologies the capacity is increased, higher data rates will be obtained, latency can be reduced and enhanced quality of service will occur.

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