scholarly journals Predictive Wireless Channel Modeling of MmWave Bands Using Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3114
Author(s):  
Abdallah Mobark Aldosary ◽  
Saud Alhajaj Aldossari ◽  
Kwang-Cheng Chen ◽  
Ehab Mahmoud Mohamed ◽  
Ahmed Al-Saman
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Wireless Communication ◽  
Random Forests ◽  
Communication Systems ◽  
Channel Modeling ◽  
Base Station ◽  
Wireless Channel ◽  
Base Stations ◽  
Data Driven

The exploitation of higher millimeter wave (MmWave) is promising for wireless communication systems. The goals of machine learning (ML) and its subcategories of deep learning beyond 5G (B5G) is to learn from the data and make a prediction or a decision other than relying on the classical procedures to enhance the wireless design. The new wireless generation should be proactive and predictive to avoid the previous drawbacks in the existing wireless generations to meet the 5G target services pillars. One of the aspects of Ultra-Reliable Low Latency Communications (URLLC) is moving the data processing tasks to the cellular base stations. With the rapid usage of wireless communications devices, base stations are required to execute and make decisions to ensure communication reliability. In this paper, an efficient new methodology using ML is applied to assist base stations in predicting the frequency bands and the path loss based on a data-driven approach. The ML algorithms that are used and compared are Multilelayers Perceptrons (MLP) as a neural networks branch and Random Forests. Systems that consume different bands such as base stations in telecommunications with uplink and downlink transmissions and other internet of things (IoT) devices need an urgent response between devices to alter bands to maintain the requirements of the new radios (NR). Thus, ML techniques are needed to learn and assist a base station to fluctuate between different bands based on a data-driven system. Then, to testify the proposed idea, we compare the analysis with other deep learning methods. Furthermore, to validate the proposed models, we applied these techniques to different case studies to ensure the success of the proposed works. To enhance the accuracy of supervised data learning, we modified the random forests by combining an unsupervised algorithm to the learning process. Eventually, the superiority of ML towards wireless communication demonstrated great accuracy at 90.24%.

scholarly journals Reconfigurable Magneto-Electric Dipole Antennas for Base Stations in Modern Wireless Communication Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Ge ◽  
Xujun Yang ◽  
Zheng Dong ◽  
Dengguo Zhang ◽  
Xierong Zeng
Keyword(s):  
Wireless Communication ◽  
Communication Systems ◽  
Frequency Standard ◽  
Base Station ◽  
Base Stations ◽  
Wireless Communication Systems ◽  
Dipole Antennas ◽  
Base Station Antennas ◽  
Electric Dipole Antennas ◽  
Antenna Characteristics

Magneto-electric (ME) dipole antennas, with the function of changing the antenna characteristics, such as frequency, polarization, or radiation patterns, are reviewed in this paper. The reconfigurability is achieved by electrically altering the states of diodes or varactors to change the surface currents distributions or reflector size of the antenna. The purpose of the designs is to obtain agile antenna characteristics together with good directive radiation performances, such as low cross-polarization level, high front-to-back ratio, and stable gain. By reconfiguring the antenna capability to support more than one wireless frequency standard, switchable polarizations, or cover tunable areas, the reconfigurable ME dipole antennas are able to switch functionality as the mission changes. Therefore, it can help increase the communication efficiency and reduce the construction cost. This shows very attractive features in base station antennas of modern wireless communication applications.

scholarly journals Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Shahzad Hassan ◽  
Noshaba Tariq ◽  
Rizwan Ali Naqvi ◽  
Ateeq Ur Rehman ◽  
Mohammed K. A. Kaabar
Keyword(s):  
Machine Learning ◽  
Wireless Communication ◽  
Communication Systems ◽  
Short Term Memory ◽  
Classification Problem ◽  
Channel Equalization ◽  
Data Rate ◽  
Wireless Channel ◽  
Support Vector ◽  
Functional Link

Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.

scholarly journals Interference Cancellation Based Spectrum Sharing for Massive MIMO Communication Systems

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3584
Author(s):  
Milembolo Miantezila Junior ◽  
Bin Guo ◽  
Chenjie Zhang ◽  
Xuemei Bai
Keyword(s):  
Interference Cancellation ◽  
Communication Systems ◽  
Spectrum Sharing ◽  
Null Space ◽  
Base Station ◽  
Base Stations ◽  
Small Cells ◽  
Data Traffic ◽  
New Approach ◽  
Lte Advanced

Cellular network operators are predicting an increase in space of more than 200 percent to carry the move and tremendous increase of total users in data traffic. The growing of investments in infrastructure such as a large number of small cells, particularly the technologies such as LTE-Advanced and 6G Technology, can assist in mitigating this challenge moderately. In this paper, we suggest a projection study in spectrum sharing of radar multi-input and multi-output, and mobile LTE multi-input multi-output communication systems near m base stations (BS). The radar multi-input multi-output and mobile LTE communication systems split different interference channels. The new approach based on radar projection signal detection has been proposed for free interference disturbance channel with radar multi-input multi-output and mobile LTE multi-input multi-output by using a new proposed interference cancellation algorithm. We chose the channel of interference with the best free channel, and the detected signal of radar was projected to null space. The goal is to remove all interferences from the radar multi-input multi-output and to cancel any disturbance sources from a chosen mobile Communication Base Station. The experimental results showed that the new approach performs very well and can optimize Spectrum Access.

scholarly journals Physical-Layer Security Improvement with Reconfigurable Intelligent Surfaces for 6G Wireless Communication Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1439
Author(s):  
Janghyuk Youn ◽  
Woong Son ◽  
Bang Chul Jung
Keyword(s):  
Wireless Communication ◽  
Physical Layer Security ◽  
Communication Systems ◽  
Low Cost ◽  
Physical Layer ◽  
Base Station ◽  
Wireless Communication Systems ◽  
Secrecy Rate ◽  
Pilot Signal ◽  
Time Division

Recently, reconfigurable intelligent surfaces (RISs) have received much interest from both academia and industry due to their flexibility and cost-effectiveness in adjusting the phase and amplitude of wireless signals with low-cost passive reflecting elements. In particular, many RIS-aided techniques have been proposed to improve both data rate and energy efficiency for 6G wireless communication systems. In this paper, we propose a novel RIS-based channel randomization (RCR) technique for improving physical-layer security (PLS) for a time-division duplex (TDD) downlink cellular wire-tap network which consists of a single base station (BS) with multiple antennas, multiple legitimate pieces of user equipment (UE), multiple eavesdroppers (EVEs), and multiple RISs. We assume that only a line-of-sight (LOS) channel exists among the BS, the RISs, and the UE due to propagation characteristics of tera-hertz (THz) spectrum bands that may be used in 6G wireless communication systems. In the proposed technique, each RIS first pseudo-randomly generates multiple reflection matrices and utilizes them for both pilot signal duration (PSD) in uplink and data transmission duration (DTD) in downlink. Then, the BS estimates wireless channels of UE with reflection matrices of all RISs and selects the UE that has the best secrecy rate for each reflection matrix generated. It is shown herein that the proposed technique outperforms the conventional techniques in terms of achievable secrecy rates.

scholarly journals Implementation of conventional communication system in deep learning

2017 ◽  
Vol 7 (1.1) ◽  
pp. 696
Author(s):  
Satyanarayana P ◽  
Charishma Devi. V ◽  
Sowjanya P ◽  
Satish Babu ◽  
N Syam Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Communication Network ◽  
Deep Learning ◽  
Communication System ◽  
Communication Systems ◽  
Physical Layer ◽  
Learning Capacity ◽  
Constrained Learning ◽  
Complex Channel

Machine learning (ML) has been broadly connected to the upper layers of communication systems for different purposes, for example, arrangement of cognitive radio and communication network. Nevertheless, its application to the physical layer is hindered by complex channel conditions and constrained learning capacity of regular ML algorithms. Deep learning (DL) has been as of late connected for some fields, for example, computer vision and normal dialect preparing, given its expressive limit and advantageous enhancement ability. This paper describes about a novel use of DL for the physical layer. By deciphering a communication system as an auto encoder, we build up an essential better approach to consider communication system outline as a conclusion to-end reproduction undertaking that tries to together enhance transmitter and receiver in a solitary procedure. This DL based technique demonstrates promising execution change than traditional communication system.  

Introduction to Wireless Endogenous Security and Safety: Problems, Attributes, Structures and Functions

2021 ◽  
Author(s):  
Xiaoyan Hu ◽  
Liang Jin ◽  
Yangming Lou ◽  
Zhou Zhong ◽  
Xiaoli Sun
Keyword(s):  
Wireless Communication ◽  
Communication System ◽  
Communication Systems ◽  
Wireless Channel ◽  
Communication Security ◽  
Wireless Communication Systems ◽  
Functional Safety ◽  
Wireless Communication System ◽  
Safety Technology ◽  
Safety Attributes

Abstract: The information security and functional safety of wireless communication systems have become the focus of current research. The endogenous security principle based on Dynamic Heterogeneous Redundancy provides a direction for the development of wireless communication security and safety technology. This paper introduces the concept of wireless endogenous security from the following four aspects. First, we sorts out the endogenous security problems faced by the current wireless communication system, and then analyzes the endogenous security and safety attributes of the wireless channel. After that, the endogenous security and safety structure of the wireless communication system is given, and finally the applications of the existing wireless communication endogenous security and safety functions are listed. <br>

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