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An Optimized Algorithm for D2D-MIMO 5G Wireless Networks

Computers, Materials & Continua 2021年第9期68卷 3029-3044页

作者： Shahid Bashir Imran Khan Fahd NAl-Wesabi Nadhem Nemri Ammar Zahary Quang Ngoc Nguyen 无 Department of Business Studies Namal UniversityPakistan Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Communications and Computer Engineering Faculty of Science and Engineering Waseda University Shinjuku-kuTokyo169-0051Japan

The device-to-device(D2D)networking technology is extended to the conventional cellular network to boost the communication efficiency of the entire network,forming a heterogeneous 5G and beyond(B5G)communication network.D2D communication in a cellular cell will boost the efficiency of the spectrum,increase the ability of the device,and reduce the communication burden of base stations through the sharing of approved cell resources,causing serious interference as *** device-to-device(D2D)networking technology is extended to the conventional cellular network to boost the communication efficiency of the entire network,forming a heterogeneous 5G communication network.D2D communication in a cellular cell will boost the efficiency of the spectrum,increase the ability of the device,and reduce the communication burden of base stations through the sharing of approved cell resources,causing serious interference as *** paper proposes an efficient algorithm to minimize interference,based on the parity of the number of antennas,to resolve this *** primary concept is to generate the cellular connection precoding matrix by minimizing the power of interference from the base station to non-targeted *** through the criterion of maximum SINR,the interference suppression matrix of the cellular connection is ***,by removing intra-interference through linear interference alignment,the maximum degree of freedom is *** results of the simulation show that the proposed algorithm efficiently increases the performance of the spectrum,decreases interference,improves the degrees of freedom and energy efficiency compared to current algorithms.

关键词： 5G and beyond(B5G)networks device-to-device heterogeneous interference multiple-input and multiple-output

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CompactMIMOantenna using stepped impedance resonator-based metasurface for5GandWIFIapplications

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2021年第1期63卷 211-216页

作者： Wang, Zhan Dong, Yuandan Univ Elect Sci & Technol China Sch Elect Engn Chengdu Peoples R China

A compact metasurface-based multiple-input and multiple-output (MIMO) antenna using the stepped impedance resonator (SIR) for 5G and WIFI applications is investigated in this article. The SIR patch unit-cells are periodically etched on the two sides of a thin PCB substrate. As a result, the metal-insulator-metal (MIM) capacitor as the series left-hand (LH) capacitor is introduced into the right-handed transmission line (RH-TL). Besides, the RH inductor could be easily adjusted through the SIR patch. Based on the dispersion relation of the metamaterial-inspired SIR TL, a compact metasurface antenna is proposed and analyzed. A 3-cell MIMO antenna with a reduced oversize of 0.48 lambda 0 x 1.34 lambda 0 x 0.05 lambda(0)(with lambda(0)being the free-wavelength at 4.7 GHz.) for WIFI application is proposed and fabricated. Good agreement between measurement and simulation has been observed. Wide impedance bandwidth (covering 4.68-5.75 GHz, 20.5%) and good isolation (all below -25 dB) are achieved. The measured peak gain and radiation efficiency are greater than 7.8 dBi and 90%, respectively. Owing to a compact antenna configuration, wide bandwidth (covering both 5G sub-6GHz and WIFI bands), good radiation performance, and an easy fabrication process, this antenna is well suited for 5G or/and 5 GHz WIFI application in the base station.

关键词： compact antenna metasurface-inspired antenna multiple-input and multiple-output stepped impedance resonator sub-6GHz WIFI

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Quad Element MIMO Antenna for C, X, Ku, and Ka-Band Applications

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SENSORS 2023年第20期23卷 8563-8563页

作者： Mistri, Raj Kumar Mahto, Santosh Kumar Singh, Ajit Kumar Sinha, Rashmi Al-Gburi, Ahmed Jamal Abdullah Alghamdi, Thamer A. H. Alathbah, Moath Indian Inst Informat Technol Dept Elect & Commun Engn Ranchi 834010 Jharkhand India Natl Inst Technol Dept Elect & Commun Engn Jamshedpur 831014 Jharkhand India Univ Teknikal Malaysia Melaka UTeM Ctr Telecommun Res & Innovat CeTRI Fak Teknol & Kejuruteraan Elekt & Komputer FTKEK Jalan Hang Tuah Jaya Durian Tunggal 76100 Melaka Malaysia Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff CF24 3AA Wales King Saud Univ Coll Engn Dept Elect Engn Riyadh 11451 Saudi Arabia

This article presents a quad-element MIMO antenna designed for multiband operation. The prototype of the design is fabricated and utilizes a vector network analyzer (VNA-AV3672D) to measure the S-parameters. The proposed antenna is capable of operating across three broad frequency bands: 3-15.5 GHz, encompassing the C band (4-8 GHz), X band (8-12.4 GHz), and a significant portion of the Ku band (12.4-15.5 GHz). Additionally, it covers two mm-wave bands, specifically 26.4-34.3 GHz and 36.1-48.9 GHz, which corresponds to 86% of the Ka-band (27-40 GHz). To enhance its performance, the design incorporates a partial ground plane and a top patch featuring a dual-sided reverse 3-stage stair and a straight stick symmetrically placed at the bottom. The introduction of a defected ground structure (DGS) on the ground plane serves to provide a wideband response. The DGS on the ground plane plays a crucial role in improving the electromagnetic interaction between the grounding surface and the top patch, contributing to the wideband characteristics of the antenna. The dimensions of the proposed MIMO antenna are 31.7 mm x 31.7 mm x 1.6 mm. Furthermore, the article delves into the assessment of various performance metrics related to antenna diversity, such as ECC, DG, TARC, MEG, CCL, and channel capacity, with corresponding values of 0.11, 8.87 dB, -6.6 dB, +/- 3 dB, 0.32 bits/sec/Hz, and 18.44 bits/sec/Hz, respectively. Additionally, the equivalent circuit analysis of the MIMO system is explored in the article. It's worth noting that the measured results exhibit a strong level of agreement with the simulated results, indicating the reliability of the proposed design. The MIMO antenna's ability to exhibit multiband response, good diversity performance, and consistent channel capacity across various frequency bands renders it highly suitable for integration into multi-band wireless devices. The developed MIMO system should be applicable on n77/n78/n79 5G NR (3.3-5 GHz);WLAN (4.9-5.7

关键词： channel capacity channel capacity loss diversity gain mean effective gain mm-wave multiple-input and multiple-output total active reflection coefficient

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Autoencoder-Based Target Detection in Automotive MIMO FMCW Radar System

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SENSORS 2022年第15期22卷 5552页

作者： Kang, Sung-wook Jang, Min-ho Lee, Seongwook Korea Aerosp Univ Coll Engn Sch Elect & Informat Engn Goyang Si 10540 Gyeonggi Do South Korea

In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, the number of training cells, and the probability of false alarm are set improperly in the conventional CFAR algorithm, the target detection performance is severely degraded. Therefore, we propose a method using a convolutional neural network-based autoencoder (AE) to replace the CFAR algorithm in the multiple-input and multiple-output FMCW radar system. In the AE, the entire detection result is compressed at the encoder side, and only significant signal components are recovered on the decoder side. In this work, by changing the number of hidden layers and the number of filters in each layer, the structure of the AE showing a high signal-to-noise ratio in the target detection result is determined. To evaluate the performance of the proposed method, the AE-based target detection result is compared with the target detection results of conventional CFAR algorithms. As a result of calculating the correlation coefficient with the data marked with the actual target position, the proposed AE-based target detection shows the highest similarity with a correlation of 0.73 or higher.

关键词： autoencoder constant false alarm rate frequency-modulated continuous wave radar multiple-input and multiple-output target detection

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Transfer Learning for Semi-Supervised Automatic Modulation Classification in ZF-MIMO Systems

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2020年第2期10卷 231-239页

作者： Wang, Yu Gui, Guan Gacanin, Haris Ohtsuki, Tomoaki Sari, Hikmet Adachi, Fumiyuki Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China Rhein Westfal TH Aachen Fac Elect Engn & Informat Technol D-52062 Aachen Germany Keio Univ Dept Informat & Comp Sci Yokohama Kanagawa 2238521 Japan Tohoku Univ Res Org Elect Commun ROEC Sendai Miyagi 9808577 Japan

Automatic modulation classification (AMC) is an essential technology for the non-cooperative communication systems, and it is widely applied into various communications scenarios. In the recent years, deep learning (DL) has been introduced into AMC due to its outstanding identification performance. However, it is almost impossible to implement previously proposed DL-based AMC algorithms without large number of labeled samples, while there are generally few labeled sample and large unlabel samples in the realistic communication scenarios. In this paper, we propose a transfer learning (TL)-based semi-supervised AMC (TL-AMC) in a zero-forcing aided multiple-input and multiple-output (ZF-MIMO) system. TL-AMC has a novel deep reconstruction and classification network (DRCN) structure that consists of convolutional auto-encoder (CAE) and convolutional neural network (CNN). Unlabeled samples flow from CAE for modulation signal reconstruction, while labeled samples are fed into CNN for AMC. Knowledge is transferred from the encoder layer of CAE to the feature layer of CNN by sharing their weights, in order to avoid the ineffective feature extraction of CNN under the limited labeled samples. Simulation results demonstrated the effectiveness of TL-AMC. In detail, TL-AMC performs better than CNN-based AMC under the limited samples. What's more, when compared with CNN-based AMC trained on massive labeled samples, TL-AMC also achieved the similar classification accuracy at the relative high SNR regime.

关键词： Modulation MIMO communication Receiving antennas Transmitting antennas Equalizers Automatic modulation classification deep learning convolutional neural network transfer learning multiple-input and multiple-output

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Static Model Identification for Sendzimir Rolling Mill Using Noise Corrupted Operation Data

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IEEE ACCESS 2020年 8卷 150685-150695页

作者： Seo, Minseok Ban, Jaepil Koo, Bae Young Kim, Sang Woo Pohang Univ Sci & Technol Dept Elect Engn Pohang 37673 South Korea POSCO Pohang Res Lab Tech Res Labs Pohang 37859 South Korea

A Sendzimir rolling mill (ZRM), one of the rolling mill systems, is a machine used to obtain a steel strip with a desired shape in cold rolling. Model based controllers are mainly used for the shape control, but it is difficult to obtain the mathematical model of the ZRM, so model identification should be used. This study proposes a method identifying static model of the ZRM. To identify the static model of the ZRM, a mill matrix (G(m)) is obtained that expresses the linear relation between the actuators, and the shape of the strip and the data obtained through the ZRM's operation are used to obtain G(m). However, as the operation data are affected by large measurement noise, and the patterns of the multiple control inputs are not diverse, this results in an inaccurate estimation. Therefore, a data processing method using multiple valid sets of operation data is proposed to estimate G(m). Additionally, a G(m) update method is suggested to estimate the G(m) whenever a single pass operation is finished, according to the static model change of the plant. The proposed method is verified by comparing the results with the real operation data. This research will be helpful in all industries that use rolling mill machines such as 4-high mill, 6-high mill, and clustering mill in hot and cold rolling.

关键词： Cold rolling flatness control mill matrix model identification multiple-input and multiple-output rolling mills Sendzimir rolling mill steel industry static linear model steel strip Z-mill

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Detection and Localization of the Eavesdropper in MIMO Systems

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IEEE ACCESS 2020年 8卷 94984-94993页

作者： Ning, Lina Li, Bin Zhao, Chenglin Tao, Yiwen Wang, Xiang Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Beijing Jianyi Corp Ltd Beijing 100089 Peoples R China

The pilot spoofing attack (PSA) is one kind of active eavesdropping that happens in the channel training phase, in which an intelligent eavesdropper transmits identical pilot sequences synchronously with the legitimate user to spoof the transmitter. This attack leads to the estimated channel being a mix of a legitimate channel (the channel from the transmitter to the legitimate user) and an eavesdropper channel (the channel from the transmitter to the eavesdropper). And as a result, confidential information is leaked to the eavesdropper during the data transmission phase. Especially when the eavesdropper utilizes sufficiently large power, the channel rate at the legitimate user end decreases observably and increases dramatically at the eavesdropper. To against the active PSA, we propose a new effective scheme called the spatial spectrum method (SSM) which can be applied in situations in which the eavesdropper attacks not only the transmitter but also the legitimate user in multiple-input multiple-output (MIMO) communication systems. Specifically, this method utilizes the spatial spectrums that are attained by the uplink training phase to detect the eavesdropper. Besides it also can locate the legitimate user and the eavesdropper by identifying the direction-of-arrival (DOA) of the legitimate user and the eavesdropper based on the symmetry of the uplink and downlink channels in a time-division-duplex (TDD) system and estimating the geographical distance between the legitimate user and the eavesdropper. Consequently, the secure transmission of secret information can be guaranteed by utilizing spatial information, such as by adopting beamforming technology. Numerical results show that our method can effectively detect and locate the eavesdropper.

关键词： Training Channel estimation Uplink Transmitters Downlink MIMO communication Eavesdropping Physical layer security spatial spectrum method pilot spoofing attack multiple-input and multiple-output

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Target range-angle estimation based on time reversal FDA-MIMO radar

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INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES 2020年第4期12卷 267-275页

作者： Mu, Tong Song, Yaoliang Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing Peoples R China

Different from traditional multiple-input and multiple-output (MIMO) radar, the frequency diverse array MIMO (FDA-MIMO) radar generates beampattern that is dependent on both range and angle, making it applicable for joint range-angle estimation of targets. In this paper, we propose a novel time reversal based FDA-MIMO (TR-FDA-MIMO) approach for target detection. Based on the time reversal theory, the TR-FDA-MIMO signal model is established, the TR transmitting-receiving and signal processing procedure are analyzed, and the resulting range-angle spectra for targets imaging are acquired by utilizing the multiple signal classification algorithm. Numerical simulations are carried out for both single and multiple targets cases. The imaging resolution and robustness to the noise of the proposed approach are investigated and results are compared with conventional FDA-MIMO radar. It turned out that by cooperating with TR, the performance of FDA-MIMO radar for target range-angle estimation is effectively enhanced, consequently improving its applicability in practical target-detecting cases.

关键词： multiple-input and multiple-output frequency diverse array time reversal range-angle estimation

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Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第10期69卷 11353-11368页

作者： Chen, Jienan Feng, Wei Xing, Jing Yang, Ping Sobelman, Gerald E. Lin, Dengsheng Li, Shaoqian Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

Hybrid analog/digital processing is crucial for millimeter-wave (mmWave) MIMO systems due to its ability to balance the gain and cost. Despite fruitful recent studies, the optimal beamforming/combining method remains unknown for a practical multiuser, broadband mmWave MIMO equipped with low-resolution phase shifters and low-resolution analog-to-digital converters (ADCs). In this paper, we leverage artificial intelligence techniques to tackle this problem. Particularly, we propose a neural hybrid beamforming/combining (NHB) MIMO system, where the various types of hybrid analog/digital mmWave MIMO systems are transformed into a corresponding autoencoder (AE) based neural networks. Consequently, the digital and analog beamforming/combiners are obtained by training the AE based new model in an unsupervised learning manner, regardless of particular configurations. Using this approach, we can apply a machine learning-based design methodology that is compatible with a range of different beamforming/combing architectures. We also propose an iterative training strategy for the neural network parameter updating, which can effectively guarantee fast convergence of the established NHB model. According to simulation results, the proposed NHB can offer a significant performance gain over existing methods in terms of bit error rate (BER). Moreover, NHB can fast formulate the neural network parameters as channel changed, which is believed more promising for practice due to its better flexibility and compatibility.

关键词： Array signal processing Neural networks Radio frequency Phase shifters MIMO communication Training Optimization Artificial intelligence hybrid beamforming multiple-input and multiple-output millimeter wave

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Probability-Based Energy Reinforced Management of Electric Vehicle Aggregation in the Electrical Grid Frequency Regulation

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IEEE ACCESS 2020年 8卷 110598-110610页

作者： Dong, Chaoyu Sun, Jianwen Wu, Feng Jia, Hongjie Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Nanyang Technol Univ Energy Res Inst Singapore 637141 Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

The model uncertainties and the heterogeneous energy states burden the effective aggregation of electric vehicles (EVs), especially coupling with the real-time frequency dynamic of the electrical grid. Integrating the advantages of deep learning and reinforcement learning, deep reinforcement learning shows its potential to relieve this challenge, where an intelligent agent fully considers the individual state of charge (SOC) difference of EV and the grid state to optimize the aggregation performance. However, existing policies of deep reinforcement learning usually provide deterministic and certain actions, and it is difficult to deal with the increasing uncertainties and randomness in modern electrical systems. In this paper, a probability-based management strategy is proposed with continuous action space based on the deep reinforcement learning, which provides fine-grained energy management and addresses the time-varying dynamics from EVs and electrical grid simultaneously. Moreover, an optimization based on the proximal policy is further introduced to clip the policy upgradation speed to enhance the training stability. The effectiveness of proposed energy management structure and policy optimization strategy are verified on various scenarios and uncertainties, which demonstrates advantageous performance in the SOC management and frequency maintenance. Besides the performance merits, the training procedure is also presented revealing the evolution reason for the proposed approach.

关键词： Reinforcement learning Energy management Uncertainty State of charge Optimization Electric vehicles Training State of charge (SOC) deep reinforcement learning hybrid framework electric vehicle aggregator multiple-input and multiple-output

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