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Magnitude and Wrap-Phase OFDM for MIMO Visible Light Communication Systems

IEEE COMMUNICATIONS LETTERS 2021年第7期25卷 2324-2328页

作者： Al-Nahhal, Mohamed Basar, Ertugrul Uysal, Murat Ozyegin Univ Dept Elect & Elect Engn TR-34794 Istanbul Turkey Koc Univ Dept Elect & Elect Engn CoreLab TR-34450 Istanbul Turkey

Generalized light emitting diode (LED) index modulation (GLIM) has been proposed as an effective multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) scheme for visible light communications (VLC) systems. In this letter, we propose a magnitude and wrap-phase OFDM (MW-OFDM) scheme for MIMO VLC systems. The proposed MW-OFDM system relies on the conversion of complex signals into polar form with the magnitudes and wrap-phases to decrease the restriction on the number of LEDs compared to the conventional GLIM-OFDM. Furthermore, maximum likelihood and best linear unbiased estimators of the proposed scheme are derived. The proposed MW-OFDM scheme improves the average bit error rate and provides a significant reduction in the decoding complexity, compared to the GLIM-OFDM. Moreover, a half number of LEDs are required for the proposed scheme to deliver the same spectral efficiency in a comparison with the GLIM-OFDM. Numerical results are presented over MIMO VLC channels to elaborate the superiority of our proposed scheme.

关键词： Visible light communications multiple-input multiple-output orthogonal frequency division multiplexing maximum likelihood estimator decoding complexity

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3 x 3 MIMO Fiber-Wireless System in W-Band With WDM/PDM RoF Transmission Capability

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2021年第24期39卷 7794-7803页

作者： Dat, Pham Tien Rottenberg, Francois Kanno, Atsushi Yamamoto, Naokatsu Kawanishi, Tetsuya Natl Inst Informat & Commun Technol Network Res Inst Tokyo 1840015 Japan Catholic Univ Louvain ICTEAM Inst B-1348 Louvain La Neuve Belgium Waseda Univ Sch Fundamental Sci & Engn Tokyo 1698050 Japan

We propose and demonstrate a 3 x 3 full multiple-input multiple-output (MIMO) fiber-wireless system in the W-band with wavelength-division multiplexing (WDM) and polarization-division multiplexing (PDM) radio-over-fiber (RoF) transmission capability. The system is realized using two highly stable RoF links based on the optical self-heterodyne method and PDM transmission in one of the RoF links. We experimentally demonstrate and confirm satisfactory performances for a 3 x 3 MIMO offset quadrature amplitude modulation-based filter-bank multicarrier signal. Total capacities of approximately 110 and 132 Gb/s, corresponding to spectral efficiencies of approximately 8.5 and 10.2 bits/s/Hz, respectively, are achieved for the system using antennas placed in the same and alternately different polarizations, respectively. The system is applicable to WDM and combined WDM/PDM RoF transmissions. It provides a scalable solution for facilitating large-scale MIMO signal transportation and can be a promising solution for future mobile transport and radio access networks in high-frequency bands.

关键词： MIMO communication Optical fiber polarization Optical fiber networks Wavelength division multiplexing Interference Wireless communication Digital signal processing Fifth-generation and beyond millimeter-wave mobile fronthaul multiple-input multiple-output radio-over-fiber seamless fiber-wireless system

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Knowledge Distillation-Aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems With Finite-Rate Feedback

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第10期70卷 11095-11100页

作者： Kong, Kyeongbo Song, Woo-Jin Min, Moonsik Pukyong Natl Univ Coll Liberal Arts Busan 48513 South Korea Pohang Univ Sci & Technol Dept Elect Engn Pohang 37673 South Korea Kyungpook Natl Univ Sch Elect Engn Daegu 41566 South Korea Kyungpook Natl Univ Sch Elect & Elect Engn Daegu 41566 South Korea

We propose a deep learning-based channel estimation, quantization, feedback, and precoding method for downlink multiuser multiple-input and multiple-output systems. In the proposed system, channel estimation and quantization for limited feedback are handled by a receiver deep neural network (DNN). Precoder selection is handled by a transmitter DNN. To emulate the traditional channel quantization, a binarization layer is adopted at each receiver DNN, and the binarization layer is also used to enable end-to-end learning. However, this can lead to inaccurate gradients, which can trap the receiver DNNs at a poor local minimum during training. To address this, we consider knowledge distillation, in which the existing DNNs are jointly trained with an auxiliary transmitter DNN. The use of an auxiliary DNN as a teacher network allows the receiver DNNs to additionally exploit lossless gradients, which is useful in avoiding a poor local minimum. For the same number of feedback bits, our DNN-based precoding scheme can achieve a higher downlink rate compared to conventional linear precoding with codebook-based limited feedback.

关键词： Precoding Receivers Channel estimation Auxiliary transmitters Downlink MIMO communication Quantization (signal) Deep learning multiple-input multiple-output limited feedback spatial multiplexing linear precoding

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ChannelAttention: Utilizing Attention Layers for Accurate Massive MIMO Channel Feedback

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IEEE WIRELESS COMMUNICATIONS LETTERS 2021年第5期10卷 1079-1082页

作者： Ji, Dong Jin Cho, Dong-Ho Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea

Recently the idea of using deep learning algorithms in massive multiple-input multiple-output channel state information feedback has been studied in great detail. To use the deep-learning-based feedback schemes over the air, they must be able to operate under extremely large antennas. These schemes also need to be verified under a realistic channel model and propagation environment. Thus, we propose ChannelAttention, a deep-learning-based channel state information feedback scheme that utilizes attention layers and residual blocks. Simulations for a 64-by-64 QuaDRiGa channel model based on the 3GPP 38.901 urban microcell scenario show that ChannelAttention surpasses the normalized mean square error and cosine similarity performance of the conventional CsiNet+ scheme across all compression ratio ranges.

关键词： Machine learning for communications deep learning channel feedback multiple-input multiple-output

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Enhanced cross-polarization isolation of loop-dipole antenna array backed by dielectric cavities for 5G base stations

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2021年第15期35卷 2034-2050页

作者： Da, Yiran Chen, Xiaoming Li, Mengting Zhang, Zhenyuan Al-Hadi, Azremi Abdullah Zhang, Anxue Kishk, Ahmed A. Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Aalborg Univ Dept Elect Syst Aalborg Denmark Univ Malaysia Perlis Fac Elect Engn Technol Arau Malaysia Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

Various decoupling methods were proposed and investigated for multiple-input multiple-output (MIMO) Base Station (BS) systems. These methods can effectively decouple the dual-polarized co-polar signal but fail to decouple the cross-polarized signal, similarly limiting the BS's overall decoupling performance. In this paper, different antenna arrays are designed to illustrate the coupling mechanism of cross-polarized BS antennas, and a comprehensive study of the relationship between antenna configurations and mutual couplings is conducted. It is shown that compact size antenna elements enlarge the edge-to-edge separation, and the loop-shaped dipole elements reduce the cross-polarization couplings. Finally, the array-antenna decoupling surface (ADS) method is applied in a 2x2 array composed of the designed antennas. Thanks to the improved antenna unit, the decoupling task is eased, and a better decoupling performance is achieved compared with the original ADS work.

关键词： Antenna configuration base station antennas decoupling multiple-input multiple-output mutual couplings wireless communication

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Hardware Implementation and Performance Analysis of Improved Sphere Decoder in Spatially Correlated Massive MIMO Channels

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2021年 2卷 2680-2694页

作者： Vordonis, Dimitris Paliouras, Vassilis Univ Patras Elect & Comp Engn Dept Patras 26504 Greece

Detection for high-dimensional multiple-input multiple-output (MIMO) and massive MIMO (MMIMO) systems is an active field of research in wireless communications. While most works consider spatially uncorrelated channels, practical MMIMO channels are correlated. This paper investigates the impact of correlation on Sphere Decoder (SD), for both single-user and multi-user scenarios. The complexity of SD is mainly determined by the initial radius method and the number of visited nodes during detection. This paper introduces a new constraint on the evaluation process of the partial distance thus modifying the conventional tree searching algorithm. This significantly decreases the number of visited nodes and renders SD feasible for large-scale systems. In addition, a proposed hardware implementation featured with a one-node-per-cycle architecture, minimizes the latency of the detection process. Tradeoffs between bit error rate performance and computational complexity are presented. The trade-offs are achieved by either modifying the backtracking mechanism or limiting the number of radius updates. Simulation results prove that the proposed optimizations are effective for both correlated and uncorrelated channels, regardless of the level of noise. The decoding gain of SD compared to the low-complexity linear detectors is higher in the presence of correlation than in the uncorrelated case. However, as expected, spatial correlation adversely affects the performance and the complexity of SD. Simulation results reported here also confirm that correlation at the side equipped with more antennas is less detrimental. Hardware implementation aspects are examined for both a Virtex-7 field-programmable gate array device and a 28-nm application-specific integrated circuit technology.

关键词： Correlation initial radius massive MIMO maximum likelihood decoding MIMO detection multiple-input multiple-output sphere decoding hardware architecture

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SMSP Mainlobe Jamming Suppression with FDA-MIMO Radar Based on FastICA Algorithm

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SENSORS 2023年第12期23卷 5619-5619页

作者： Wan, Pengfei Liao, Guisheng Xu, Jingwei Fu, Xiaolong Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China

In the electronic warfare environment, the performance of ground-based radar target search is seriously degraded due to the existence of smeared spectrum (SMSP) jamming. SMSP jamming is generated by the self-defense jammer on the platform, playing an important role in electronic warfare, making traditional radars based on linear frequency modulation (LFM) waveforms face great challenges in searching for targets. To solve this problem, an SMSP mainlobe jamming suppression method based on a frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar is proposed. The proposed method first uses the maximum entropy algorithm to estimate the target angle and eliminate the interference signals from the sidelobe. Then, the range-angle dependence of the FDA-MIMO radar signal is utilized, and the blind source separation (BSS) algorithm is used to separate the mainlobe interference signal and the target signal, avoiding the impact of mainlobe interference on target search. The simulation verifies that the target echo signal can be effectively separated, the similarity coefficient can reach more than 90% and the detection probability of the radar is significantly enhanced at a low signal-to-noise ratio.

关键词： smeared spectrum jamming frequency diverse array multiple-input multiple-output maximum entropy method blind source separation

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Multi-output time series forecasting with randomized multivariate Fuzzy Cognitive Maps

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CHAOS SOLITONS & FRACTALS 2023年 176卷

作者： Orang, Omid de Lima e Silva, Petronio Candido Guimaraes, Frederico Gadelha Univ Fed Minas Gerais Machine Intelligence & Data Sci MINDS Lab Belo Horizonte MG Brazil Fed Inst Educ Sci & Technol Northern Minas Gerais Januaria Campus Belo Horizonte MG Brazil Univ Fed Minas Gerais UFMG Dept Comp Sci Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Comp Sci Av Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil

Fuzzy Cognitive Maps (FCMs) have become a relevant technique for modeling and forecasting time series due to their advantages in dealing with uncertainty and simulating the dynamics of complex systems. Although numerous univariate and multivariate FCM-based forecasting models have been presented in the literature, one of the still open questions is how to enable FCMs to forecast multivariate time series for multiple-input, multiple-output (MIMO) systems with an efficient learning mechanism. from a computational point of view. This paper suggests a randomized MIMO FCM-based forecasting approach called MO-RHFCM to predict low -dimensional multivariate time series. More specifically, MO-RHFCM is a hybrid model merging the concepts of multivariate fuzzy time series, high order FCM (HFCM), and Echo State Networks (ESN). The structure of MO-RHFCM consists of three layers: input layer, reservoir (internal) layer, and output layer. Only the output layer is trainable using the Least Squares minimization algorithm;hence training the proposed MO-RHFCM method is fast and simple. The weights inside each sub-reservoir are selected randomly and remain fixed during the training process. The obtained results indicate the efficacy and validity of the proposed MO-RHFCM technique compared with some machine learning and deep learning baseline models.

关键词： Fuzzy Cognitive Maps multiple-input multiple-output Multivariate fuzzy time series Echo state network Least squares

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multiple Integrated Antennas for Wearable Fifth-Generation Communication and Internet of Things Applications

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IEEE ACCESS 2021年 9卷 120328-120346页

作者： Liao, Chia-Te Yang, Zhe-Kai Chen, Hua-Ming Air Force Inst Technol Dept Aviat Commun & Elect Kaohsiung 820 Taiwan Natl Kaohsiung Univ Sci & Technol Inst Photon Engn Kaohsiung 807 Taiwan

This paper proposes integrating multiple antennas into wearable devices for application in fifth-generation (5G) communications and the Internet of Things (IoT). The frequency bands covered were wireless local area network 2.4-GHz/5-GHz channels (2410-2484 MHz), the global positioning system (1575 MHz), narrowband IoT band 8 (880-960 MHz), Global System for Mobile Communications 850/900, and the 5G frequency range 1 n77/78/79 band (3300-5000 MHz). The antennas were configured as a 2 x 2 multiple-input multiple-output system (MIMO). The antenna resonance contribution integrates the slot mode, monopole mode of the three-dimensional frame, and loop coupling monopole mode. The configuration of the independent matching circuits facilitated the effective control of the desired mode and the coverage of the desired bands. All integrated antennas were placed in a watch device (37 x 43 x 8 mm(3)) with a metal body and a metal strap. An integrated miniaturized antenna was designed. The diversity gain and multiplexing efficiency between the MIMO antennas were 9.97dB and above -2dB, respectively. Favorable diversity performance was confirmed. The channel capacity of the 2 x 2 MIMO array was determined to be 8.8 bps/Hz. Moreover, its performance was evaluated through the addition of a hand tissue module. The impact of metal straps on antenna radiation performance was discussed. The specific absorption rate (SAR) was examined, and the best results obtained under an SAR of 1 g and 10 g were 0.25 and 0.58 W/kg, respectively. The findings demonstrate the suitability of the antenna for practical applications in 5G communication systems and the IoT.

关键词： Wearable computers multiple-input multiple-output metal frame metal strap impedance matching

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Evolutionary generative adversarial network based end-to-end learning for MIMO molecular communication with drift system

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NANO COMMUNICATION NETWORKS 2023年 37卷

作者： Zhu, Jiarui Bai, Chenyao Zhu, Yunlong Lu, Xiwen Wang, Kezhi Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China Brunel Univ London Dept Comp Sci Kingston Lane Uxbridge UB8 3PH England

Molecular communication (MC) is a novel paradigm for nano-communication networks. Compared with diffusion-based single-input single-out (SISO) systems, multiple-input multiple-output (MIMO) MC with drift systems can effectively mitigate the negative effects of inter symbol interference (ISI), inter link interference (ILI) and noise, further improving transmission efficiency. The modeling complexity of MIMO MC systems inspires the application of deep learning (DL) techniques to establish end-to-end architectures for signal recovery. However, training of the entire end-to-end system is limited by the unknown channel and small training sample size. In this paper, aiming at signal recovery of the newly developed mathematical MIMO MC with drift system model, a Kullback-Leibler divergence (KLD) evolutionary generative adversarial network (EGAN)-based end-to-end learning method is proposed. The end-to-end architecture can be trained offline with both the sampled and fake signals generated by KLD EGAN, even with a small training sample size, and then used to recover online transmitted signals directly. Besides, two traditional detection algorithms denoted as the maximum a posterior (MAP) detector and fixed threshold (FT) detector, are proposed as well for theoretical comparison. Experiments of the effect of different model parameters on the system performance have been carried out. Results validate the effectiveness and robustness of our proposed method compared to other DL-based methods, including the deep neural networks (DNN)-based, the original GAN-based, and the original EGAN-based, in terms of transmission accuracy. (c) 2023 Elsevier B.V. All rights reserved.

关键词： End-to-end learning Evolutionary computation Generative adversarial network Molecular communication multiple-input multiple-output

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