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Joint Beamforming and Power Allocation for Cognitive MIMO Systems Under Imperfect CSI Based on Game Theory

WIRELESS PERSONAL COMMUNICATIONS 2013年第3期73卷 679-694页

作者： Zhao, Feng Li, Bin Chen, Hongbin Lv, Xuezhi Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Minist Educ Guilin 541004 Peoples R China

The problem of joint beamforming and power allocation for cognitive multi-input multi-output systems is studied via game theory. The objective is to maximize the sum utility of secondary users (SUs) subject to the primary user (PU) interference constraint, the transmission power constraint of SUs, and the signal-to-interference-plus-noise ratio (SINR) constraint of each SU. In our earlier work, the problem was formulated as a non-cooperative game under the assumption of perfect channel state information (CSI). Nash equilibrium (NE) is considered as the solution of this game. A distributed algorithm is proposed which can converge to the NE. Due to the limited cooperation between the secondary base station (SBS) and the PU, imperfect CSI between the SBS and the PU is further considered in this work. The problem is formulated as a robust game. As it is difficult to solve the optimization problem in this case, existence of the NE cannot be analyzed. Therefore, convergence property of the sum utility of SUs will be illustrated numerically. Simulation results show that under perfect CSI the proposed algorithm can converge to a locally optimal pair of transmission power vector and beamforming vector, while under imperfect CSI the sum utility of SUs converges with the increase of the transmission power constraint of SUs.

关键词： Cognitive radio multi-input multi-output Power allocation Beamforming Imperfect channel state information Game theory

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Cooperative Beamforming With Nonlinear Power Amplifiers: A Deep Learning Approach for Distributed Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第5期72卷 5973-5988页

作者： Jee, Jeongju Kwon, Girim Park, Hyuncheol Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea MIT Lab Informat & Decis Syst Cambridge MA 02139 USA

Network densification is essential to enhance the areal capacity of future wireless networks, for which the user-centric network is a promising solution. In the perspective of network scalability, user association and cooperative beamforming (BF) among the base stations (BSs) should be designed elaborately to manage strong interference from nearby BSs. However, practical systems with hardware impairments including nonlinearity of power amplifiers may disturb these techniques and make the network difficult to be scalable. The nonlinearity of power amplifiers affects not only radio frequency (RF)-level signal but also performance according to the number of user equipments (UEs), resulting in necessity of re-designing network-level operations including BS-UE association. This paper proposes a deep learning-based cooperative BF framework for distributed networks with nonlinear power amplifiers. To optimize the cooperative BF for complicated nonlinear systems, we adopt unsupervised learning approach with constraints. To reduce the communication overhead of the network, we propose a novel neural network structure, from which the BSs can perform the cooperative BF in distributed manner. In particular, the information exchange between the central unit and the local BSs is reduced by designing neural network so that the local channel state information are utilized only in the local BSs while the central unit utilizes only covariance matrices. We show that the proposed scheme achieves a higher effective sum rate compared to the baseline schemes by adjusting the user association, BF, and power allocation to control interference and nonlinear distortion with reduced communication overhead.

关键词： MIMO communication Nonlinear distortion Interference Hardware Deep learning Optimization Neural networks distributed network cooperative beamforming multi-input multi-output nonlinear power amplifiers

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Decentralised power system load frequency control beyond the limit of diagonal dominance

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2002年第3期24卷 173-184页

作者： Yang, TC Ding, ZT Yu, H Univ Sussex Sch Informat Technol & Engn Brighton BN1 9QT E Sussex England

The design of decentralised robust load frequency control for interconnected multi-area power systems is studied in this paper. It is shown that although the design can be naturally formulated as a large-scale system decentralised control problem, it can be translated into an equivalent problem of decentralised controller design for a multi-input multi-output (MIMO) control system. It is known that simple controllers can be designed to achieve satisfactory performances if diagonal dominance can be achieved in a multivariable system. This is further extended in this paper. Using the design method proposed in this paper, even when the diagonal dominance cannot be achieved, subject to a condition based on the structured singular values (SSVs), each local area load-frequency controller can be designed independently. The robust stability condition for the overall system can be easily stated as to achieve a sufficient interaction margin, and a sufficient gain and phase margin during each independent design. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： multi-input multi-output structured singular values decentralised load frequency control

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A novel MIMO SMC for comprehensive unified control of the cascaded DC-DC and DC-AC converters in grid connected photovoltaic systems

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2024年第4期52卷 1794-1812页

作者： Radmand, Fereshteh Firouz, Mansour Hosseini Salimi, Mahdi Babaei, Ebrahim Islamic Azad Univ Dept Elect Engn Ardabil Branch Ardebil Iran Univ Greenwich Fac Engn & Sci Kent England Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Near East Univ Fac Engn Nicosia North Cyprus Turkiye

It is well known that DC-DC boost converters are cascaded with DC-AC inverters for grid connection of the photovoltaic (PV) systems. In the traditional control approaches, the mentioned DC-DC and DC-AC converters are controlled separately to facilitate the controller design problem. However, from the controller design viewpoint, the overall structure of the grid connected PV generator is a multi-input multi-output (MIMO) system. The duty cycle of the DC-DC converter and inverter modulation index are the control inputs and, on the other hand, generated photovoltaic DC power and exported power to the grid are control outputs. Moreover, the inverter DC link voltage should be stabilized by the closed-loop controller as well as an internal control output. If controllers are designed separately, it means that the interaction between DC-DC and DC-AC controllers isn't considered accurately, and since the isolated models of DC-DC converter and DC-AC inverter are extracted based on some approximated assumptions, separate controller design cannot guarantee stability and robustness of the whole system in a wide range of operation. To cope with these problems, in this paper, a novel MIMO sliding mode controller (SMC) is developed for comprehensive closed-loop control of the DC-DC boost converter cascaded with a single-phase DC-AC grid connected photovoltaic inverter. In the proposed approach, the dynamic model of whole system is developed comprehensively at first, and then, a unique MIMO controller is designed to control both DC-to-AC and DC-to-DC converters together. To cope with the nonlinear characteristic of the system and uncertainty of model parameters in a wide range, a fixed-frequency SMC is developed using the comprehensive state space model of the closed loop system. In the proposed MIMO-SMC controller, the AC power (which is exported to the grid) and operating point of the PV source are controlled via inverter modulation index and duty cycle of the DC-DC boost convert

关键词： cascaded converters DC-DC boost converter grid-connected inverter averaged state space model multi-input multi-output non-minimum phase

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DOA and DOD Estimation Based on Double 1-D Root-MVDR Estimators for Bistatic MIMO Radars

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WIRELESS PERSONAL COMMUNICATIONS 2016年第3期86卷 1321-1332页

作者： Chen, Shiaw-Wu Meng, Chao-Li Chang, Ann-Chen Feng Chia Univ Dept Automat Control Engn Taichung 407 Taiwan Feng Chia Univ PhD Program Elect & Commun Engn Taichung 407 Taiwan Ling Tung Univ Dept Informat Technol Taichung 408 Taiwan

This paper deals with direction of arrival (DOA) and direction of departure (DOD) estimation of existing targets based on polynomial root finding estimators with minimum variance distortionless response (MVDR) criterion in bistatic multi-input multi-output radar systems. First, the presented estimator transforms the conventional two-dimensional searching approach into double one-dimensional polynomial root-MVDR approach for DOA and DOD estimation. Thus, the pairing operation can be obtained automatically. Second, to mitigate the influence of noise corruption and the estimate radial bias, we also presented a differential polynomial root-MVDR estimator to obtain more accuracy estimation performance. Finally, simulation results are provided to verify the efficiency of the proposed estimators.

关键词： Bistatic radar multi-input multi-output Direction of departure Direction of arrival Polynomial rooting Minimum variance distortionless response

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Joint Detection and Communication System Design via Combination of Index and Phase Modulations

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 15690-15704页

作者： Men, Wei Du, Jun Yin, Jingwei Zhang, Liang Liu, Lei Ren, Yong Niyato, Dusit Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Minist Educ Key Lab Polar Acoust & Applicat Harbin 150001 Peoples R China Natl Key Lab Underwater Acoust Technol Harbin 150001 Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Key Lab Marine Informat Acquisit & Secur Minist Ind & Informat Technol Harbin 150001 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Peng Cheng Lab Network & Commun Res Ctr Shenzhen 518055 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

Joint detection and communication (JDC) systems can implement both functionalities simultaneously using the same hardware and software resources. This feature proves advantageous in reducing the size and power consumption of underwater vehicles. This paper develops a JDC system based on multi-input multi-output sonar by using orthogonal linear frequency modulation (OLFM) waveforms. Here, the proposed OLFM-based JDC system (OLFM-JDC) considers the detection functionality as the primary task. Therefore, OLFM-JDC exploits the mainlobe of transmit beam to detect targets and the sidelobes to communicate with the remote receivers. To enhance the information embedding capacity, the waveform diversity and the combination of index and phase modulations are utilized. Particularly, we propose a low-complexity two-step decoder to simplify the information decoding. Furthermore, the two-step decoder effectively utilizes multipath information to improve the performance of index decoding, and it can even outperform the maximum likelihood scheme when assessed within a simulated South China Sea acoustic channel. The numerical results demonstrate that OLFM-JDC achieves higher data rates and lower error rates compared to the JDC systems that only utilize phase modulation. Additionally, the simultaneous transmission of multiple waveforms facilitates target detection by utilizing the generalized high-resolution range profile synthesis technique. Performance analysis indicates that OLFM-JDC exhibits similar resolution performance to systems implementing a wideband waveform.

关键词： Vectors Receivers Radar Phase modulation Indexes Array signal processing OFDM Joint detection and communication multi-input multi-output orthogonal linear frequency modulation combination of index modulation and phase modulation two-step decoder range profile synthesis

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A two elementMIMOantenna for sub-6GHzandmmWave 5Gsystems using characteristics mode analysis

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2021年第2期63卷 587-595页

作者： Kumar, Naveen Khanna, Rajesh Thapar Inst Engn & Technol Dept Elect & Commun Engn POB 32Bhadson Rd Patiala Punjab India

In this article, a two-element multiband multi-input multi-output (MIMO) antenna for 5G communication devices is presented. Characteristics mode analysis is used to develop the proposed antenna. The planar inverted-F antenna (PIFA) structure is used as an antenna element operating at 3.5, 4.3, 28, and 35 GHz bands. The shape of the PIFA is L-shaped patch with an L slot placed which is positioned above the ground plane at 4 mm height. The proposed antenna covers more than 400 MHz bandwidth at 3.5, 4.3 GHz bands, and wideband coverage of more than 12 GHz covering many millimeter Wave (mmWave) bands between 24 and 38 GHz. The use of complementary metamaterial unit cell resulted in a minimum of 21 dB isolation in 3.5 GHz band and a minimum of 24 dB in mmWave bands. The simulated and measured results are presented for the proposed MIMO antenna system, which is in good agreement with each other.

关键词： 5G characteristic mode analysis mmWave multi-input multi-output planar inverted-F antenna

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Network traffic forecasting model based on long-term intuitionistic fuzzy time series

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INFORMATION SCIENCES 2020年 506卷 131-147页

作者： Fan, Xiaoshi Wang, Yanan Zhang, Mengyu Natl Def Univ PLA Jonit Logist Coll Beijing 100039 Peoples R China Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China Univ Int Business & Econ Beijing 100029 Peoples R China

In this paper, a network traffic forecasting model based on long-term intuitionistic fuzzy time series (LT-IFTS) is proposed. It describes the fuzziness and uncertainty of network flow and improves the traffic forecasting performance. The multi-input multi-output (MIMO) intuitionistic fuzzy time series forecasting model, namely, (p-q) IFTS is defined. An intuitionistic fuzzy time series vectors clustering algorithm based on vector variation pattern is given. The cluster centroid in the proposed model is quite different from the traditional method. As a kind of typical time series data, the network flow forecasting system is constructed particularly. Characteristic intuitionistic fuzzy is a practical method to manage the fuzziness and uncertainty of network traffic data. The network traffic data is intuitionistic fuzzified and vector quantized. The time series vectors are gathered based on the improved intuitionistic fuzzy c-means clustering and matched with centroids by coordinate translation. Compared with other traditional forecasting models, the improved FCM clustering algorithm increases discrimination of time series segments. In addition, the long-term scheme improves forecasting efficiency and reduces computational complexity than other single-output models. In experiments, the proposed model and relevant models are implemented on four different scales network traffic dataset from MAWI. The experiment result indicates that the proposed model is with better generalization performance. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Intuitionistic fuzzy time series Vector quantization Network traffic Long-term forecasting multi-input multi-output

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Transferable Takagi-Sugeno-Kang Fuzzy Classifier With multi-Views for EEG-Based Driving Fatigue Recognition in Intelligent Transportation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第12期24卷 15807-15817页

作者： Gu, Yi Xia, Kaijian Lai, Khin-Wee Jiang, Yizhang Qian, Pengjiang Gu, Xiaoqing Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Univ Malaya Fac Engn Dept Biomed Engn Kuala Lumpur 50603 Malaysia Changzhou Univ Sch Comp Sci & Artificial Intelligence Changzhou 213164 Peoples R China

The safety monitoring system of intelligent transportation provides driving fatigue warning and risk control. Electroencephalogram (EEG) signals can directly reflect the neuronal activity of the brain. The detection and early warning of driving fatigue using EEG signals has important practical significance. However, because of the non-stationarity and timeliness of EEG signals, the single feature detection method is significantly impacted by data distribution differences. In this paper, in the framework of multi-input multi-output (MIMO) Takagi-Sugeno-Kang (TSK) fuzzy system, transferable TSK fuzzy classifier with multi-views (T-TSK-MV) is developed for EEG-based driving fatigue recognition in intelligent transportation. First, in view-specific consequent parameter learning, the view-specific consequent regularizer is designed based on technologies of ridge regression, maximum mean discrepancy (MMD), and manifold regularization, which becomes the bridge to transfer the discriminative information from the related domain to the target domain. In addition, the $\ell_{2,1} $ -norm sparse constraint on consequent parameters is used to simplify fuzzy rules. Then multi-view learning is integrated into the consequent parameter learning, in which T-TSK-MV explores the view-shared consequent regularizer and adaptively assigns weights to each view. The $\ell_{2,1} $ -norm sparse constraint on view-shared consequent regularizer can effectively exploit the local structure of multi-view data. Finally, the fuzzy classifier is constructed on view-specific regularizers and view weights. The experiment on real-word datasets shows that the proposed fuzzy classifier can significantly improve the driving fatigue recognition performance.

关键词： multi-input multi-output Takagi-Sugeno-Kang fuzzy classifier electroencephalogram driving fatigue recognition

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Flexible Frequency-Band Reallocation: Complex Versus Real

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2009年第3期28卷 409-431页

作者： Eghbali, Amir Johansson, Hakan Lowenborg, Per Linkoping Univ Div Elect Syst Dept Elect Engn S-58183 Linkoping Sweden

This paper discusses a new approach for implementing flexible frequency-band reallocation (FFBR) networks for bentpipe satellite payloads which are based on variable oversampled complex-modulated filter banks (FBs). We consider two alternatives to process real signals using real input/output and complex input/output FFBR networks (or simply real and complex FFBR networks, respectively). It is shown that the real case has a lower overall number of processing units, i.e., adders and multipliers, compared to its complex counterpart. In addition, the real system eliminates the need for two Hilbert transformers, further reducing the arithmetic complexity. An analysis of the computational workload shows that the real case has a smaller rate of increase in the arithmetic complexity with respect to the prototype filter order and number of FB channels. This makes the real case suitable for systems with a large number of users. Furthermore, in the complex case, a high efficiency in FBR comes at the expense of high-order Hilbert transformers;thus, trade-offs are necessary. Finally, the performance of the two alternatives based on the error vector magnitude (EVM) for a 16-quadrature amplitude modulation (QAM) signal is presented.

关键词： Flexible frequency-band reallocation multi-input multi-output Discrete Fourier transform Hilbert transformer Arithmetic complexity Error vector magnitude

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