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IEEE International Conference on Communications (ICC)

作者： Fei Wang Xi Zhang Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs.

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Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-Based 6G Mobile Wireless Networks

Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-B...

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Annual Conference on information Sciences and Systems (CISS)

作者： Fei Wang Xi Zhang Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

We propose the novel scheme to solve a multi-objective optimization problem over an unmanned aerial vehicle (UAV) communications system to jointly minimize the energy consumption of the UAV and ground users (GUs). In particular, the UAV communicates with multiple GUs using active intelligent reflecting surfaces (IRSs), which can actively amplify and thus significantly enhance the strengths of reflected signals. We develop an energy minimization scheme based on the multi-objective hierarchical deep reinforcement learning (DRL), by decomposing the formulated optimization problem into two-layered subproblems. By solving the upper-level subproblem, we derive the optimal UAV trajectory and GUs scheduling strategies to minimize the UAV's energy consumption. By solving the lower-level subproblem, we obtain the IRS's phase shifts and amplification factors and GUs' transmit/receive beamforming to minimize the GUs' energy consumption. Finally, we validate and evaluate the proposed schemes through simulations, which show that the UAV's and GUs' energy consumption can be significantly reduced by using the active IRS, when the thermal noise powers at the IRS are much smaller than those at the UAV and GUs.

关键词： Energy consumption Communication systems Wireless networks Reinforcement learning Autonomous aerial vehicles Minimization Thermal noise

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Distributed Model Predictive Control Framework for Consensus of Multi-Agent System

Distributed Model Predictive Control Framework for Consensus...

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IEEE International Conference on Mechatronics and Automation

作者： Huaqing Li Lifeng Zheng Huiwei Wang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University No. 2 Tiansheng Road Beibei District Chongqing China

Considering the widespread applications of the model predictive control (MPC), such as industrial process control and robotic operations, this paper proposes an MPC-based asynchronous distributed algorithm to solve the consensus problem of multi-agent system (MAS) by using the operator splitting method. The proposed algorithm is asynchronous, which means that all agents do not require to update and communicate simultaneously. When the positive uncoordinated step sizes satisfy the given condition, the optimal solution can be worked out by using the proposed algorithm. Moreover, all agents are allowed to set the step sizes locally based on local information. Numerical simulation validates the feasibility and effectiveness of the proposed algorithm.

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A recurrent neural network for optimal energy management considering the battery cycle-life in smart grid

A recurrent neural network for optimal energy management con...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Miao Sun Xing He School of Microelectronics Fudan University Shanghai China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronics and Information Engineering Southwest University Chongqing China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

This paper proposes a combined battery energy management (CBEM) model against the background of smart grid, which focus on the absorptive ability of Battery Energy Storage System (BESS) distinguishing to the traditional energy management. To solve the CBEM model, an one-layer recurrent neural network (RNN) is recommended. Furthermore, by considering the peak and valley periods division prices, the generators absorption and the distribution network bought from power grid are optimized. Meanwhile, the emission cost is considered in total cost and there is a corresponding constraint to control the emission and the outputs of generators. Finally, the effectiveness of the CBEM model is verified on Matlab. With the fluctuation of electrical price, the charge model and discharge model of BESS are showed. Results indicate that this model performs well in cutting the load in peak time and storage energy in valley time.

关键词： BESS cycle-life cost emission cost control recurrent neural network energy management recurrent neural nets Energy management Smart grids Power grids Battery Cycle Life energy storage battery energy storage system

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Open Set Adversarial Domain Match for Electronic Nose Drift Compensation and Unknown Gas Recognition

SSRN

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SSRN 2023年

作者： Yao, Youbin Chen, Bin Feng, Changhao Liu, Chuanjun Gao, Xuliang Gu, Yun Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Department of Electronics Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan

Electronic nose (EN) is widely used for gas classification in practical applications. In the long-term open environments work, there often exists the unknown gases that the electronic nose cannot predict in advance. ENs need to resist the interference of these unknown gases in addition to overcoming long-term sensor drift problem. However, the present research cannot solve both the sensor drift and unknown gas intrusion problem simultaneously well. In this work, we unify above problems in to the open-set risk boundary. We propose an open-set adversarial domain match (OSADM) model and introduce the considers of open-set domain adaptation (OSDA). OSDA trains a target classifier through matching the domain distribution to recognize the known and unknown gases. First, a binary adversarial loss divides the class boundary. Secondly, adversarial domain adaptation unifies the distribution of different domains. Compared with the metric methods, it avoids complex distribution computation and parameter adjustment to reduce negative transfer. Extensive experimental results on two benchmark datasets, Gas Sensor Array Drift and Twin gas sensor arrays Dataset show that OSADM outperformance of the existing open-set models. © 2023, The Authors. All rights reserved.

关键词： Electronic nose

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Relay selections for cooperative underlay CR systems with energy harvesting

Relay selections for cooperative underlay CR systems with en...

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作者： Ye, Jia Liu, Zhedong Zhao, Hui Pan, Gaofeng Ni, Qiang Alouini, Mohamed-Slim Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom

In this paper, we consider a cooperative underlay cognitive radio system in which one secondary user (S) transmits its data to a secondary receiver with the assistance of multiple relays. In the considered underlay spectrum sharing scenario, the transmit power levers at S and forwarding relays are adjusted simultaneously according to a given threshold interference power that the primary user can tolerate. We also consider that relays adopt threshold decode-and-forward relay scheme and harvest energy from the primary transmitter with finite energy storage capacity under time switching scheme. Three best relay selection (RS) schemes, optimal source-relay link with wireless energy harvesting (WEH), optimal relay-destination link with WEH and optimal source-relay-destination link with WEH schemes, are proposed and studied. Closed-form analytical expressions for the outage probability of these three best RS schemes are derived, respectively. Finally, our analytical results are verified by Monte-Carlo simulations. © 2015 IEEE.

关键词： Energy harvesting

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Reversible Data Hiding Based on Improved Rhombus Prediction Method

Reversible Data Hiding Based on Improved Rhombus Prediction ...

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International Conference on intelligent Control and information processing (ICICIP)

作者： Simi Lu Xiaofeng Liao Nankun Mu Jiahui Wu Junqing Le Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Electronic and Information Engineering Southwest University Chongqing P.R. China Computer Science Chongqing University Chongqing P. R. China

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

Reversible data hiding(RDH) is a research hotspot in the field of information hiding. Among them, RDH based on histogram shift(HS) is a high performance algorithm. Accurate pixel prediction can reduce image distortion while maintaining high capacity. Therefore, this paper proposes an RDH algorithm based on the improved rhombus prediction method. Experiments show that the improved rhombus prediction method can predict pixels more accurately, and the generated prediction error histogram is more compact and clear. The proposed RDH algorithm has a higher embedding capacity and a lower distortion rate.

关键词： intelligent control information processing Airplanes

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(Rectified Version) The Barzilai-Borwein Method for Distributed Optimization over Unbalanced Directed Networks

arXiv

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arXiv 2023年

作者： Hu, Jinhui Chen, Xin Zheng, Lifeng Zhang, Ling Li, Huaqing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Big Data & Software Engineering Chongqing University Chongqing401331 PR China

This paper studies optimization problems over multi-agent systems, in which all agents cooperatively minimize a global objective function expressed as a sum of local cost functions. Each agent in the systems uses only local computation and communication in the overall process without leaking their private information. Based on the Barzilai-Borwein (BB) method and multi-consensus inner loops, a distributed algorithm with the availability of larger step-sizes and accelerated convergence, namely ADBB, is proposed. Moreover, owing to employing only row-stochastic weight matrices, ADBB can resolve the optimization problems over unbalanced directed networks without requiring the knowledge of neighbors’ out-degree for each agent. Via establishing contraction relationships between the consensus error, the optimality gap, and the gradient tracking error, ADBB is theoretically proved to converge linearly to the globally optimal solution. A real-world data set is used in simulations to validate the correctness of the theoretical analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Secrecy outage analysis of mixed RF-FSO downlink SWIPT systems

arXiv

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arXiv 2018年

作者： Lei, Hongjiang Dai, Zhijun Park, Ki-Hong Lei, Weijia Pan, Gaofeng Alouini, Mohamed-Slim Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Thuwal23955-6900 Saudi Arabia Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715

We analyze a secure dual-hop mixed radio frequency-free space optical (RF-FSO) downlink simultaneous wireless information and power transfer (SWIPT) systems. The FSO link and all RF links experience Gamma-Gamma, independent and identical Nakagami-mfading, *** analyze the effects of atmospheric turbulence, pointing error, detection technology, path loss, and energy harvesting on secrecy performance. Signal-to-noise ratios at both legitimate and illegitimate receivers are not independent since they are both simultaneously influenced by the FSO link. We derive the closedform expression of the secrecy outage probability (SOP) as well as the asymptotic result for SOP when signal-to-noise ratios at relay and legitimate destinations tend to infinity. Monte-Carlo simulations are performed to verify the accuracy of our analysis. The results show that the secrecy diversity order (SDO) depends on the fading parameter of the relay-destination link and the number of the destination's antennas. Additionally, the SDO also depends on the fading parameters, the pointing error parameter, and the detection type of the FSO link. Copyright © 2018, The Authors. All rights reserved.

关键词： Monte Carlo methods

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A Proportionate Maximum Total Complex Correntropy Algorithm for Sparse Systems

SSRN

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SSRN 2023年

作者： Huang, Sifan Qian, Guobing Wang, Xin Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Cyber Science and Engineering Sichuan University Chengdu610065 China

While much attention has been paid to the practical application of adaptive filters, two significant problems that have a great impact on performance need to be solved. One is that the sparsity of the system, and the other is the additive interference noise is impulsive noise. With the purpose of solving these issues at the same time, by introducing the proportionate matrix, a proportionate maximum total complex correntropy algorithm based on the errors-in-variables (EIV) model is proposed in this paper. We derive the theoretical analysis of the steady-state weight error power on the premise of impulsive noise. Meanwhile, the performance comparison in system identification is discussed and the robustness of the proposed algorithm is illustrated. Finally, the simulation with the stereophonic acoustic echo cancellation (SAEC) is carried out, and the result validates that the proposed PMTCC algorithm has obvious advantages. © 2023, The Authors. All rights reserved.

关键词： Impulse noise

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