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Robust beamforming design for multiple-input-single-output secrecy multicasting systems with simultaneous wireless information and power transmission

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IET COMMUNICATIONS 2016年第15期10卷 1979-1985页

作者： Zhu, Zhengyu Chu, Zheng Wang, Zhongyong Cui, Jianhua Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China Newcastle Univ Sch Elect & Elect Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England Luoyang Normal Univ Sch Phys & Elect Informat Luoyang Peoples R China

In this study, the authors study simultaneous wireless information and power transfer for multiuser multiple-input-single-output secure multicasting channels with imperfect channel state information. First, a robust secure beamforming design is considered, where the transmit power is minimised subject to the secrecy rate outage probability constraint for legitimate users and the harvested energy outage probability constraint for energy harvesting receivers. The original problem is non-convex due to the presence of the probabilistic constraints. By utilising Bernstein-type inequalities, the authors transform the outage constraints into the deterministic forms. In order to identify a local optimal rank-one solution, the authors propose an efficient approach based on a constrained concave convex procedure method to convert the original problem into a sequence of convex programming problems. Finally, simulation results are provided to validate the performance of the proposed design methods.

关键词： energy harvesting array signal processing telecommunication security wireless channels multicast communication multiuser channels concave programming convex programming probability robust secure beamforming design multiple-input-single-output secrecy multicasting systems simultaneous wireless information-and-power transmission multiuser multiple-input-single-output secure multicasting channels imperfect channel state information secrecy rate outage probability constraint harvested energy outage probability constraint energy harvesting receivers nonconvex problem Bernstein-type inequalities local optimal rank-one solution constrained concave convex procedure method convex programming problems

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Sparse signal recovery via minimax- concave penalty and l 1-norm loss function

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IET SIGNAL PROCESSING 2018年第9期12卷 1091-1098页

作者： Sun, Yuli Chen, Hao Tao, Jinxu Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Anhui Peoples R China

In sparse signal recovery, to overcome the l 1- norm sparse regularisation's disadvantages tendency of uniformly penalise the signal amplitude and underestimate the high- amplitude components, a new algorithm based on a non- convex minimax- concave penalty is proposed, which can approximate the l 0- norm more accurately. Moreover, the authors employ the l 1- norm loss function instead of the l 2- norm for the residual error, as the l 1- loss is less sensitive to the outliers in the measurements. To rise to the challenges introduced by the non- convex non- smooth problem, they first employ a smoothed strategy to approximate the l 1- norm loss function, and then use the difference- of- convex algorithm framework to solve the nonconvex problem. They also show that any cluster point of the sequence generated by the proposed algorithm converges to a stationary point. The simulation result demonstrates the authors' conclusions and indicates that the algorithm proposed in this study can obviously improve the reconstruction quality.

关键词： signal reconstruction minimax techniques approximation theory concave programming sparse signal recovery nonconvex minimax-concave penalty (1)-norm loss function (1)-norm sparse regularisation signal amplitude high-amplitude components nonconvex nonsmooth problem difference-of-convex algorithm framework nonconvex problem cluster point reconstruction quality

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Jointly optimal rate control and total transmission power for cooperative cognitive radio system

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IET COMMUNICATIONS 2017年第11期11卷 1679-1688页

作者： Soleimanpour-moghadam, Mohadese Talebi, Siamak Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

In this study, the authors apply the multi-objective optimisation (MOO) methods to the challenge posed by joint rate maximisation and total transmission power (TTP) minimisation in cooperative cognitive radio networks. The proposed MOO methods which are based on amplify and forward relaying strategy optimise the two conflicting objectives and, at same time, they maximise the rate quality and minimise the TTP allocated to the network relays simultaneously. The MOO problem under investigation is a non-convex non-linear combinatorial optimisation one that three MOO methods are presented for a desired solution. The explored methods are: weighted sum algorithm (which is based on a simplistic model), MOO fractional programming method (which has much in common with their MOO problem) and lexicographic algorithm (which is suitably adapted to complex combinatorial optimisation environments due to its robustness in avoiding trapping in local optima). Their simulation results confirm the proposed methods' effectiveness for simultaneous rate maximisation and TTP minimisation as well as their superiority over counterpart algorithms.

关键词： cooperative communication cognitive radio minimisation optimal control amplify and forward communication relay networks (telecommunication) concave programming nonlinear programming optimal rate control total transmission power multiobjective optimisation methods joint rate maximisation and total transmission power minimisation TTP minimisation cooperative cognitive radio networks amplify and forward relaying strategy rate quality maximisation nonconvex nonlinear combinatorial optimisation weighted sum algorithm MO fractional programming method lexicographic algorithm MOO problem

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Exact relaxation of complementary constraints for optimal bidding strategy for electric vehicle aggregators

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IET RENEWABLE POWER GENERATION 2022年第12期16卷 2493-2507页

作者： Chen, Dapeng Jing, Zhaoxia Li, Zhigang Xu, Hedong Ji, Tianyao South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China China Elect Power Planning & Engn Inst Beijing Peoples R China Jinan Univ Inst Finance Guangzhou Peoples R China

This paper concentrates on the optimal bidding strategy of a plug-in electric vehicle aggregator (PEVA) using indirect load control in the day-ahead energy market, which is generally formulated as bilevel programming. However, this bilevel model is nonconvex and intractable due to the complementary constraints imposed to preclude plug-in electric vehicles (PEVs) from simultaneously charging and discharging. To handle this problem, an exact relaxation method is first proposed to remove the complementary constraints of the PEVs' charging and discharging behaviours. To further reduce the complexity of the model, a method is proposed for the exact relaxation of the complementary slackness of Karush-Kuhn-Tucker (KKT) conditions in the reformulated mathematical program with equilibrium constraints. By removing the complementary constraints, the relaxation methods can effectively reduce the complexity of the model and improve the computational performance. The results of the case study demonstrate the exactness and efficiency of the proposed relaxation methods. Specifically, in the test case with 1000 PEVs and 20 scenarios, 220,000 constraints are removed by the two exact relaxation methods. Moreover, using the second exact relaxation method, in the test case with 400 PEVs and 60 scenarios and the case with 1000 PEVs and 60 scenarios, the computational time drops by 30.3% and 23.3%, respectively.

关键词： battery powered vehicles KKT condition PEVA electric vehicle charging power markets plug-in electric vehicle aggregator concave programming equilibrium constraints mathematical programming tendering PEV charging-discharging behaviour Karush-Kuhn-Tucker condition bidding strategy relaxation methods nonconvex model day-ahead energy market bilevel programming complementary constraints mathematical program

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Empirical continuous metaheuristic for multiple distributed generation scheduling considering energy loss minimisation, voltage and unbalance regulatory limits

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第16期14卷 3301-3309页

作者： Coelho, Francisco C. R. Peres, Wesley Silva Junior, Ivo C. Dias, Bruno H. Univ Fed Sao Joao del Rei Dept Elect Engn Sao Joao Del Rei Brazil Univ Fed Juiz de Fora Dept Elect Energy Juiz De Fora Brazil

Distributed generation (DG) and other electric resources such as batteries and electric vehicles are transforming the planning and operation of power distribution system all over the world. Although the operation gets more complex in the presence of DG, it also brings some potential benefits to the grid. In this study, the authors propose an optimisation approach for multiple DG units scheduling, considering a daily load profile. The main objective is to minimise the total energy loss in a period of time, dealing with a specific voltage and unbalance constraints, required by the Brazilian Electricity Regulatory Agency. The problem formulation results in a discontinuous non-convex objective function. An empirical continuous metaheuristic (ECM) is proposed to solve this challenging optimisation problem. As metaheuristic methods are suitable for this kind of problems, they present some limitations regarding final results variability, relative dependence on initial conditions and usually a large set of parameters to tune. ECM confronts directly these limitations, presenting good quality results in comparison to other well-known algorithms. By using the Open Distribution System Simulator - OpenDSS, and the well-known IEEE-123 distribution system, the proposed approach shows its effectiveness and efficiency to optimise the grid operation, with special attention to the Brazilian requirements for unbalance.

关键词： distributed power generation power generation scheduling minimisation power grids concave programming ECM open distribution system simulator IEEE-123 distribution system grid operation Brazilian requirements multiple distributed generation scheduling energy loss minimisation unbalance regulatory limits electric resources electric vehicles power distribution system planning optimisation approach daily load profile specific voltage unbalance constraints Brazilian Electricity Regulatory Agency OpenDSS multiple DG unit scheduling total energy loss minimization discontinuous nonconvex objective function empirical continuous metaheuristic method

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Topology identification in distribution networks based on alternating optimisation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第24期14卷 5851-5857页

作者： Feng, Renhai Yuan, Wanqi Xiao, Meng Zhao, Zheng Wang, Qiulin Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Tianjin Univ Sch Microelect Tianjin Peoples R China

Topology identification (TI) is an essential problem in the distribution network due to exponentially growing power grid size in recent years. In this study, it is reformulated as a regularised alternating convex optimisation problem. Then an application based on the current injection model is proposed. Compared to the traditional algorithm optimising l(1)-norm, which may lead to overfitting, a new l(2)-norm minimisation problem with l(1)-norm regularisation is proposed to solve the trade-off problem with non-convex constraints. The proposed method reduces the size of the training data set compared with the traditional TI method. Simulation results show that the recovery performance of the proposed algorithm is superior to the traditional one in additive white Gaussian noise scenario.

关键词： power grids convex programming minimisation distribution networks concave programming l(1)-norm regularisation l(2)-norm minimisation problem topology identification nonconvex constraints trade-off problem current injection model regularised alternating convex optimisation problem power grid size distribution network

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Multicasting in NOMA-based UAV networks: Path design and throughput maximisation

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IET COMMUNICATIONS 2022年第14期16卷 1708-1723页

作者： Hosseini, Shima Salar Javan, Mohammad Reza Nazari, Ali Shahrood Univ Technol Dept Elect Engn Shahrood Iran

In this paper, a new resource allocation framework for unmanned aerial vehicle (UAV) assisted multicast wireless networks is proposed in which the network users according to their request are divided into several multicast groups. Power domain non-orthogonal multiple access is adopted as the transmission technology using which the dedicated signals of multicast groups are superimposed and transmitted simultaneously as the UAV passes over the communication area for fixed and mobile users. The proposed scenarios are discussed from two perspectives, offline and online modes. In offline mode, the problem is implemented for fixed and mobile users whose locations are predictable (the location of users, over the communication time, is known at the beginning of the communication time) and in online mode for mobile users whose locations are unpredictable (the location of users, over the communication time, is unknown at the beginning of the communication time). Also, a scenario is proposed in which the online mode the number of mobile users can grow in each time slot. The problem of joint power allocation and UAV trajectory design as an optimisation problem that is non-linear and non-convex for two proposed scenarios is formulated. To solve the problem, an alternate search method, successive convex approximation, and geometric programming are adopted. Using simulation results, the performance of the proposed scheme is evaluated for different values of the network parameters.

关键词： network users time slot autonomous aerial vehicles network parameters UAV trajectory design multicasting wireless channels communication area throughput maximisation convex programming resource allocation communication time optimisation concave programming telecommunication power management NOMA-based UAV networks approximation theory free-space optical communication multicast groups unmanned aerial vehicle offline mode nonorthogonal multiple access mobile users power domain nonorthogonal telecommunication control online mode wireless networks geometric programming joint power allocation online modes fixed users resource allocation framework

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Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

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IET COMMUNICATIONS 2019年第13期13卷 1971-1979页

作者： Choubin, Morteza Taherpour, Abass Rahmani, Mehdi Imam Khomeini Int Univ Dept Elect Engn Qazvin Iran

In this study, the authors study the collaborative data aggregation using multiple antennas sensors and fusion centre (FC) with energy harvesting capability in the wireless sensor network (WSN). The optimisation problem is formulated to improve the data transfer rate, based on the parameters of collaboration among sensors, the energy harvesting, and storage of each sensor. In particular, they observe several practical constraints for energy harvesting and capability battery energy storage to maintain network connectivity. They propose three scenarios based on the number of antennas for transferring, collecting, and sharing the data on sensor and FC. It is shown these optimisation problems are a non-convex and to resolve this issue, the objective function is converted to a convex function using a relaxation method. The numerical results show the impact of different parameters on the data rate at FC and improvement in network connection and throughput by using proposed collaborative data aggregation techniques compared to their counterparts.

关键词： wireless sensor networks energy harvesting sensor fusion data aggregation concave programming convex programming antennas relaxation theory telecommunication power management energy harvesting capability WSN wireless sensor network optimisation problem data transfer rate collaborative data aggregation techniques battery energy storage multiple antenna sensors sensor fusion centre FC nonconvex problem convex function relaxation method

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Max-min fairness based linear transceiver-relay design for MIMO interference relay channel

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IET COMMUNICATIONS 2017年第9期11卷 1485-1496页

作者： Singh, Vindheshwari P. Chaturvedi, Ajit Kumar Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India

In this work, the authors investigate a multi-user multi-input multi-output (MIMO) interference relay system, where several transmitter nodes simultaneously communicate with their respective receiver nodes through half-duplex MIMO amplify and forward (AF) relay nodes. For this system configuration, they address the problem of linear transceiver-relay design to achieve max-min fairness among all the users' data streams. Assuming perfect channel state information, this problem is formulated as the maximisation of the minimum signal-to-interference-plus-noise ratio (SINR) per stream among all the users subject to transmit power constraints at the transmitter and relay nodes. Since the formulated problem of jointly optimising all the transmitters, relays and receivers is non-convex, a globally optimal solution cannot be easily obtained. Therefore, they propose a quasi-optimal iterative algorithm to jointly optimise the transmitter precoders, relay precoding matrices and receiver filters for all the users and relays such that the minimum per stream received SINR is improved at each iteration. Simulation results show that the proposed algorithm improves fairness among data-streams of all users and outperforms existing strategies in terms of minimum user rate and bit error rate.

关键词： radio transceivers MIMO communication multiuser channels relay networks (telecommunication) radiofrequency interference amplify and forward communication wireless channels concave programming iterative methods precoding matrix algebra filtering theory max-min fairness based linear transceiver-relay design MIMO interference relay channel multiuser multiinput multioutput interference relay system transmitter nodes receiver nodes half-duplex MIMO amplify-and-forward relay nodes half-duplex MIMO AF relay nodes perfect channel state information minimum signal-to-interference-plus-noise-per-stream maximisation SINR transmit power constraints relay nodes nonconvex problem quasioptimal iterative algorithm relay precoding matrices transmitter precoders receiver filters

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A set-theoretic model predictive control approach for transient stability in smart grid

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IET CONTROL THEORY AND APPLICATIONS 2020年第5期14卷 700-707页

作者： Bagherzadeh, Maryam Lucia, Walter Concordia Univ Elect & Comp ECE Dept Montreal PQ Canada Concordia Univ CIISE Montreal PQ Canada

In this study, the authors deal with the transient stability control problem in smart grids. They consider an operative scenario where a physical fault or a cyber-attack produces an impulsive perturbation in the state of the system, and a controller must be designed to robustly recover, in a finite-time, transient stability despite initial perturbation and uncertainties. The authors propose a solution that is based on a low-demanding model predictive control (MPC) idea that is known as set-theoretic MPC. They show that such a controller can be used as an emergency controller to deal with the considered scenario. A peculiar capability of the proposed solution is that the worst-case time to transient stability can be apriori established. Moreover, most of the required computations are moved into an off-line phase leaving into the on-line phase a simple and computationally affordable convex optimisation problem. Finally, the authors have conducted an extensive simulation campaign to testify the validity of the proposed solution experimentally and to investigate its performance when contrasted with a competitor scheme.

关键词： optimisation power system transient stability power distribution reliability concave programming convex programming nonlinear programming power generation dispatch set theory predictive control power system control smart power grids set-theoretic model predictive control approach smart grid transient stability control problem operative scenario impulsive perturbation initial perturbation uncertainties model predictive control idea set-theoretic MPC emergency controller considered scenario worst-case time simple convex optimisation problem computationally affordable convex optimisation problem

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