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Robust control via concave minimization local and global algorithms

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2000年第2期45卷 299-305页

作者： Apkarian, P Tuan, HD Off Natl Etud & Rech Aerosp CERT Control Syst Dept F-31055 Toulouse France Toyota Technol Inst Dept Control & Informat Nagaoka Niigata 4688511 Japan

This paper is concerned with the robust control problem of linear fractional representation (LFT) uncertain systems depending on a time-varying parameter uncertainty. Our main result exploits a linear matrix inequality (LMI) characterization involving scalings and Lyapunov variables subject to an additional essentially nonconvex algebraic constraint. The nonconvexity enters the problem in the form of a rank deficiency condition or matrix inverse relation on the scalings only. It is shown that such problems, but also more generally rank inequalities and bilinear constraints, can be formulated as the minimization of a concave functional subject to LMI constraints. First of all, a local Frank and Wolfe (FW) feasible direction algorithm is introduced in this context to tackle this hard optimization problem. Exploiting the attractive concavity structure of the problem, several efficient global concave programming methods are then introduced and combined with the local feasible direction method to secure and certify global optimality of the solutions. Computational experiments indicate the viability of our algorithms, and in the worst case, they require the solution of a few LMI programs.

关键词： concave programming linear matrix inequalities (LMI's) robust control time-varying uncertainties

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Adaptive detection of a signal with angle uncertainty

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IET RADAR SONAR AND NAVIGATION 2010年第4期4卷 537-547页

作者： De Maio, A. De Nicola, S. Farina, A. Iommelli, S. Univ Naples Federico 2 Dipartimento Ingn Biomed Elettron & Telecomunicaz I-80125 Naples Italy SELEX Sistemi Integrati I-00131 Rome Italy

This study deals with adaptive detection of a signal with unknown complex amplitude and whose steering vector presents angle uncertainties. The background environment is assumed homogeneous and Gaussian with unknown covariance matrix. At the design stage, the authors devise a class of robust receivers accounting for the aforementioned uncertainty. To this end, the authors prove that the maximisation of the concentrated likelihood function over the phase parameter shares a hidden convexity property. Specifically, exploiting some recent results concerning trigonometric polynomials, the authors formulate the apparently non-convex maximisation over the phase as a semidefinite programming (SDP) convex optimisation problem. Then, the authors show that the receivers of the class (but for one) exhibit a bounded-constant false alarm rate (bounded-CFAR) behaviour and derive lower and upper bounds for both the false alarm and the detection performance. At the analysis stage, the authors assess the performance of the new receivers in comparison with some classic detectors available in open literature.

关键词： convex programming nonconvex maximisation concentrated likelihood function Optimisation techniques semidefinite programming convex optimisation problem Digital signal processing adaptive signal detection maximisation Algebra bounded-constant false alarm rate behaviour hidden convexity property Signal detection covariance matrix covariance matrices angle uncertainty concave programming trigonometric polynomials robust receivers signal adaptive detection phase parameter

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Equivalent convex optimisation approach for green cognitive radio-based cooperative networks

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IET COMMUNICATIONS 2018年第11期12卷 1290-1296页

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

Ensuring total rate maximisation whilst keeping Greenhouse gas emission (GHGE) at a minimum in green cooperative cognitive radio networks is key to achieving maximum network quality. This study employs the multi-objective lexicographic algorithm to a proposed multi-objective optimisation problem scheme to deal with these conflicting objectives through two consecutive steps. The challenge posed by maximum throughput - a complex non-convex optimisation problem - is first resolved through a mathematical manipulation resulting in a fractional programming model which is then converted to its simplified convex programming equivalent by applying the Charnes-Cooper transformation. Next, the GHGE minimisation issue is tackled effectively by applying a convex programming. Simulation results confirm that the proposed approach offers superior performance compared to well-known schemes in terms of throughput gain and emission quality.

关键词： convex programming concave programming cognitive radio cooperative communication equivalent convex optimisation approach green cognitive radio-based cooperative networks total rate maximisation multi objective lexicographic algorithm multi objective optimisation problem scheme complex nonconvex optimisation problem fractional programming model simplified convex programming Charnes-Cooper transformation GHGE minimisation

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Robust beamforming and power allocation in cognitive radio relay networks with imperfect channel state information

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IET COMMUNICATIONS 2014年第9期8卷 1560-1569页

作者： Mohammadkhani, Saeideh Kahaei, Mohammad Hossein Razavizadeh, Seyed Mohammad Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

The authors study robust beamforming and power allocation in cognitive relay networks using several relays in the secondary link. The channel state information is imperfect modelled by Gaussian random variables. First, minimisation of the total transmit power of relays and the secondary transmitter is considered with a constraint on the interference in the primary receiver. Also, a constraint is assumed on the received signal-to-interference plus noise ratio (SINR) in the secondary receiver. In the other scenario, maximisation of the SINR in the secondary receiver is investigated with a limit on the maximum transmit power of relays and the secondary transmitter. For each non-convex optimisation problem, an iterative and robust algorithm is derived. It is proved that the first proposed algorithm converges to the global optimum. Simulation results similarly show the convergence of both algorithms with a few number of iterations. The outperformance of the robust algorithms is shown compared with a non-robust design.

关键词： cognitive radio relay networks (telecommunication) array signal processing channel allocation radio links Gaussian processes random processes radio receivers radio transmitters interference suppression minimax techniques concave programming iterative methods convergence robust beamforming algorithm power allocation cognitive radio relay network imperfect channel state information radio link convergence iterative algorithm nonconvex optimisation problem secondary transmitter SINR maximisation secondary receiver SINR signal-to-interference plus noise ratio primary receiver transmit power minimisation Gaussian random variables

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Energy-efficient optimisation for UAV-aided wireless sensor networks

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IET COMMUNICATIONS 2019年第8期13卷 972-980页

作者： Hua, Meng Wang, Yi Zhang, Zhengming Li, Chunguo Huang, Yongming Yang, Luxi Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Jiangsu Peoples R China Zhengzhou Univ Aeronaut Sch Elect & Commun Engn Zhengzhou 450046 Henan Peoples R China Natl Digital Switching Syst Engn & Technol Res Ct Zhengzhou 450002 Henan Peoples R China

This study investigates a novel unmanned aerial vehicle (UAV)-based wireless sensor network, where the UAV acts as a flying base station to serve multiple wireless sensor nodes (SNs). The authors goal is to maximise the system energy efficiency of the UAV while satisfying the fairness among SNs by jointly optimising the UAV trajectory and UAV time allocation. The formulated problem is shown to be a non-convex fractional optimisation problem, which is hard to tackle. To this end, they decompose the original problem into two sub-problems, and the block coordinate descent method and successive convex optimisation technique are employed to solve these two sub-problems iteratively. Specifically, in the first sub-problem, the optimal UAV time allocation is obtained by maximising the minimum achievable rate of SNs with given UAV trajectory constraints. In the second sub-problem, the UAV trajectory is achieved by minimising the energy consumption of the UAV with the given UAV time allocation. Subsequently, an iterative algorithm is proposed to optimise the time allocation and UAV trajectory alternately. Furthermore, the convergence and complexity of their proposed algorithm are provided. Numerical results show that the proposed scheme outperforms the existing benchmark strategies in terms of energy efficiency.

关键词： remotely operated vehicles convex programming concave programming autonomous aerial vehicles wireless sensor networks iterative methods optimisation SNs formulated problem nonconvex fractional optimisation problem original problem successive convex optimisation technique optimal UAV time allocation given UAV trajectory constraints given UAV time allocation energy-efficient optimisation UAV-aided wireless sensor networks novel unmanned aerial vehicle-based wireless sensor network flying base station sensor nodes system energy efficiency

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Simultaneous wireless information and power transfer for massive MIMO networks

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IET COMMUNICATIONS 2020年第5期14卷 880-887页

作者： Sun, Wenfeng Liu, Chen Qian, Mujun Xu, Shu Nanjing Univ Posts & Telecommun Sch Elect & Opt Engn Nanjing 210003 Peoples R China Qingdao Agr Univ Sch Sci & Informat Qingdao 266109 Peoples R China

In this study, the authors propose a simultaneous wireless information and power transfer scheme based on the power-splitting (PS) protocol for a massive multiple-input multiple-output (MIMO) network. On the downlink, the base station transmits the information and energy simultaneously to each terminal and each terminal divides the received signals by its PS device between information decoder and energy harvester (EH). On the uplink, a portion of the energy harvested by EH is drawn for uplink pilot transmission in the coming frame and then the other is used for uplink data transmission in the current frame. Based on this scheme, the closed-form lower bound expressions are obtained for the achievable downlink rate and uplink rate of each terminal and then are utilised to formulate an optimisation problem that is to maximise achievable sum rate of the whole network while ensuring the minimum required uplink and downlink rates of each terminal. However, as the original optimisation problem is non-convex and non-linear and very hard to solve directly, an algorithm is proposed by utilising successive approximation method based on geometric program. Numerical results verify the correctness and feasibility of the proposed algorithm.

关键词： MIMO communication energy harvesting telecommunication power management protocols radiofrequency power transmission concave programming nonlinear programming geometric programming approximation theory power-splitting protocol massive multiple-input multiple-output network base station transmits PS device information decoder uplink pilot transmission uplink data transmission closed-form lower bound expressions achievable downlink rate uplink rate achievable sum rate minimum required uplink downlink rates massive MIMO networks power transfer scheme

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Transmit waveform and receive filter design for multiple-input multiple-output radar with one-bit digital-to-analogue converters

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IET SIGNAL PROCESSING 2022年第7期16卷 873-881页

作者： Wan, Huan Quan, Zhi Liao, Bin Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Polytech Sch Artificial Intelligence Shenzhen Peoples R China

In this paper, we investigate the joint design of transmit waveform and receive filter for colocated Multiple-Input Multiple-Output radar equipped with one-bit digital-to-analogue converters (DACs). The problem is formulated as maximising the output signal-to-interference-plus-noise ratio in the presence of signal-dependent interferences, subject to a discrete constraint imposed by the waveform quantised with one-bit DACs. To cope with the challenging non-convex problem, an alternating maximisation framework is developed to optimise the transmit waveform and receive filter vectors in an iterative manner. More specifically, for a given transmit waveform vector, the analytical expression of the receive filter vector is first derived. Then, by fixing the receive filter, a biconvex relaxation method is employed to tackle the non-convex problem with respect to the transmit waveform vector. The performance of the proposed approach is demonstrated by numerical simulations in different circumstances.

关键词： filter design filter vector receive filter design MIMO radar colocated multiple-input multiple-output radar convex programming iterative methods transmit waveform vector output signal-to-interference-plus-noise ratio concave programming iterative manner numerical simulations discrete constraint digital-analogue conversion one-bit digital-to-analogue converters alternating maximisation framework one-bit DAC biconvex relaxation method signal-dependent interferences nonconvex problem

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Stochastic optimal dispatch of PV/wind/diesel/battery microgrids using state-space approximate dynamic programming

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第15期13卷 3409-3420页

作者： Lin, Shunjiang Wang, Yaping Liu, Mingbo Fan, Guansheng Yang, Zhibin Li, Qifeng South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Guangdong Peoples R China Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

In the operation of microgrids (MGs), the stochastic production of solar/wind, the discrete variables of photovoltaic (PV) inverter's auxiliary service state and diesel generators' (DGs') off-on state generally need to be considered, and a stochastic mixed-integer non-linear non-convex programming (MINNP) model is established for the optimal dispatch of MGs. In this model, the expected value of the sum of DGs' operation as well as start-up cost, the network-loss cost and the PV inverter's auxiliary service cost, is considered as the objective function. The stochastic MINNP model is transformed into a stochastic mixed-integer second-order cone programming (MISOCP) model to reduce the computational complexity. The state-space approximate dynamic programming algorithm is adopted to solve the stochastic MISOCP model. In the algorithm, based on the approximate value functions of typical states that are computed according to the Markov decision process, solving the optimisation model of multiple periods is executed by solving each period's optimisation model one by one to improve the computational efficiency. Meanwhile, parallel computing is executed to greatly improve the efficiency of the proposed algorithm. Test results on two modified IEEE-33 bus and IEEE-123 bus islanded MGs with PV/wind/diesel/battery demonstrate the correctness and efficiency of the proposed model and algorithm.

关键词： convex programming photovoltaic power systems integer programming wind power plants Markov processes distributed power generation hybrid power systems power generation dispatch concave programming nonlinear programming diesel-electric power stations battery storage plants stochastic optimal dispatch PV wind diesel battery microgrids photovoltaic inverter diesel generators mixed-integer nonlinear nonconvex programming PV inverter stochastic MINNP model state-space approximate dynamic programming algorithm stochastic MISOCP model stochastic mixed-integer second-order cone programming model Markov decision process

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Grating lobe suppression in focussed energy delivery for precision electronic warfare

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IET RADAR SONAR AND NAVIGATION 2021年第11期15卷 1420-1432页

作者： Yang, Zhong-ping Zhou, Qing-song Li, Zhi-hui Zhang, Jian-yun Yang, Shu-ning Natl Univ Def Technol Coll Elect Engn Hefei Peoples R China

Focussed energy delivery (FED), which transmits energy to a specific area by ultrasparse arrays, is the core technique of precision electronic warfare (PREW). However, array sparsity results in severe grating lobes in practice, which greatly increase the risks of an interference system being attacked and unexpected jamming occurring in other devices. Here, two methods are presented which are aimed at suppressing grating lobes when implementing FED in the application of PREW to avoid the problems mentioned. Regarding the first method, a new metric based on the l infinity-norm is proposed to measure the maximum grating lobe and introduce the metric to the standard FED model. A multiobjective optimisation problem (MOP) is established and solved by the alternating direction penalty method (ADPM) framework. In the second method, the degrees of freedom of the transmitted signals are extended by equipping each platform with a group of linear uniform antennas. The corresponding MOP is established and block coordinate descent is employed to simplify the problem into two subproblems concerning the amplitude and phase components of the transmitted signals. Then, these two subproblems are solved using Taylor expansion and the ADPM framework. Simulation results are provided to demonstrate the advantages of the proposed methods over the state-of-the-art methods.

关键词： radar imaging jamming antenna arrays optimisation electronic warfare concave programming array signal processing antenna radiation patterns linear antenna arrays

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Channel selection and power allocation for device-to-device enabled cellular networks

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IET COMMUNICATIONS 2018年第15期12卷 1854-1863页

作者： Gour, Radhika Tyagi, Anshul Indian Inst Technol Roorkee Elect & Commun Engn Roorkee 247667 Uttarakhand India

Cellular networks with device-to-device (D2D) communication capabilities offer increased spectrum utilisation and system throughput. However, these performance gains depend on the channel and power allocation strategy used. In this study, the authors have chosen underlay D2D as it is more advantageous in terms of spectrum efficiency, but introduces additional interferences. To cope up with these challenges, they propose new channel assignment and power allocation schemes with and without Quality of Service (QoS) constraint for D2D pairs. The authors' schemes allow multiple channels to be shared by each D2D pair without degrading the performance of the existing cellular system. First, they formulate the joint channel and power allocation problem without considering the QoS for D2D pairs which results in a non-convex mixed-integer non-linear programming problem and cannot be solved in polynomial time. Then, the problem of channel and power allocation is reformulated and optimised, individually. Following this, they introduce QoS constraints for each D2D pair by specifying the minimum number of channels that should be allocated to each D2D pair during the assignment. The effectiveness of their scheme is verified through numerical results.

关键词： cellular radio wireless channels channel allocation radio spectrum management quality of service concave programming integer programming nonlinear programming telecommunication power management radiofrequency interference channel selection power allocation strategy device-to-device enabled cellular networks device-to-device communication capabilities D2D communication capabilities spectrum utilisation system throughput underlay D2D spectrum efficiency channel assignment scheme quality-of-service constraint QoS constraint D2D pairs joint channel-and-power allocation problem nonconvex mixed-integer nonlinear programming problem

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