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Robust resource allocation for IRS-assisted secure cognitive radio networks

ELECTRONICS LETTERS 2022年第21期58卷 816-818页

作者： Tang, Runzhe Wu, Wei Wang, Yibo Jiang, Huaixuan Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing Peoples R China Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing Peoples R China

In this paper, a robust resource allocation problem in an intelligent reflecting surface (IRS) assisted artificial noise (AN)-aided cognitive radio network is investigated. Considering that the channel state information (CSI) of the eavesdropper channel and interference channel cannot be perfectly known in practice, the aim is to minimise the power consumption of the secondary user transmitter (SU-TX) by jointly optimising the transmit beamforming and covariance matrix of AN at the SU-TX as well as the phase shifts of the IRS. The formulated problem is non-convex and challenging to tackle due to the coupling among multiple variables. An efficient alternative optimisation algorithm to solve it by exploiting the S-procedure and semi-definite relaxation. Simulation results demonstrate that the IRS helps in reducing the power consumption significantly even under imperfect CSI conditions.

关键词： covariance matrix nonconvex problem eavesdropper channel intelligent reflecting surface assisted artificial noise-aided cognitive radio network SU-TX power consumption wireless channels channel state information interference channel semidefinite programming resource allocation intelligent materials concave programming radio transmitters telecommunication power management semidefinite relaxation array signal processing alternative optimisation algorithm secondary user transmitter telecommunication security covariance matrices S-procedure IRS-assisted secure cognitive radio networks radiofrequency interference transmit beamforming IRS assisted artificial noise-aided cognitive radio network cognitive radio resource allocation problem

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Joint offloading and beamforming design in full-duplex multi-hop SWIPT MEC systems under non-linear energy harvesting model

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IET COMMUNICATIONS 2022年第18期16卷 2171-2182页

作者： Xu, Zhiguo Lv, You Liu, Jianxin Zou, Junwei Wen, Zhigang Beijing Univ Posts & Telecommun Dept Elect Engn Beijing Key Lab Work Safety Intelligent Monitorin Beijing Peoples R China

In Internet of Things scenarios, the problem of restricted energy and limited computation capability of user node usually exists. To address the problem, a multiple-input-multiple-output full-duplex multi-hop relay simultaneous wireless information and power transfer (SWIPT) mobile-edge computing (MEC) system is proposed in this paper, where energy harvesting model is non-linear. With the aid of MEC and SWIPT, user node equipped with power splitting receiver can locally execute computation tasks or offload partial or all of it to access point associated with an MEC server by using its battery energy, and harvest energy to replenish its battery while downloading the computation results. In addition, the system introduces full-duplex multi-hop relay and non-linear energy model to improve network coverage and model practical energy harvesting circuit. Aiming at minimising the system energy consumption, an energy efficient optimisation problem is formulated while satisfying the latency and energy constraints. Since the original problem is non-linear and non-convex, it is converted into two subproblems and successive convex approximation-based algorithm and geometric programming-based algorithm based alternating optimisation technique are adopted to solve them. Numerical results verify the superiority of the proposed optimisation scheme compared with other benchmark schemes.

关键词： Internet of Things restricted energy beamforming design energy efficient optimisation problem battery energy computation results Things scenarios radiofrequency power transmission mobile-edge computing system power splitting receiver energy harvesting MIMO communication convex programming relay networks (telecommunication) resource allocation energy constraints edge computing concave programming energy consumption optimisation telecommunication power management nonlinear energy model nonconvex array signal processing limited computation capability network coverage system energy consumption computation tasks nonorthogonal multiple access mobile computing energy conservation joint offloading nonlinear energy harvesting model user node geometric programming full-duplex multihop relay power transfer MEC server model practical energy harvesting circuit full-duplex multihop SWIPT MEC systems

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Steady state and transient simulation for electricity-gas integrated energy systems by using convex optimisation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第9期12卷 2199-2206页

作者： Chen, Sheng Wei, Zhinong Sun, Guoqiang Wang, Dan Zang, Haixiang Hohai Univ Coll Energy & Elect Engn Nanjing 210098 Jiangsu Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China

The increasing interdependence between electric power and natural-gas systems requires integrated simulation tools applicable to the coupled system. Therefore, this study proposes steady-state and transient simulation tools for electricity-gas integrated energy systems (IESs) by using convex optimisation. Steady-state IES simulation is designed for long-term planning studies while transient IES simulation, which includes line-pack storage, is designed for real-time operational research. The proposed convex steady-state gas flow model introduces binary variables to represent gas flow directions, and employs the second-order cone (SOC) to relax non-convex pipeline gas flow equations. As a result, steady-state gas flow analysis is formulated as a mixed integer SOC programming problem. The proposed transient gas flow model employs the implicit finite difference method to transform partial differential equations into algebraic difference equations. Moreover, the short-term gas flow directions are specified and SOC relaxations are used. Simulation results on an integrated IEEE 24-node system and Belgian 20-node gas system verify the effectiveness of the proposed SOC models. The impacts of wind power forecast errors on multi-period gas network operations are also investigated.

关键词： natural gas technology power system transients power system simulation convex programming concave programming integer programming partial differential equations power generation planning differential algebraic equations thermal power stations transient simulation tools electricity-gas integrated energy systems electric power natural-gas systems integrated simulation tools coupled system convex optimisation steady-state IES simulation long-term planning studies line-pack storage convex steady-state gas flow model binary variables gas flow directions second-order cone nonconvex pipeline gas flow equations steady-state gas flow analysis mixed integer SOC programming problem transient gas flow model implicit finite difference method partial differential equations algebraic difference equations short-term gas flow directions SOC relaxations integrated IEEE 24-node system Belgian 20-node gas system multiperiod gas network operations wind power forecast errors real-time operational research

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Performance analysis and SINR-based power allocation strategies for downlink NOMA networks

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IET COMMUNICATIONS 2020年第5期14卷 723-735页

作者： Baidas, Mohammed W. Alsusa, Emad Hamdi, Khairi A. Kuwait Univ Coll Engn & Petr Dept Elect Engn Kuwait Kuwait Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

This study analyses the performance of downlink non-orthogonal multiple-access (NOMA) networks over independent but not necessarily identically distributed Rayleigh fading channels by deriving closed-form expressions for the average signal-to-interference-plus-noise ratio (SINR), average achievable rate, and outage probability of all network users. Moreover, SINR-based power allocation strategies are studied. Specifically, the optimisation problems of max-min SINR, multi-objective SINR, sum-SINR and proportional-fairness-SINR maximisation under minimum SINR constraints are formulated and shown to be non-convex. Additionally, the problems of total power and sum-SINR minimisation are also formulated. Particularly, the formulated problems take the form of linear-fractional programming problems. By applying intelligent reformulation techniques, these problems have been reformulated into convex optimisation problems, and thus efficiently solved. Numerical evaluations are presented to validate the derived closed-form expressions for the different performance metrics, which are found to be in agreement with the network simulation results. More importantly, it has been demonstrated that the user with the best channel conditions achieves full diversity order, whereas all the other users achieve diversity orders equivalent to their ordered channel conditions. Finally, the reformulated power allocation strategies are evaluated, and shown to coincide with the original problem formulations.

关键词： probability Rayleigh channels radiofrequency interference convex programming multi-access systems concave programming minimisation linear programming numerical analysis SINR-based power allocation strategies proportional-fairness-SINR maximisation minimum SINR constraints sum-SINR minimisation linear-fractional programming problems convex optimisation problems closed-form expressions reformulated power allocation strategies downlink NOMA networks downlink nonorthogonal multiple-access networks identically distributed Rayleigh fading channels signal-to-interference-plus-noise ratio outage probability intelligent reformulation techniques numerical evaluations

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Joint relay selection and power allocation for NOMA-based multicast cognitive radio networks

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IET COMMUNICATIONS 2020年第13期14卷 2027-2037页

作者： Baidas, Mohammed W. Alsusa, Emad Hamdi, Khairi A. Kuwait Univ Coll Engn & Petr Dept Elect Engn Kuwait Kuwait Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

In this study, the problem of joint relay selection and power allocation (J-RS-PA) for NOMA-based multicast cognitive radio networks is considered. In particular, the aim is to simultaneously maximise the end-to-end SINR/SNR of the primary and secondary transmitter-receiver (TR) pairs, subject to quality-of-service (QoS) constraints. Communication between the primary and secondary TR pairs is performed over two-phases, namely, the broadcasting phase, and the cooperation phase. In the broadcasting phase, the primary and secondary transmitters broadcast their data symbols;while in the cooperation phase, the selected relay forwards the decoded symbols to their intended receivers. However, the formulated J-RS-PA problem happens to be non-convex, resulting in computationally-prohibitive complexity. Consequently, an optimal low-complexity two-stage relay selection and power allocation (TS-RS-PA) algorithm is devised, which is based on the solution of linear programming problem reformulations. Simulation results are presented to validate the proposed TS-RS-PA algorithm, which is shown to yield the optimal SINR/SNR values in comparison to the J-RS-PA scheme, but with lower computational complexity, while satisfying QoS constraints.

关键词： multicast communication linear programming quality of service cognitive radio computational complexity radio receivers radio transmitters concave programming cooperative communication multi-access systems relay networks (telecommunication) telecommunication power management joint relay selection and power allocation NOMA-based multicast cognitive radio networks quality-of-service constraints primary TR pairs secondary TR pairs broadcasting phase cooperation phase primary transmitters secondary transmitters J-RS-PA problem optimal low-complexity two-stage relay selection and power allocation algorithm TS-RS-PA algorithm end-to-end SINR maximisation end-to-end SNR maximisation QoS constraints nonconvex problem computationally-prohibitive complexity linear programming problem transmitter-receiver pairs

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Fast learning optimiser for real-time optimal energy management of a grid-connected microgrid

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第12期12卷 2977-2987页

作者： Tan, Zhukui Zhang, Xiaoshun Xie, Baiming Wang, Dezhi Liu, Bin Yu, Tao Guizhou Power Grid Co Ltd Elect Power Res Inst Guiyang Guizhou Peoples R China South China Univ Technol Sch Elect Power Guangzhou Guangdong Peoples R China

This study proposes a novel fast learning optimiser (FLO) for real-time optimal energy management (OEM) of a grid-connected microgrid. To reduce the optimisation difficulty, the non-convex real-time OEM is decomposed into a two-layer optimisation. The non-convex top-layer optimisation is responsible to determine the direction of tie-line power, and the heat energy outputs of combined heat and power units. Then bottom-layer optimisation is strictly convex with the rest controllable variables, which is solved by the classical interior point method. The model-free Q-learning is employed for knowledge learning and decision making in the top-layer optimisation, thus the feedback reward from the bottom-layer optimisation can effectively realise a coordination between them. The real-coded associative memory is presented for a more efficient optimisation of continuous controllable variables. In order to dramatically reduce the execution time, the knowledge transfer is adopted for approximating the optimal knowledge matrices of a real-time new task by abstracting the optimal knowledge matrices of the predictive source tasks. Simulation results demonstrates that the proposed FLO can rapidly search a high-quality optimum of real-time OEM, in which the computation rate is about 2.75-29.23 times faster than that of eight classical heuristic algorithms.

关键词： energy management systems distributed power generation power grids concave programming learning (artificial intelligence) cogeneration convex programming decision making content-addressable storage matrix algebra power engineering computing fast learning optimiser real-time optimal energy management grid-connected microgrid FLO nonconvex real-time OEM two-layer optimisation nonconvex top-layer optimisation tie-line power heat energy outputs combined heat-power units bottom-layer optimisation classical interior point method model-free Q-learning knowledge learning decision making feedback reward real-coded associative memory continuous controllable variable optimisation optimal knowledge matrices predictive source tasks classical heuristic algorithms convex optimization

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A new penalty parameter for linearly constrained 0-1 quadratic programming problems

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OPTIMIZATION LETTERS 2013年第4期7卷 765-778页

作者： Hsia, Yong Wang, Yanping Beihang Univ Sch Math & Syst Sci State Key Lab Software Dev Environm LMIBMinist Educ Beijing 100191 Peoples R China Beihang Univ Sch Math & Syst Sci Beijing 100191 Peoples R China

The equivalence between the linearly constrained 0-1 quadratic programming problem and the continuous quadratic programming problem is studied in this note. Specifically, we show that the existing penalty parameter fr... 详细信息

关键词： Linearly constrained 0-1 quadratic programming Integer programming concave programming Penalty parameter

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Waveform design for multiple extended targets in temporally correlated cognitive radar system

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IET RADAR SONAR AND NAVIGATION 2016年第2期10卷 398-410页

作者： Chen, Peng Wu, Lenan Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Jiangsu Peoples R China Columbia Univ Dept Elect Engn New York NY 10027 USA

In cognitive radar systems, a closed-loop feedback is formed by the transmitter, environment, and receiver. Then, the transmit waveform can be optimised to improve the radar estimation performance. Unlike existing works that only consider single target with temporally correlated characteristic, waveform design for multiple extended targets is investigated in this study. The authors propose a Kalman filtering (KF)-based method to exploit the temporal correlation of target scattering coefficients (TSCs) and improve the corresponding estimation performance. Additionally, for the targets of both closed and separated in range, a novel optimisation problem is established to design the transmit waveform and minimise the mean square error of estimated TSC at each KF iteration, where an additional weight vector is introduced to achieve a trade-off among different targets. Since the optimisation problem is non-convex and cannot be solved efficiently, a two-step method is proposed to convert it into a convex problem, which can be solved by an optimisation toolbox such as CVX. Simulation results demonstrate that the joint method of waveform design outperforms the existing methods in TSC estimation for both separated and closed targets, and offers much more flexibility.

关键词： correlation theory waveform analysis radar receivers radar transmitters Kalman filters electromagnetic wave scattering mean square error methods minimisation concave programming iterative methods feedback multiple extended target temporally correlated cognitive radar system waveform design closed loop feedback transmitter receiver waveform transmission radar estimation performance Kalman filtering target scattering coefficients TSC estimation optimisation problem mean square error minimisation KF iteration weight vector CVX nonconvex programming

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Non-convex nonlocal adaptive tight frame image deblurring

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IET IMAGE PROCESSING 2022年第7期16卷 1908-1923页

作者： Shen, Zhengwei Univ Sci & Technol Sch Math & Phys Beijing Peoples R China

The challenge of the image restoration is to recover more detailed information from the degraded images. Based on the observations that wavelet frames have efficient representation ability to image details and the nonconvex regularization in the model may admit unbiased solutions, in this paper, in order to recover more details, a wavelet frames nonconvex l(p)(0 < p < 1) regularization image restoration model is established that combing with a nonlocal adaptive mean doubly augmented Lagrangian (MDAL) method and context model. Specifically, a nonlocal adaptive MDAL method is proposed to solve the established nonconvex model. Furthermore, in order to mitigate the trade-off between the regularization error and the noise magnification error, spatially adaptive wavelet thresholding methods based on the context model and nonlocal mean filter are introduced in the algorithm. The convergence analysis of this nonlocal adaptive nonconvex algorithm is also obtained using the KL inequality. Numerical experiments demonstrate that the proposed method has a competent deblurring and denoising ability, also is efficient and is comparable to state-of-the-art methods.

关键词： image restoration nonlocal adaptive nonconvex algorithm Lagrangian method context model nonconvex regularization spatially adaptive wavelet thresholding methods wavelet transforms nonlocal adaptive MDAL method image representation representation ability established nonconvex model image filtering concave programming denoising ability degraded images nonconvex nonlocal adaptive tight frame image deblurring wavelet frame nonconvex regularization image restoration model KL inequality nonlocal adaptive mean doubly augmented Lagrangian method image denoising nonlocal mean filter

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Design of phase codes with interval staggering for Doppler ambiguous target detection

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ELECTRONICS LETTERS 2013年第24期49卷 1564-+页

作者： Wei, Na Zhang, Linke BeiHang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Wuhan Univ Technol Sch Energy & Power Engn Wuhan 430063 Peoples R China

The design of radar coherent pulse trains encoded with interval staggering for ambiguous Doppler target detection in clutter is addressed. To alleviate the Doppler ambiguous problem, an interpulse phase coding optimisation model is formulated in terms of staggered interval sampling, while maximising the output signal-to-interference-plus-noise ratio for improved target detection. Based on a reformulation of this optimisation problem as a non-convex quadratically constrained quadratic program (QCQP), a semidefinite relaxation algorithm for this QCQP is presented. Numerical results demonstrate the effectiveness of this proposed approach.

关键词： concave programming Doppler effect electromagnetic interference object detection phase coding quadratic programming radar clutter radar detection signal sampling phase code design Doppler ambiguous target detection radar coherent pulse train encoding radar clutter interpulse phase coding optimisation model staggered interval sampling signal-to-interference-plus-noise ratio nonconvex quadratically constrained quadratic program QCQP semidefinite relaxation algorithm

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