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Clutter suppression based on iterative reweighted methods with multiple measurement vectors for airborne radar

IET RADAR SONAR AND NAVIGATION 2022年第9期16卷 1446-1459页

作者： Liu, Cheng Wang, Tong Zhang, Shuguang Ren, Bing Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

Sparse recovery algorithms have been applied to the Space-time adaptive processing for reducing the requirement of samples over the past 15 years. However, many Sparse recovery algorithms are not robust and need accurate user parameters. Conventional sparse Bayesian learning (SBL) algorithms are insensitivity to user parameters but converge slowly. To remedy the limitation, two iterative reweighted algorithms are proposed based on SBL. In order to minimise the SBL penalty function, we construct its upper-bounding surrogate function via the concave conjugate function and apply iterative reweighted algorithms to minimise the surrogate function. Theoretical analysis and numerical experiments all exhibit great performance of the proposed algorithms.

关键词： iterative reweighted methods multiple measurement vectors Bayes methods iterative methods airborne radar conventional sparse Bayesian learning algorithms Space-time adaptive processing SBL penalty function concave programming Sparse recovery algorithms clutter suppression concave conjugate function iterative reweighted algorithms space-time adaptive processing upper-bounding surrogate function radar clutter

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Resource allocation for IRS-assisted MC MISO-NOMA system

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IET COMMUNICATIONS 2022年第13期16卷 1617-1627页

作者： Javadi, Sepideh Shafiei, Hossein Forouzanmehr, Malihe Khalili, Ata Nguyen, Ha H. Inst Res Fundamental Sci Tehran Iran KN Toosi Univ Technol Dept Comp Engn Tehran Iran Univ Tehran ECE Dept Tehran Iran Friedrich Alexander Univ Erlangen Nurnberg Digital Commun Erlangen Germany Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK Canada

In this paper, a downlink multi-user communication of an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) power-domain non-orthogonal multiple access (NOMA) system is investigated. Considering multi-carrier (MC) transmission and to enhance user fairness, two users are assigned to the same subcarrier. For such a system, the authors optimize active beamforming at the base station (BS), subcarrier allocation policy, and phase shifts at the IRS to maximize the system throughput. A semi-definite relaxation (SDR) is applied to tackle the non-convex optimization problem, and an alternating optimization (AO) algorithm is proposed to obtain a suboptimal solution. Numerical results illustrate the higher throughput of the proposed MC multi-user IRS-aided MISO-NOMA system as compared to the conventional IRS-assisted orthogonal multiple access (OMA) system.

关键词： intelligent reflecting surface-assisted multiple-input MC multiuser IRS-aided MISO-NOMA system subcarrier allocation policy IRS-assisted MC MISO-NOMA system resource allocation conventional IRS-assisted orthogonal multiple access system concave programming MISO communication OFDM modulation telecommunication power management interference suppression nonorthogonal multiple access considering multicarrier transmission array signal processing single-output power-domain nonorthogonal multiple access system wireless channels system throughput downlink multiuser communication

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Robust joint design of transmit waveform and receive filter for MIMO radar space-time adaptive processing with signal-dependent interferences

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IET RADAR SONAR AND NAVIGATION 2017年第8期11卷 1321-1332页

作者： Wang, Yuxi Li, Wei Sun, Qilu Huang, Guoce Air Force Engn Univ Informat & Nav Coll Xian 710077 Shaanxi Peoples R China

This study deals with the problem of joint design of multiple-input-multiple-output (MIMO) radar transmit waveform and receive filter in the presence of signal-dependent interferences. To enhance the detection performance of a slow moving target, MIMO radar space-time adaptive technology is adapted. Considering the issue of imprecise knowledge about target's Doppler frequency and direction, the worst-case output signal-to-interference-plus-noise ratio over the uncertainty set of target steering vector is set as figure of merit. Furthermore, due to the non-linear power amplifier and the fact that transmit antenna may be out of work, constraints of peak-to-average power ratio and transmit power for each transmit antenna are taken into account. As to the resulted hard problem, some suitable mathematical transformations are performed and a novel sequential optimisation procedure combined with semi-definite relaxation, Charnes-Cooper transform and Lagrange duality method is developed to tackle the non-convex joint design problem. Randomisation procedure is utilised to synthesise the final transmit waveform and receive filter. The computational complexity is linear with the numbers of iterations and trials of the randomisation. The convergence of the devised algorithm is analysed and numerical results in a variety of scenarios are provided to demonstrate the effectiveness of the proposed algorithm.

关键词： computational complexity concave programming filtering theory MIMO radar power amplifiers radar antennas radar detection radar signal processing space-time adaptive processing transmitting antennas

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Enhanced particle swarm optimisation applied for transient angle and voltage constrained discrete optimal power flow with flexible AC transmission system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第1期9卷 61-74页

作者： Xia, Shiwei Chan, Ka Wing Bai, Xuefeng Guo, Zhizhong Hong Kong Polytech Univ Dept Elect Engn Kowloon Hong Kong Peoples R China Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Heilongjiang Peoples R China

Transient stability constrained optimal power flow (TSCOPF) is increasingly important as many modern power systems nowadays have been forced to operate close to their stability limits. In this study, a non-convex mixed integer non-linear program (MINLP) TSCOPF model with consideration of valve-point effects and discrete control variables is first presented, in which the energy-based transient angle and voltage constraints are simultaneously considered as an integrated stability control process. The proposed model is general and flexible with support for any complex dynamic components, including detailed generator model and flexible AC transmission system (FACTS) devices, valve-point effects and discrete control variables. An enhanced particle swarm optimisation with dynamic adjusted inertia weight and shrinking Gaussian distribution disturbance is then proposed to solve this challenging MINLP-TSCOPF problem with comprehensive testing and evaluation using a benchmarking MINLP mathematical function and two representative power systems with FACTS devices.

关键词： particle swarm optimisation load flow flexible AC transmission systems power system transient stability concave programming integer programming nonlinear programming Gaussian distribution particle swarm optimisation transient angle voltage constrained discrete optimal power flow flexible AC transmission system transient stability constrained optimal power flow nonconvex mixed integer nonlinear program MINLP mathematical function valve-point effects discrete control variables energy-based transient angle integrated stability control process generator model flexible AC transmission system devices FACTS devices discrete control variables dynamic adjusted inertia weight Gaussian distribution disturbance MINLP-TSCOPF problem

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Joint spectrum sensing, power, and bandwidth allocation for multiband cognitive radio systems

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IET COMMUNICATIONS 2020年第19期14卷 3490-3497页

作者： Karimi, Mohammad Ahmadi, Shadi Univ Kurdistan Dept Elect Engn Kurdistan Iran

Multiband cognitive radio (CR) signalling allows CR networks to efficiently take advantage of the unused frequency bands. On the other hand, joint spectrum sensing and power allocation in CR systems has been shown to enable higher opportunities for CR transmission. In this work, the authors perform joint spectrum sensing and resource allocation (power and bandwidth) in a multiband CR system. This is achieved by defining an optimisation problem, which is formulated to maximise the average opportunistic CR data rate under constraints on interference, power budget, and total available bandwidth leading to a non-convex optimisation problem. As the original formulated optimisation problem is inherently non-convex, they convert it into a convex problem where the optimal solution is obtained using the Lagrange multipliers method and linear programming. For performance comparison, they consider the classical method based on equal bandwidth allocation for each subchannel. They provide several numerical results to contrast the performance of the proposed method compared to the classical method in terms of the average achievable CR data rate.

关键词： cognitive radio bandwidth allocation signal detection radio spectrum management linear programming convex programming resource allocation concave programming radiofrequency interference channel allocation CR networks unused frequency bands joint spectrum sensing power allocation CR systems CR transmission multiband CR system average opportunistic CR data rate power budget nonconvex optimisation problem convex problem bandwidth allocation average achievable CR data rate multiband cognitive radio systems multiband cognitive radio signalling resource allocation Lagrange multiplier method linear programming

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Joint transmitter and PS factor optimal design for secure transmission in MIMO full-duplex relay network powered by SWIPT with perfect and imperfect channel state information

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IET COMMUNICATIONS 2020年第6期14卷 1056-1064页

作者： Zhang, Meng Zheng, Jianhong He, Yun Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

The joint transmitter and PS factor optimal design scheme for secure transmission in multiple-input multiple-output (MIMO) full-duplex relay network powered by simultaneous wireless information and power transfer (SWIPT) is studied. An optimisation problem is presented, aimed at maximising the system security capacity by jointly optimising transmitter and PS factor while satisfying the energy harvest constraint in both perfect and imperfect channel state information (CSI). In perfect CSI case, the optimisation function is formulated with energy harvest constraint, and semidefinite relaxation (SDR) is introduced to solve the non-convex optimisation problem. In imperfect CSI case, it is assumed that only the first and second order statistics of the eavesdropper's channels are available, but the specific distribution is unknown. In this case, the security capacity of the system is maximised while satisfying the security outage probability and energy harvest constraint. The SDR solution based on equivalent conic reformulation is proposed. And the simulation results demonstrate the effectiveness of the optimal designs in both CSI cases.

关键词： convex programming energy harvesting MIMO communication wireless channels relay networks (telecommunication) telecommunication network reliability concave programming telecommunication security optimisation probability joint transmitter PS factor optimal design secure transmission MIMO full-duplex relay network SWIPT perfect channel state information imperfect channel state information multiple-input multiple-output full-duplex relay network power transfer system security capacity energy harvest constraint perfect CSI case optimisation function nonconvex optimisation problem imperfect CSI case eavesdropper security outage probability CSI cases

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Spectrum sensing and resource allocation for 5G heterogeneous cloud radio access networks

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IET COMMUNICATIONS 2022年第4期16卷 348-358页

作者： Safi, Hossein Montazeri, Ali Mohammad Rostampoor, Javane Parsaeefard, Saeedeh Shahid Beheshti Univ Fac Elect Engn Tehran Iran Iran Telecommun Res Ctr ITRC Commun Technol CT Res Fac Tehran Iran Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

In this paper, the problem of opportunistic spectrum sharing for the next generation of wireless systems empowered by the cloud radio access network (C-RAN) is studied. More precisely, low-priority users employ cooperative spectrum sensing to detect a vacant portion of the spectrum that is not currently used by high-priority users. The authors' aim is to maximize the overall throughput of the low-priority users while guaranteeing the quality of service of the high-priority users. This objective is attained by optimally adjusting spectrum sensing time, with respect to target probabilities of detection and false alarm, as well as dynamically allocating C-RAN resources, that is, powers, sub-carriers, remote radio heads, and base-band units. To solve this problem, which is non-convex and NP-hard, a low-complex iterative solution is proposed. Numerical results demonstrate the necessity of sensing time adjustment as well as effectiveness of the proposed solution.

关键词： 5G heterogeneous cloud radio access networks remote radio heads nonconvex problem high-priority users base-band unit signal detection low-priority users radio spectrum management C-RAN resource allocation concave programming low-complex iterative solution cooperative spectrum sensing power allocation cloud computing radio access networks subcarrier allocation 5G mobile communication cooperative communication iterative methods quality of service dynamic resource allocation overall throughput telecommunication computing NP-hard problem

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Robust transmit beamforming design for multi-cell multiuser MIMO NOMA

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IET COMMUNICATIONS 2022年第18期16卷 2193-2199页

作者： Mohammadkhani, Saeideh Iran Univ Sci & Technol Elect Engn Dept Tehran Iran

This paper focuses on the robust beamforming design for downlink multi-cell multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) systems without having any restrictions on the number of users and cells. The beamforming matrices are designed by solving a sum rate maximization problem under imperfect channel state information (CSI) while satisfying transmission power constraints. In order to solve the proposed non-convex optimization problem with infinite-dimensional robust constraints, an iterative algorithm is provided to transform this problem into a convex form by utilizing diagonalization theory and introducing some auxiliary variables. Numerical results demonstrate that the proposed algorithm converges quickly, and the proposed robust NOMA design achieves strong robustness and acceptable performance.

关键词： matrix algebra nonconvex optimization problem robust transmit beamforming design downlink multicell multiple-input wireless channels MIMO communication sum rate maximization problem convex programming optimisation concave programming array signal processing infinite-dimensional robust constraints multicell multiuser MIMO NOMA robust NOMA design beamforming matrices nonorthogonal multiple access imperfect channel state information iterative methods transmission power constraints robust beamforming design strong robustness multiple-output nonorthogonal multiple access systems

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UE-centric clustering and resource allocation for practical two-tier heterogeneous cellular networks

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IET COMMUNICATIONS 2018年第18期12卷 2384-2392页

作者： Oguejiofor, Obinna S. Zhang, Li X. Nawaz, Naveed Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

The heterogeneous cellular network (HetNet) has emerged as a promising technology for the fifth generation mobile networks that can be used to meet high demand of data rate and better quality of service (QoS) performance. However, the performance of HetNet will depend on how scarce resources such as frequency, time, power and spatial resource are shared among user equipments (UEs) in the system and also how interference is controlled. In this work, we utilise UE-Centric clustering as a tool to effectively determine the interfering base stations (BSs) that cause significant interference to each UE in the network. These interfering BSs together with the serving BSs of these interfered UEs will coordinate and make resource allocation (RA) decisions together to allocate spatial directions to each UE in the network in order to manage interference in the network. We formulate the RA problem as maximizing the weighted sum-rate of the HetNet while fulfilling power, QoS and interference constraints. This optimization problem is non-convex. We readily split the RA problem into two sub-problems: the spatial direction allocation problem and the power allocation problem, respectively. We are able to solve these problems efficiently using SeDumi. Simulation results of our proposed method, show significant improvement.

关键词： quality of service cellular radio radiofrequency interference resource allocation concave programming 5G mobile communication pattern clustering UE-centric clustering two-tier heterogeneous cellular networks HetNet fifth generation mobile networks data rate QoS scarce resources spatial resource interfering base stations interfering BSs serving BSs interfered UE resource allocation decision RA problem interference constraints spatial direction allocation problem power allocation problem quality of service performance weighted sum-rate maximization nonconvex optimisation problem interference management SeDumi interior point methods

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Improved RPCA method via non-convex regularisation for image denoising

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IET SIGNAL PROCESSING 2020年第5期14卷 269-277页

作者： Wang, Sijie Xia, Kewen Wang, Li Zhang, Jiangnan Yang, Huaijin Hebei Univ Technol Sch Elect & Informat Engn Tianjin Peoples R China

The traditional robust principal component analysis (RPCA) model is based on the nuclear norm, which usually underestimates the singular values of the low-rank matrix. As a consequence, the restoration image experiences serious interference by Gaussian noise, and the image quality degenerates during the denoising process. Therefore, an improved RPCA method via non-convex regularisation terms is proposed to remedy the above shortcomings. First, in order to estimate the singular value of the low-rank matrix more accurately, the authors employ the non-convex penalty function and add a weight vector to it. Then, the regularisation with non-convex penalty function and its weighted version are used to replace the nuclear norm and entry-wise l(1) norm in original RPCA, respectively, to establish an improved model. Finally, an optimal solution algorithm is derived by developing the alternating direction multiplier method. Experimental results show that the proposed method has better performance in terms of both quantitative measurement and visual perception quality than other several state-of-the-art image denoising methods.

关键词： image denoising Gaussian noise concave programming image restoration convex programming matrix algebra principal component analysis alternating direction multiplier method image denoising methods improved RPCA method robust principal component analysis model nuclear norm singular value low-rank matrix restoration image serious interference Gaussian noise image quality denoising process nonconvex regularisation terms nonconvex penalty function

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