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nonconvex BI-OBJECTIVE OPTIMIZATION MODEL WITH STABILITY GUARANTEES AND ITERATIVE ALGORITHM FOR S-TYPE BIOCHEMICAL SYSTEMS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第5期21卷 3441-3464页

作者： Yue, Meibo Xu, Gongxian Feng, Shuang Bohai Univ Sch Math Sci Jinzhou 121013 Peoples R China

In this paper, the steady-state optimization problem of S-type biochemical systems is investigated. A mathematical model of the S-type biochemical system is given. A nonconvex bi-objective optimization model with stability guarantees is constructed with the optimization objectives of maximizing the product yield and minimizing the sum of metabolite concentrations, which can ensure that the S-type biochemical system operates at an optimal and stable steady state. The nonconvex bi-objective optimization model is transformed into a nonconvex single-objective optimization problem based on the normal-boundary intersection (NBI) method. In order to efficiently solve the transformed nonconvex single-objective problem, a geometric programming algorithm is proposed based on the equivalence transformation and monomial compensation strategy, and the convergence analysis of the algorithm is given. An iterative solution algorithm for nonconvex bi-objective optimization model with stability guarantees is given based on this algorithm. The Pareto optimal solutions and Pareto optimal fronts of three different nonconvex bi-objective optimization models are obtained by computing three examples of S-type biochemical systems.

关键词： S-type biochemical system bi-objective optimization stability guar- antees nonconvex problem algorithm geometric programming

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Near-optimal hybrid precoding for millimeter wave massive MIMO systems via cost-efficient Sub-connected structure

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IET COMMUNICATIONS 2020年第14期14卷 2340-2349页

作者： Huang, Yu Liu, Chen Song, Yunchao Yu, Xiaolei Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing Peoples R China Nanjing Univ Posts & Telecommun Coll Microelect Nanjing Peoples R China Nanjing Univ Posts & Telecommun Natl & Local Joint Engn Lab RF Integrat & Microas Nanjing Peoples R China Jiangsu Inst Qual & Standardizat Natl Qual Supervis & Testing Ctr RFID Prod Jiangs Nanjing Peoples R China

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) is a promising technology for next generation wireless communications. The utilisation of traditional full digital precoding techniques for mmWave massive MIMO systems is too costly. Fortunately, the hybrid (analogue/digital) precoding is a better choice. In this study, the authors investigate a near-optimal iterative approximation hybrid precoding algorithm via cost-efficient sub-connected structure for mmWave massive MIMO systems. Based on the minimum mean square error (MSE) criterion, the optimal design of the proposed hybrid precoders is non-convex. The original problem is reformulated as two optimisation sub-problems, where one of the sub-problems is convex and another is non-convex. First, the authors convert the non-convex sub-problem into a convex problem by relaxing the constant modulus constraint from beampattern with interference control method, and eliminating the block diagonal constraint from vector operation with the properties of Kronecker product. Then the two convex sub-problems are solved iteratively. The proposed algorithm can achieve a highly approximate optimal solution, which can be theoretically demonstrated to converge to a Karush-Kuhn-Tucker point of the original problem. Simulation results show that the proposed algorithm via both sub-connected and fully connected structure gets favourable performance in terms of MSE and achievable rate.

关键词： mean square error methods iterative methods MIMO communication precoding millimetre wave communication convex programming concave programming interference suppression approximation theory millimeter wave massive MIMO systems millimeter wave massive multiple-input multiple-output systems traditional full digital precoding techniques mmWave massive MIMO systems constant modulus block diagonal constraints nonconvex problem convex problem near-optimal hybrid precoding cost-efficient sub-connected structure next-generation wireless communications analogue-digital precoding gigabits per second transmitting data rate gigabits per second transmitting data rate near-optimal iterative approximation hybrid precoding algorithm minimum mean square error criterion optimisation sub-problems beampattern interference control method Kronecker product approximate optimal solution Karush-Kuhn-Tucker point achievable rate MSE

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Joint active and passive beamforming optimization for multigroup multicast system aided by intelligent reflecting surface

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IET COMMUNICATIONS 2021年第4期15卷 642-652页

作者： Wang, Junzhi Liao, Xiangbai Liu, Yingzhuang Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Jiaxing Univ Coll Math Phys & Informat Engn South Yuexiu Rd Jiaxing Zhejiang Peoples R China

An intelligent-reflecting-surface aided multigroup multicast communication system is investigated in this paper. We develop energy-efficient designs for both the precoding matrix at the base station and passive reflecting beamforming at intelligent-reflecting-surface, subject to the minimum signal-to-interference-plus-noise ratio constraint of each user and the unit-modulus constraint imposed by passive intelligent-reflecting-surface. This leads to a complex non-convex problem for which to tackle we propose two novel algorithms based on alternating optimization techniques. Specifically, one algorithm employs semi-definite relaxation to obtain the suboptimal reflection coefficient vector and suboptimal precoding vector for each group. In order to reduce the computational complexity, the second algorithm transforms this non-convex problem into two second-order cone programming problems. Numerical results show that intelligent-reflecting-surface can significantly reduce the power consumption of the base station.

关键词： multicast communication convex programming Optimisation techniques Signal processing and detection intelligent-reflecting-surface aided multigroup multicast communication system Codes precoding optimisation nonconvex problem passive reflecting concave programming multigroup multicast system intelligent reflecting surface minimum signal-to-interference-plus-noise ratio constraint array signal processing computational complexity Radio links and equipment passive beamforming optimization suboptimal reflection coefficient vector MIMO communication

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Optimal downlink and uplink design in a wireless powered two-user indoor communication system

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IET COMMUNICATIONS 2022年第7期16卷 745-759页

作者： Syam, M. S. Che, Yue Ling Luo, Sheng Wu, Kaishun Shenzhen Univ Coll Elect & Informat Engn Shenzhen Guangdong Peoples R China Shenzhen Univ IoT Res Ctr Shenzhen Guangdong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Canghai Campus Shenzhen 518060 Guangdong Peoples R China

This paper applies the wireless powered communication network (WPCN) to an indoor communication system with two energy harvesting (EH) enabled users. Unlike the existing WPCN works designed for outdoor communications, where each user harvests energy only from the signals transmitted by a dedicatedly deployed hybrid access point (H-AP), due to the short device-to-device distance in the indoor scenario, each user additionally harvests a sufficient amount of energy from the information signals transmitted by the H-AP and the other user. First, the joint downlink and uplink throughput are maximized for the wireless powered indoor communication system. This problem is non-convex. Thus, the authors manage to transform this problem into a convex one and solve it using convex optimization techniques. The solutions reveal that the total throughput increases largely over a reduced device-to-device distance due to the resultant harvested energy from both energy and information signals at each user. However, an unfair downlink versus uplink rate allocation phenomenon is observed. Thus, considering the importance of uplink communication quality for various indoor applications, a new problem is further proposed to maximize the uplink sum-throughput over both users with an additional constraint to ensure a sufficient downlink rate. This problem is also shown to be non-convex and is solved optimally by using a method similar to that in the first problem. Numerical results demonstrate the effectiveness of the proposed approach for improving the downlink versus uplink rate allocation fairness in the indoor wireless-powered communication system.

关键词： nonconvex problem unfair downlink uplink design information signals indoor scenario sufficient downlink rate dedicatedly deployed hybrid access point energy harvesting uplink rate allocation phenomenon convex programming telecommunication power management indoor applications total throughput increases outdoor communications uplink communication quality radio access networks wireless powered indoor communication system uplink rate allocation fairness convex optimization techniques uplink throughput two-user indoor communication system optimal downlink wireless-powered communication system indoor communication uplink sum-throughput device-to-device distance mobile radio wireless powered communication network multi-access systems

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Robust transmission design for artificial noise aided multiuser broadcast system in inaccurate channel state information scenario with users of different importance

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IET COMMUNICATIONS 2023年第2期17卷 171-182页

作者： Yao, Li Liu, Youjiang Zhang, Jian He, Yudong Sichuan Jiuzhou Elect Grp Co Ltd Mianyang Sichuan Peoples R China China Acad Engn Phys CAEP Inst Elect Engn Mianyang 621900 Sichuan Peoples R China Univ Elect Sci & Technol China UESTC Sch Elect Sci & Engn Chengdu Peoples R China

In this article, robust beamforming (BF) and artificial noise (AN) design method is proposed for multiuser broadcast secure transmission under secrecy outage probability (SOP) constraints. A special scenario is assumed that the transmitter cannot obtain the accurate channel state information (CSI) of the users' channel, and different users have different priorities and importance. This special scenario makes traditional BF algorithms and AN design methods unable to guarantee the effective and secure transmission of information. To deal with intractable non-convex problems in this new design, a series of optimization methods like Bernstein-type inequality (BTI) and S-Procedure are employed to transform these problems into solvable functions. Moreover, a search algorithm is explored for simplifying the computational complexity of the optimization algorithm. After that, the new robust algorithm is compared with former methods in inaccurate CSI scenarios by numerical simulation, which shows the advantages of our algorithm.

关键词： secrecy outage probability constraints broadcast channels optimization methods Reliability Optimisation techniques inaccurate CSI scenarios nonconvex problem Signal processing and detection telecommunication network reliability array signal processing numerical simulation S-procedure search algorithm artificial noise aided multiuser broadcast system robust beamforming multiuser channels Radio links and equipment search problems robust transmission design probability telecommunication security wireless channels noise channel state information Bernstein-type inequality Other topics in statistics optimisation computational complexity Combinatorial mathematics

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Traffic Aware Resource Allocation Schemes for Multi-Cell MIMO-OFDM Systems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2016年第11期64卷 2730-2745页

作者： Venkatraman, Ganesh Tolli, Antti Juntti, Markku Le-Nam Tran Univ Oulu Ctr Wireless Commun DCE FI-90014 Oulu Finland Maynooth Univ Dept Elect Engn Maynooth Co Kildare Ireland

We consider a downlink multi-cell multiple-input multiple-output (MIMO) interference broadcast channel (IBC) using orthogonal frequency division multiplexing (OFDM) with multiple users contending for space-frequency resources in a given scheduling instant. The problem is to design precoders efficiently to minimize the number of backlogged packets queuing in the coordinating base stations (BSs). Conventionally, the queue weighted sum rate maximization (Q-WSRM) formulation with the number of backlogged packets as the corresponding weights is used to design the precoders. In contrast, we propose joint space-frequency resource allocation (JSFRA) formulation, in which the precoders are designed jointly across the space-frequency resources for all users by minimizing the total number of backlogged packets in each transmission instant, thereby performing user scheduling implicitly. Since the problem is nonconvex, we use the combination of successive convex approximation (SCA) and alternating optimization (AO) to handle nonconvex constraints in the JSFRA formulation. In the first method, we approximate the signal-to-interference-plus-noise ratio (SINR) by convex relaxations, while in the second approach, the equivalence between the SINR and the mean squared error (MSE) is exploited. We then discuss the distributed approaches for the centralized algorithms using primal decomposition and alternating directions method of multipliers. Finally, we propose a more practical iterative precoder design by solving the Karush-Kuhn-Tucker expressions for the MSE reformulation that requires minimal information exchange for each update. Numerical results are used to compare the proposed algorithms to the existing solutions.

关键词： MIMO-IBC MIMO-OFDM precoder design SCA WSRM nonconvex problem convergence analysis

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Efficient WMMSE beamforming for 5G mmWave cellular networks exploiting the effect of antenna array geometries

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IET COMMUNICATIONS 2018年第2期12卷 169-178页

作者： Abdelhakam, Mostafa M. Elmesalawy, Mahmoud M. Mahmoud, Korany R. Ibrahim, Ibrahim I. Helwan Univ Fac Engn Dept Elect Commun & Comp Cairo Egypt Minist Commun & Informat Technol Natl Telecommun Regulatory Author Giza Egypt

Millimetre wave (mmWave) transmission is considered as one of the potential technologies in fifth generation (5G) cellular networks that could allocate more bandwidth to support the rapid growth of mobile data services. However, mmWave signals experience large path loss which can overcome by employing directional beamforming with high-dimensional antenna arrays. In this study, a downlink multiuser multiple-input multiple-output beamforming optimisation problem is considered for maximising the system utility subject to per-user quality-of-service constraint as well as per-antenna and base station transmit power constraints. The difficulty of this problem lies in its non-convex nature. To address this challenge, a weighted minimum mean square error (WMMSE)-based solution is proposed to reformulate the non-convex problem into equivalent convex one. Then, the block coordinate descent method is used to determine beamforming vectors. Moreover, the different antenna array geometries either two-dimensional (2D) such as planar array and circular array (CiA), or 3D such as conformal array and conical array (CoA) are designed at the base station and their effect on the system performance is exploited. Extensive simulations show that the performance of the proposed WMMSE-based solution with CiA and CoA geometries outperforms the comparable algorithms in terms of system throughput, system power consumption and access rate.

关键词： array signal processing least mean squares methods cellular radio 5G mobile communication bandwidth allocation MIMO communication quality of service concave programming convex programming vectors planar antenna arrays WMMSE directional beamforming 5G mmwave cellular network millimetre wave transmission mmwave transmission fifth generation mmwave cellular network bandwidth allocation mobile data service high-dimensional antenna array geometry downlink multiuser multiple-input multiple-output beamforming optimisation per-user quality-of-service constraint system utility maximisation base station nonconvex problem weighted minimum mean square error convex problem block coordinate descent method beamforming vector determination planar array circular array CiA conformal array conical array CoA system power consumption

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Joint quantisation levels and power optimisation in uplink network multiple-input and multiple-output under constraint backhaul

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IET COMMUNICATIONS 2015年第16期9卷 2041-2047页

作者： Wang, Shipeng Chen, Li Chen, Xiaohui Wei, Guo Univ Sci & Technol China Chinese Acad Sci Sch Informat Sci & Technol Key Lab Wireless Opt Commun 96 Jinzhai Rd Hefei 230026 Anhui Peoples R China

This article studies the performance of the cooperative system where each base station (BS) is connected to a central processor (CP) via a noiseless and rate-limited backhaul link. The single-user compress-and-forward scheme is deployed at the BSs, and the problem for jointly setting the quantisation noise and the transmit power levels is left open. The authors formulate the weighted sum-rate (WSR) problem of optimising quantisation and transmit power levels for K-users and B-BSs situation. However, the problem is non-convex and difficult to be solved directly by conventional convex methods. In this work, an algorithm is proposed to solve the optimisation problem and achieve its global optimality. More importantly, the authors reveal the reason for the effect of the quantisation levels on the performance of cooperative system. Lower and upper bounds of quantisation levels are derived under the given signal to interference noise ratio (SINR) requirements. The lower and upper bounds are then demonstrated to be rather close to the numerical results. It is also numerically shown that the proposed algorithm can achieve the global optimality and is robust with respect to different multi-user situations.

关键词： quantisation (signal) MIMO communication cooperative communication convex programming concave programming power optimisation problem uplink network multiple input multiple output system constraint backhaul base station cooperative system single-user compress and forward scheme rate-limited backhaul link central processor quantisation noise level weighted sum-rate problem WSR problem nonconvex problem convex problem signal to interference noise ratio SINR

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A new augmented Lagrangian approach to duality and exact penalization

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JOURNAL OF GLOBAL OPTIMIZATION 2010年第2期46卷 233-245页

作者： Lalitha, C. S. Univ Delhi Rajdhani Coll Dept Math New Delhi 110075 India

In this paper, we introduce a new notion of augmenting function known as indicator augmenting function to establish a minmax type duality relation, existence of a path of solution converging to optimal value and a zero duality gap relation for a nonconvex primal problem and the corresponding Lagrangian dual problem. We also obtain necessary and sufficient conditions for an exact penalty representation in the framework of indicator augmented Lagrangian.

关键词： Augmented Lagrangian Augmenting function nonconvex problem Duality

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Joint beam selection and resource allocation for cognitive multiple targets tracking in MIMO radar with collocated antennas

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IET RADAR SONAR AND NAVIGATION 2020年第12期14卷 2000-2009页

作者： Li, Zhengjie Xie, Junwei Zhang, Haowei Xiang, Houhong Wang, Chaohui Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710126 Shaanxi Peoples R China

Compared with conventional phased array radar, the collocated multiple-input multiple-out (C-MIMO) radar is envisioned to offer greater freedom in waveform design and can simultaneously manage different beams to track multiple targets in the simultaneous multi-beam (SM) working mode. In this study, aiming at the multiple targets tracking (MTT) problem, a joint beam selection integrated with power and bandwidth allocation (JSPBA) scheme for the C-MIMO radar is proposed in the SM mode. By incorporating the modified particle filter, the predicted conditional Cramer-Rao lower bound (PC-CRLB) is calculated, which gives a measure of the best achievable performance for targets tracking. Then, the optimisation model is established with the aim of improving the worst case of PC-CRLB to achieve better performance of the worst-case tracking. Next, a three-step solution method is proposed to solve the JSPBA problem by converting the non-convex problem into a series of convex problems. At last, after the solutions are fed back to the control centre to guide the beam generation in the next tracking epoch, a cognitive tracking system is established. Simulation results confirm the superiority of the proposed method in improving the MTT performance in C-MIMO radar system.

关键词： particle filtering (numerical methods) radar tracking resource allocation target tracking bandwidth allocation MIMO radar phased array radar concave programming convex programming antenna phased arrays radar antennas joint beam selection resource allocation cognitive multiple targets collocated antennas phased array radar waveform design SM mode particle filter PC-CRLB multiple target tracking JSPBA problem nonconvex problem beam generation cognitive tracking system MTT performance C-MIMO radar system simultaneous multibeam working mode SM working mode power-bandwidth allocation integration conditional Cramer-Rao lower bound prediction

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