scholarly journals Throughput Fairness in Cognitive Backscatter Networks With Residual Hardware Impairments and a Nonlinear EH Model

2021 ◽  
Author(s):  
Xiaona Gao ◽  
Liqin Shi ◽  
Guangyue Lu
Keyword(s):  
Iterative Algorithm ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Primary User ◽  
Energy Harvesters ◽  
Hardware Impairments ◽  
Convex Problem ◽  
Resource Allocation Scheme ◽  
Nonlinear Energy

Abstract This paper is to design a throughput fairness-aware resource allocation scheme for a cognitive backscatter network (CBN), where multiple backscatter devices (BDs) take turns to modulate information on the primary signals and backscatter the modulated signals to a cooperative receiver (C-Rx), while harvesting energy to sustain their operations. The nonlinear energy harvesting (EH) circuits at the BDs and the residual hardware impairments (HWIs) at the transceivers are considered to better reflect the properties of the practical energy harvesters and transceivers, respectively. To ensure the throughput fairness among BDs, we formulate an optimization problem to maximize the minimum throughput of BDs by jointly optimizing the transmit power of the primary transmitter, the backscattering time and reflection coefficient for each BD, subject to the primary user’s quality of service (QoS) and BDs’ energy-causality constraints. We introduce the variable slack and decoupling methods to transform the formulated non-convex problem, and propose an iterative algorithm based on block coordinate descent (BCD) technique to solve the transformation problem. We also investigate a special CBN with a single BD and derive the optimal solution in the closed form to maximize the BD's throughput. Numerical results validate the quick convergence of the proposed iterative algorithm and that the proposed scheme ensures much fairness than the existing schemes.

scholarly journals Joint optimization of computing ratio and access points’ density for mixed mobile edge/cloud computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Tianqi Jing ◽  
Shiwen He ◽  
Fei Yu ◽  
Yongming Huang ◽  
Luxi Yang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Coverage Probability ◽  
Optimization Problem ◽  
Mobile Cloud Computing ◽  
Low Complexity ◽  
Mixed Integer ◽  
Joint Optimization ◽  
Mobile Cloud ◽  
Convex Problem

AbstractCooperation between the mobile edge computing (MEC) and the mobile cloud computing (MCC) in offloading computing could improve quality of service (QoS) of user equipments (UEs) with computation-intensive tasks. In this paper, in order to minimize the expect charge, we focus on the problem of how to offload the computation-intensive task from the resource-scarce UE to access point’s (AP) and the cloud, and the density allocation of APs’ at mobile edge. We consider three offloading computing modes and focus on the coverage probability of each mode and corresponding ergodic rates. The resulting optimization problem is a mixed-integer and non-convex problem in the objective function and constraints. We propose a low-complexity suboptimal algorithm called Iteration of Convex Optimization and Nonlinear Programming (ICONP) to solve it. Numerical results verify the better performance of our proposed algorithm. Optimal computing ratios and APs’ density allocation contribute to the charge saving.

scholarly journals Robust Beamforming Design for Secure V2X Downlink System with Wireless Information and Power Transfer under a Nonlinear Energy Harvesting Model

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3294 ◽  
Author(s):  
Shidang Li ◽  
Chunguo Li ◽  
Weiqiang Tan ◽  
Baofeng Ji ◽  
Luxi Yang
Keyword(s):  
Energy Harvesting ◽  
Traffic Safety ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Base Station ◽  
Power Splitting ◽  
Power Transfer ◽  
Vehicular Communication ◽  
Downlink System ◽  
Nonlinear Energy

Vehicle to everything (V2X) has been deemed a promising technology due to its potential to achieve traffic safety and efficiency. This paper considers a V2X downlink system with a simultaneous wireless information and power transfer (SWIPT) system where the base station not only conveys data and energy to two types of wireless vehicular receivers, such as one hybrid power-splitting vehicular receiver, and multiple energy vehicular receivers, but also prevents information from being intercepted by the potential eavesdroppers (idle energy vehicular receivers). Both the base station and the energy vehicular receivers are equipped with multiple antennas, whereas the information vehicular receiver is equipped with a single antenna. In particular, the imperfect channel state information (CSI) and the practical nonlinear energy harvesting (EH) model are taken into account. The non-convex optimization problem is formulated to maximize the minimum harvested energy power among the energy vehicular receivers satisfying the lowest harvested energy power threshold at the information vehicular receiver and secure vehicular communication requirements. In light of the intractability of the optimization problem, the semidefinite relaxation (SDR) technique and variable substitutions are applied, and the optimal solution is proven to be tight. A number of results demonstrate that the proposed robust secure beamforming scheme has better performance than other schemes.

scholarly journals Energy Efficiency Design for Secure MISO Cognitive Radio Network Based on a Nonlinear EH Model

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Ni ◽  
Xinyu Da ◽  
Hang Hu ◽  
Miao Zhang
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Primary User ◽  
Open Architecture ◽  
Maximization Problem ◽  
Transmit Beamforming ◽  
Secrecy Rate ◽  
Single Output

In this work, we investigate the secrecy energy efficiency (SEE) optimization problem for a multiple-input single-output (MISO) cognitive radio (CR) network based on a practical nonlinear energy-harvesting (EH) model. In particular, the energy receiver (ER) is assumed to be a potential eavesdropper due to the open architecture of a CR network with simultaneous wireless information and power transfer (SWIPT), such that the confidential message is prone to be intercepted in wireless communications. The aim of this work is to provide a secure transmit beamforming design while satisfying the minimum secrecy rate target, the minimum EH requirement, and the maximum interference leakage power to primary user (PU). In addition, we consider that all the channel state information (CSI) is perfectly known at the secondary transmitter (ST). We formulate this beamforming design as a SEE maximization problem; however, the original optimization problem is not convex due to the nonlinear fractional objective function. To solve it, a novel iterative algorithm is proposed to obtain the globally optimal solution of the primal problem by using the nonlinear fractional programming and sequential programming. Finally, numerical simulation results are presented to validate the performance of the proposed scheme.

scholarly journals Data Rate Maximization in RIS-Assisted D2D Communication with Transceiver Hardware Impairments

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 200
Author(s):  
Hongxia Zheng ◽  
Chiya Zhang ◽  
Yatao Yang ◽  
Xingquan Li ◽  
Chunlong He
Keyword(s):  
Optimization Problem ◽  
Original Problem ◽  
Optimal Solution ◽  
Base Station ◽  
D2d Communication ◽  
Convex Optimization Problem ◽  
Hardware Impairments ◽  
Convex Problems ◽  
Rate Maximization ◽  
Power Limit

We maximize the transmit rate of device-to-device (D2D) in a reconfigurable intelligent surface (RIS) assisted D2D communication system by satisfying the unit-modulus constraints of reflectin elements, the transmit power limit of base station (BS) and the transmitter in a D2D pair. Since it is a non-convex optimization problem, the block coordinate descent (BCD) technique is adopted to decouple this problem into three subproblems. Then, the non-convex subproblems are approximated into convex problems by using successive convex approximation (SCA) and penalty convex-concave procedure (CCP) techniques. Finally, the optimal solution of original problem is obtained by iteratively optimizing the subproblems. Simulation results reveal the validity of the algorithm that we proposed to solve the optimization problem and illustrate the effectiveness of RIS to improve the transmit rate of the D2D pair even with hardware impairments.

scholarly journals Hierarchical Clustering as a Dimension Reduction Technique for Markowitz Portfolio Optimization

2020 ◽  
Vol 27 (1) ◽  
pp. 62-71
Author(s):  
Anatoliy Y. Poletaev ◽  
Elena M. Spiridonova
Keyword(s):  
Dimension Reduction ◽  
Portfolio Selection ◽  
Hierarchical Clustering ◽  
Optimization Problem ◽  
Pair Correlation ◽  
Optimal Solution ◽  
Optimal Portfolio ◽  
Practical Applications ◽  
Optimal Portfolio Selection

Optimal portfolio selection is a common and important application of an optimization problem. Practical applications of an existing optimal portfolio selection methods is often difficult due to high data dimensionality (as a consequence of the large number of securities available for investment). In this paper, a method of dimension reduction based on hierarchical clustering is proposed. Clustering is widely used in computer science, a lot of algorithms and computational methods have been developed for it. As a measure of securities proximity for hierarchical clustering Pearson pair correlation coefficient is used. Further, the proposed method’s influence on the quality of the optimal solution is investigated on several examples of optimal portfolio selection according to the Markowitz Model. The influence of hierarchical clustering parameters (intercluster distance metrics and clustering threshold) on the quality of the obtained optimal solution is also investigated. The dependence between the target return of the portfolio and the possibility of reducing the dimension using the proposed method is investigated too. For each considered example in the paper graphs and tables with the main results of the proposed method - application which are the decrease of the dimension and the drop of the yield (the decrease of the quality of the optimal solution) - for a portfolio constructed using the proposed method compared to a portfolio constructed without the proposed method are given. For the experiments the Python programming language and its libraries: scipy for clustering and cvxpy for solving the optimization problem (building an optimal portfolio) are used.

scholarly journals Joint Placement and Device Association of UAV Base Stations in IoT Networks

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2157 ◽  
Author(s):  
Ashfaq Ahmed ◽  
Muhammad Awais ◽  
Tallha Akram ◽  
Selman Kulac ◽  
Musaed Alhussein ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Cost Effective ◽  
Low Complexity ◽  
Base Stations ◽  
Large Area ◽  
Significant Research ◽  
Minimum Number ◽  
Efficient Scheme

Drone base stations (DBSs) have received significant research interest in recent years. They provide a flexible and cost-effective solution to improve the coverage, connectivity, quality of service (QoS), and energy efficiency of large-area Internet of Things (IoT) networks. However, as DBSs are costly and power-limited devices, they require an efficient scheme for their deployment in practical networks. This work proposes a realistic mathematical model for the joint optimization problem of DBS placement and IoT users’ assignment in a massive IoT network scenario. The optimization goal is to maximize the connectivity of IoT users by utilizing the minimum number of DBS, while satisfying practical network constraints. Such an optimization problem is NP-hard, and the optimal solution has a complexity exponential to the number of DBSs and IoT users in the network. Furthermore, this work also proposes a linearization scheme and a low-complexity heuristic to solve the problem in polynomial time. The simulations are performed for a number of network scenarios, and demonstrate that the proposed heuristic is numerically accurate and performs close to the optimal solution.

scholarly journals Mixed Replenishment Policy for ATO Supply Chain Based on Hybrid Genetic Simulated Annealing Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Huang ◽  
Yan Jin ◽  
Bo Huang ◽  
Han-Guang Qiu
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Genetic Simulated Annealing Algorithm ◽  
Replenishment Policy ◽  
Annealing Algorithm ◽  
Searching Ability

Timely components replenishment is the key to ATO (assemble-to-order) supply chain operating successfully. We developed a production and replenishment model of ATO supply chain, where the ATO manufacturer adopts both JIT and (Q,r) replenishment mode simultaneously to replenish components. The ATO manufacturer’s mixed replenishment policy and component suppliers’ production policies are studied. Furthermore, combining the rapid global searching ability of genetic algorithm and the local searching ability of simulated annealing algorithm, a hybrid genetic simulated annealing algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is given to illustrate the rapid convergence of the HGSAA and the good quality of optimal mixed replenishment policy obtained by the HGSAA. Finally, by comparing the HGSAA with GA, it is proved that the HGSAA is a more effective and reliable algorithm than GA for solving the optimization problem of mixed replenishment policy for ATO supply chain.

scholarly journals Algorithmization of multi-criteria design processes for power and hydropower facilities

2020 ◽  
Vol 8 (3) ◽  
pp. 51-57
Author(s):  
Vladimir Kulikov ◽  
Vladimir Khokhlov ◽  
Z Titova ◽  
O. Galonen
Keyword(s):  
Complex Systems ◽  
Iterative Algorithm ◽  
Optimization Problem ◽  
Software Implementation ◽  
Probabilistic Algorithms ◽  
Design Processes ◽  
Best Value ◽  
Gradient Optimization ◽  
Computational Procedures

The paper considers aspects of the development of algorithms for optimizing complex systems. The principles of constructing procedures for adjusting the parameters of variable gradient optimization algorithms are proposed. In any iterative algorithm, there are parameters that require their adjustment. To control and adjust parameters, - criteria are formed that determine the quality of adjustments. The problem of determining the best value of adjustment parameters belongs to the same class as the original optimization problem. During the operation of algorithms, their parameters are adapted to the original values. The paper provides recommendations for software implementation of probabilistic algorithms and construction of computational procedures for probabilistic computational experiments based on them.

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

Sign in / Sign up

Export Citation Format

Share Document