Resource Allocation in NOMA-Assisted Ambient Backscatter Communication System

This paper considers a non-orthogonal multiple access (NOMA)-assisted ambient backscatter communication (AmBC) system. To maximize the achievable sum rate (ASR) of the AmBC system, a joint optimization problem over a backscatter device (BD) grouping strategy, reflection coefficients, and decoding order is formulated, where the BD grouping strategy contains the number of BD groups and the BD allocation strategy. The BD grouping strategy, the reflection coefficients, and the decoding order are all intertwined, and the global search is extremely complex. As a result, we propose a four-step optimization algorithm. First, we give the closed-form optimal solution of the BD decoding order and reflection coefficient for a given grouping strategy. Then, for a given number of BD groups, we propose a low-complexity BD allocation strategy based on the complexity–performance trade-off. Finally, the number of BD groups with the largest ASR is selected as the global optimal number of BD groups. The simulation results show that the proposed four-step optimization algorithm is better than the benchmark solution.

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A Low-Complexity Resource Allocation for Multiple Access Passive IoT System

Electronics ◽

10.3390/electronics8121421 ◽

2019 ◽

Vol 8 (12) ◽

pp. 1421

Author(s):

Shiying Han ◽

Zixiong Wang

Keyword(s):

Resource Allocation ◽

Optimization Problems ◽

Low Complexity ◽

Reflection Coefficients ◽

Primary System ◽

Adaptive Optimization ◽

Transmit Power ◽

Closed Form Solutions ◽

Backscatter Communication

An ambient backscatter communication (AmBC) system with multiple backscatter devices (BDs) is investigated in this work. The cooperative reader receives the information from the primary transmitter (PT) and the multiple BDs simultaneously. With the asymptotic signal-to-noise-plus-interference ratio (SINR) of the BDs, an optimization problem that jointly optimizes the reflection coefficients of BDs and the primary transmit power is formulated. Considering that the adaptive optimization of reflection coefficients according to the instantaneous primary channel state information (CSI) is unaffordable in practice, we propose a low-complexity resource allocation scheme, which results in a long-term configuration of the BD reflection coefficients before the primary transmit power is allocated. With the long-term reflection coefficients, the transmit power of the primary system is optimized by solving the transformed two cascaded optimization problems which have closed-form solutions. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

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AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666181130140709 ◽

2019 ◽

Vol 19 (2) ◽

pp. 139-145 ◽

Cited By ~ 1

Author(s):

Bote Lv ◽

Juan Chen ◽

Boyan Liu ◽

Cuiying Dong

Keyword(s):

Optimization Algorithm ◽

Learning Strategy ◽

Optimal Solution ◽

Population Diversity ◽

Global Optimal Solution ◽

Plant Medicine ◽

Suitability Index ◽

Sensing Model ◽

Local Optimal Solution ◽

Global Optimal

Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.

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Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

The Open Electrical & Electronic Engineering Journal ◽

10.2174/1874129001408010723 ◽

2014 ◽

Vol 8 (1) ◽

pp. 723-728 ◽

Cited By ~ 1

Author(s):

Chenhao Niu ◽

Xiaomin Xu ◽

Yan Lu ◽

Mian Xing

Keyword(s):

Neural Network ◽

Optimization Algorithm ◽

Load Forecasting ◽

Optimal Solution ◽

Electric Power System ◽

Local Optimum ◽

Good Prediction ◽

Ant Colony Optimization Algorithm ◽

The Neural Network ◽

Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

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Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200365 ◽

2020 ◽

pp. 1-12

Author(s):

Zheping Yan ◽

Jinzhong Zhang ◽

Jialing Tang

Keyword(s):

Lateral Inhibition ◽

Optimization Algorithm ◽

Image Matching ◽

Autonomous Underwater Vehicle ◽

Optimal Solution ◽

Search Space ◽

Whale Optimization Algorithm ◽

Inhibition Mechanism ◽

Whale Optimization ◽

Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

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Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Electronics ◽

10.3390/electronics10050597 ◽

2021 ◽

Vol 10 (5) ◽

pp. 597

Author(s):

Kun Miao ◽

Qian Feng ◽

Wei Kuang

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm ◽

Optimal Solution ◽

Small Region ◽

Benchmark Problems ◽

Swarm Optimization ◽

Local Searches ◽

Engineering Design Problems ◽

Direction Search

The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm. However, it suffers search stagnation from being trapped into a sub-optimal solution in an optimization problem. This paper proposes a novel hybrid algorithm (SDPSO) to improve its performance on local searches. The algorithm merges two strategies, the static exploitation (SE, a velocity updating strategy considering inertia-free velocity), and the direction search (DS) of Rosenbrock method, into the original PSO. With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search. The SDPSO algorithm was implemented and tested on unconstrained benchmark problems (CEC2014) and some constrained engineering design problems. The performance of SDPSO is compared with that of other optimization algorithms, and the results show that SDPSO has a competitive performance.

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A Physical Layer Security Enhancement Scheme under the Ambient Backscatter System

Symmetry ◽

10.3390/sym13010005 ◽

2020 ◽

Vol 13 (1) ◽

pp. 5

Author(s):

Pengfei Hou ◽

Jianping Gong ◽

Jumin Zhao

Keyword(s):

Radio Frequency ◽

Radio Frequency Identification ◽

Physical Layer Security ◽

Physical Layer ◽

Artificial Noise ◽

Identification System ◽

Secrecy Rate ◽

Channel Quality ◽

Backscatter Communication ◽

Ambient Backscatter

In this paper, we proposed a scheme that Injects artificial noise from the tag end (IANT) to enhance the physical layer security of the ambient backscatter communication (ABC) system. The difference between the ABC system and the traditional radio frequency identification system is whether it uses the radio frequency (RF) signals in the environment to supply energy and modulation information for passive tags. In the IANT scheme, we select the best tag to communicate with the reader according to the channel quality between tags and reader, and at the same time select another tag to generate artificial noise that affects the receiving effect of the eavesdropper. This paper uses the method of generating noise copies in the reader to reduce the interference of artificial noise on the signal received by the reader. The simulation results show that with the increase in channel quality between tags and reader and the increase in the number of tags, the proposed IANT scheme is significantly superior to the contrast scheme in terms of system achievable secrecy rate, effectively enhancing the physical layer security of the ABC system.

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Exploiting Multiple Antennas for Cognitive Ambient Backscatter Communication

IEEE Internet of Things Journal ◽

10.1109/jiot.2018.2856633 ◽

2019 ◽

Vol 6 (1) ◽

pp. 765-775 ◽

Cited By ~ 34

Author(s):

Huayan Guo ◽

Qianqian Zhang ◽

Sa Xiao ◽

Ying-Chang Liang

Keyword(s):

Multiple Antennas ◽

Backscatter Communication ◽

Ambient Backscatter

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Batch Scheduling In A Two-Stage Flexible Flow Shop Problem

Foundations of Computing and Decision Sciences ◽

10.2478/fcds-2014-0001 ◽

2014 ◽

Vol 39 (1) ◽

pp. 3-16 ◽

Cited By ~ 6

Author(s):

Enrique Gerstl ◽

Gur Mosheiov ◽

Assaf Sarig

Keyword(s):

Flow Shop ◽

Optimal Solution ◽

Setup Time ◽

Optimal Number ◽

Two Stage ◽

Second Stage ◽

Flow Shop Problem ◽

Batch Sizes ◽

Batch Schedule ◽

Identical Jobs

Abstract We study a special two-stage flexible flowshop, which consists of several parallel identical machines in the first stage and a single machine in the second stage. We assume identical jobs, and the option of batching, with a required setup time prior to the processing of a new batch. We also consider the option to use only a subset of the available machines. The objective is minimum makespan. A unique optimal solution is introduced, containing the optimal number of machines to be used, the sequence of batch sizes, and the batch schedule. The running time of our proposed solution algorithm is independent of the number of jobs, and linear in the number of machines

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Study on Mix-Variable Collaborative Design Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.215-216.592 ◽

2012 ◽

Vol 215-216 ◽

pp. 592-596

Author(s):

Li Gao ◽

Rong Rong Wang

Keyword(s):

Design Optimization ◽

Product Design ◽

Optimization Algorithm ◽

Collaborative Design ◽

Optimization Problems ◽

Optimal Solution ◽

Collaborative Optimization ◽

Continuous Variables ◽

Complex Product ◽

Divide And Rule

In order to deal with complex product design optimization problems with both discrete and continuous variables, mix-variable collaborative design optimization algorithm is put forward based on collaborative optimization, which is an efficient way to solve mix-variable design optimization problems. On the rule of “divide and rule”, the algorithm decouples the problem into some relatively simple subsystems. Then by using collaborative mechanism, the optimal solution is obtained. Finally, the result of a case shows the feasibility and effectiveness of the new algorithm.

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