A Novel Hybrid Artificial Bee Colony-Based Deep Convolutional Neural Network to Improve the Detection Performance of Backscatter Communication Systems

被引:21
作者
Aghakhani, Sina [1 ]
Larijani, Ata [2 ]
Sadeghi, Fatemeh [3 ]
Martin, Diego [3 ]
Shahrakht, Ali Ahmadi [3 ]
机构
[1] Iowa State Univ, Dept Ind & Mfg Syst Engn, Ames, IA 50011 USA
[2] Oklahoma State Univ, Dept Management Informat Syst, Stillwater, OK 74074 USA
[3] Univ Politecn Madrid, ETSI Telecomunicac, Ave Complutense 30, Madrid 28040, Spain
关键词
backscatter communication; detection performance; bit-error rate; deep convolutional neural network; hybrid artificial bee colony; ULTRA-LIGHTWEIGHT;
D O I
10.3390/electronics12102263
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Backscatter communication (BC) is a promising technology for low-power and low-data-rate applications, though the signal detection performance is limited since the backscattered signal is usually much weaker than the original signal. When the detection performance is poor, the backscatter device (BD) may not be able to accurately detect and interpret the incoming signal, leading to errors and degraded communication quality. This can result in data loss, slow data transfer rates, and reduced reliability of the communication link. This paper proposes a novel approach to improve the detection performance of backscatter communication systems using evolutionary deep learning. In particular, we focus on training deep convolutional neural networks (DCNNs) to improve the detection performance of BC. We first develop a novel hybrid algorithm based on artificial bee colony (ABC), biogeography-based optimization (BBO), and particle swarm optimization (PSO) to optimize the architecture of the DCNN, followed by training using a large set of benchmark datasets. To develop the hybrid ABC, the migration operator of the BBO is used to improve the exploitation. Moving towards the global best of PSO is also proposed to improve the exploration of the ABC. Then, we take advantage of the proposed deep architecture to improve the bit-error rate (BER) performance of the studied BC system. The simulation results demonstrate that the proposed algorithm has the best performance in training the benchmark datasets. The results also show that the proposed approach significantly improves the detection performance of backscattered signals compared to existing works.
引用
收藏
页数:21
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共 49 条
[1]   A New Hybrid Multi-Objective Scheduling Model for Hierarchical Hub and Flexible Flow Shop Problems [J].
Aghakhani, Sina ;
Rajabi, Mohammad Sadra .
APPLIEDMATH, 2022, 2 (04) :721-737
[2]   An Ultra-Lightweight Mutual Authentication Scheme for Smart Grid Two-Way Communications [J].
Aghapour, Saeed ;
Kaveh, Masoud ;
Mosavi, Mohammad Reza ;
Martin, Diego .
IEEE ACCESS, 2021, 9 :74562-74573
[3]   An Ultra-Lightweight and Provably Secure Broadcast Authentication Protocol for Smart Grid Communications [J].
Aghapour, Saeed ;
Kaveh, Masoud ;
Martin, Diego ;
Mosavi, Mohammad Reza .
IEEE ACCESS, 2020, 8 :125477-125487
[4]   Utilisation of deep learning for COVID-19 diagnosis [J].
Aslani, S. ;
Jacob, J. .
CLINICAL RADIOLOGY, 2023, 78 (02) :150-157
[5]   A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems [J].
Baniasadi, Sahba ;
Rostami, Omid ;
Martin, Diego ;
Kaveh, Mehrdad .
SENSORS, 2022, 22 (12)
[6]   Reconfigurable Intelligent Surface-Assisted Backscatter Communication: A New Frontier for Enabling 6G IoT Networks [J].
Basharat, Sarah ;
Hassan, Syed Ali ;
Mahmood, Aamir ;
Ding, Zhiguo ;
Gidlund, Mikael .
IEEE WIRELESS COMMUNICATIONS, 2022, 29 (06) :96-103
[7]   IRS Backscatter Enhancing Against Jamming and Eavesdropping Attacks [J].
Cao, Yurui ;
Xu, Sai ;
Liu, Jiajia ;
Kato, Nei .
IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (12) :10740-10751
[8]   Deep learning for power quality [J].
de Oliveira, Roger Alves ;
Bollen, Math H. J. .
ELECTRIC POWER SYSTEMS RESEARCH, 2023, 214
[9]   An efficient modeling attack for breaking the security of XOR-Arbiter PUFs by using the fully connected and long-short term memory [J].
Fard, Sina Soleimani ;
Kaveh, Masoud ;
Mosavi, Mohammad Reza ;
Ko, Seok-Bum .
MICROPROCESSORS AND MICROSYSTEMS, 2022, 94
[10]   Task Offloading Based on Lyapunov Optimization for MEC-assisted Platooning [J].
Hu, Yuyu ;
Cui, Taiping ;
Huang, Xiaoge ;
Chen, Qianbin .
2019 11TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING (WCSP), 2019,