A Coordinated Ambient/Dedicated Radio Frequency Energy Harvesting Scheme Using Machine Learning

This paper proposes a coordinated ambient/ dedicated (CA/D) protocol where backscatter-enabled combination sensors are optimized to harvest energy from intended radio frequency (RF) sources when available and fall back to harvesting energy from unintended sources when only unintended sources are available. The CA/D protocol can use either of the two new machine learning techniques proposed, the linear forecaster with near-time linear regression-based enhancer (LFNTLRE) algorithm and an artificial neural network (ANN), to determine the optimum energy harvesting (EH) schedule. These machine learning algorithms can reliably operate in environments where there is unpredictable availability of unintended sources and ongoing changes in channel conditions between the sensors and the unintended sources. Numerical results show that sensors using the ANN and LFNTLRE algorithm can achieve up to 99.5% and 99.6% hit count relative to that of ideal sensors, respectively, compared to 100% by an ideal sensor. Sensors using the ANN and LFNTLRE algorithm have an accuracy percentage of up to 99% and 100%, respectively, as well. Ensuring reliable operation of RF EH sensors in all environments will enable its future widespread adoption in the real world.

[1]  Abraham O. Fapojuwo,et al.  Radio Frequency Energy Harvesting and Data Rate Optimization in Wireless Information and Power Transfer Sensor Networks , 2017, IEEE Sensors Journal.

[2]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[3]  L. Decreusefond,et al.  Efficient simulation of the Ginibre point process , 2015 .

[4]  Mérouane Debbah,et al.  Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach , 2019, IEEE Transactions on Cognitive Communications and Networking.

[5]  Hyungsik Ju,et al.  Throughput Maximization in Wireless Powered Communication Networks , 2013, IEEE Trans. Wirel. Commun..

[6]  Charu C. Aggarwal,et al.  Neural Networks and Deep Learning , 2018, Springer International Publishing.

[7]  Abraham O. Fapojuwo,et al.  Performance Optimization of a Multi-Source, Multi-Sensor Beamforming Wireless Powered Communication Network With Backscatter , 2019, IEEE Sensors Journal.

[8]  Hao Yu,et al.  Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[9]  P. M. Shankar.  Propagation Characteristics of Wireless Channels , 2004 .

[10]  Miao He,et al.  Design of a High-Efficiency 2.45-GHz Rectenna for Low-Input-Power Energy Harvesting , 2012, IEEE Antennas and Wireless Propagation Letters.

[11]  Abraham O. Fapojuwo,et al.  Measurement and Analysis of Available Ambient Radio Frequency Energy for Wireless Energy Harvesting , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[12]  Swades De,et al.  Smart RF energy harvesting communications: challenges and opportunities , 2015, IEEE Communications Magazine.

[13]  Paschalis C. Sofotasios,et al.  Opportunistic Ambient Backscatter Communication in RF-Powered Cognitive Radio Networks , 2019, IEEE Transactions on Cognitive Communications and Networking.

[14]  Wuyang Zhou,et al.  The Ginibre Point Process as a Model for Wireless Networks With Repulsion , 2014, IEEE Transactions on Wireless Communications.

[15]  A. Hall.  The Method of Least Squares , 1872, Nature.

[16]  Hai Jiang,et al.  Sensing, Probing, and Transmitting Policy for Energy Harvesting Cognitive Radio With Two-Stage After-State Reinforcement Learning , 2019, IEEE Transactions on Vehicular Technology.

[17]  Jacques Palicot,et al.  Smart decision making policy for faster harvesting from ambient RF sources in wireless sensor nodes , 2016, 2016 International Symposium on Wireless Communication Systems (ISWCS).

[18]  Abraham O. Fapojuwo,et al.  Sum-Throughput Maximization in Wireless Sensor Networks With Radio Frequency Energy Harvesting and Backscatter Communication , 2018, IEEE Sensors Journal.

[19]  Shuguang Cui,et al.  Reinforcement Learning-Based Multiaccess Control and Battery Prediction With Energy Harvesting in IoT Systems , 2018, IEEE Internet of Things Journal.

[20]  Themistoklis Charalambous,et al.  Optimal Radio Frequency Energy Harvesting With Limited Energy Arrival Knowledge , 2015, IEEE Journal on Selected Areas in Communications.

[21]  P. D. Mitcheson,et al.  Ambient RF Energy Harvesting in Urban and Semi-Urban Environments , 2013, IEEE Transactions on Microwave Theory and Techniques.

[22]  Hervé Abdi.  The Method of Least Squares , 2007 .