Learning-Based Resource Management for SWIPT

In this article, we consider the joint optimization of transmit power and power splitting ratio to maximize the energy efficiency in a simultaneous wireless information and power transfer based interference channel, in which receivers use a power splitting policy to harvest energy from a wireless signal. We propose an optimization-based iterative algorithm (O-IA) from well-known optimization techniques as a comparative scheme, and also devise a neural network based learning algorithm (NN-LA) to deal with nonconvexity caused by cochannel interference among multiple nodes. Through simulations, we provide a comparative study of the two approaches in terms of energy efficiency and time complexity. In particular, we find that NN-LA achieves a near-optimal energy efficiency, whereas its time complexity is significantly reduced, in comparison with O-IA.

[1]  Kisong Lee,et al.  Proportional Fair Energy-Efficient Resource Allocation in Energy-Harvesting-Based Wireless Networks , 2018, IEEE Systems Journal.

[2]  Rui Zhang,et al.  Optimized Power Allocation for Interference Channel With SWIPT , 2016, IEEE Wireless Communications Letters.

[3]  Jörg Fliege,et al.  Complexity of gradient descent for multiobjective optimization , 2018, Optim. Methods Softw..

[4]  Kostas Pentikousis,et al.  In search of energy-efficient mobile networking , 2010, IEEE Communications Magazine.

[5]  Xiaodong Wang,et al.  Coordinated Scheduling and Power Allocation in Downlink Multicell OFDMA Networks , 2009, IEEE Transactions on Vehicular Technology.

[6]  Woongsup Lee,et al.  Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication , 2018, IEEE Wireless Communications Letters.

[7]  Rui Zhang,et al.  Wireless Information and Power Transfer: Architecture Design and Rate-Energy Tradeoff , 2012, IEEE Transactions on Communications.

[8]  He Chen,et al.  Distributed Power Splitting for SWIPT in Relay Interference Channels Using Game Theory , 2014, IEEE Transactions on Wireless Communications.

[9]  Woongsup Lee,et al.  Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network , 2018, IEEE Communications Letters.

[10]  Robert Sedgewick,et al.  Computer Science - An Interdisciplinary Approach , 2016 .

[11]  Zhu Han,et al.  Wireless Networks With RF Energy Harvesting: A Contemporary Survey , 2014, IEEE Communications Surveys & Tutorials.

[12]  Kee Chaing Chua,et al.  Wireless Information and Power Transfer: A Dynamic Power Splitting Approach , 2013, IEEE Transactions on Communications.