Research On Energy Scheduling For Wireless Powered Communication Networks With Channel Interference

The continuous development in Wireless Power Transfer(WPT)technology has promoted the widespread deployment of Wireless Powered Communication Networks(WPCNs)in various fields,especially in various low-power long-range IoT scenarios.The application of WPCNs not only extends the operating life and range of large-scale IoT devices,but also provides stable and flexible deployment of energy supply.In one WPCNs,one or multiple Hybrid Access Points(HAPs)with constant power supply transfer the energy and receive the status update through wireless to and from a set of distributed sensor nodes.How to improve system performance in WPCNs is a hot research topic,and most of the previous work has been centered on improving the traditional performance metrics of system throughput,Radio Frequency(RF)energy conversion efficiency,and latency.Since many IoT applications increasingly tend to be used to monitor and control the exchange of time-critical information,it is important to keep the information freshness,and outdated information will be worthless even if it is received.Age of Information(AoI)has been proposed to assess the freshness of information received by the destination.Moreover,channel interference plays a crucial role in performance impact of wireless networks,and in addition to packet loss,channel interference reduces the energy transfer efficiency of WPCNs.Earlier work studied scheduling strategies to improve performance primarily under the assumption of ignoring channel interference in energy transfer,thus reducing the realistic utilization.However,with RF energy harvesting,harmful interference can be converted into useful energy through scheduling strategies.To address the objective problems mentioned above,this paper explores how to schedule energy to optimize the information freshness in WPCNs when considering channel interference among adjacent sensor nodes.This motivates us to design a set of experimental studies on channel interference and attempt to gain insight into the unique behaviors of channel interference among adjacent nodes in WPCNs.As a result,our experiments reveal that the channel interference of adjacent nodes can not be neglected in many cases.To take the phenomena into account,we propose an freshness-aware energy scheduling solution to detect channel interference among adjacent nodes and then judiciously determine the energy and time allocation for individual sensor nodes to avoid non-negligible channel interference and improve the AoI performance as well as the system throughput.We further extend the proposed solution to the weighted worst-case AoI,which distinguishes the significance of the data sources in WPCNs for better prioritization strategies and resource allocation.We implement a multi-node WPCNs testbed to validate the functional correctness of the proposed solution,and our extensive experiments have demonstrated the effectiveness of the proposed solution.The experimental results show that the proposed solution can reduce the average AoI by 54.7%and the weighted average AoI by 70.5%,and improve the throughout by 49.8%on average compared to the state-of-the-art solutions.