DEMO: Real-time Edge Analytics and Concept Drift Computation for Efficient Deep Learning From Spectrum Data

Cloud managed wireless network resource configuration platforms are being developed for efficient network utilization. These platforms can improve their performance by utilizing real-time edge analytics of key wireless metrics, such as wireless channel utilization (CU). This paper demonstrates a real-time spectrum edge analytics system which utilizes field-programmable gate array (FPGA) to process in real-time hundreds of millions of streaming inphase and quadrature (IQ) samples per second. It computes not only mean and maximum values of CU but also computes histograms to obtain probability distribution of CU values. It sends in real-time these descriptive statistics to an entity which collects these statistics and utilises them to train a deep learning model for prediction of future CU values. Even though utilization in a wireless channel can often exhibit stable seasonal patterns, they can be affected by uncertain usage events, such as sudden increase/decrease in channel usage within a certain time period. Such changes can unpredictably drift concept of CU data (underlying distribution of incoming CU data) over time. In general, concept drift can deteriorate the prediction performance of deep learning models which in turn can impact the performance of cloud managed resource allocation solution. This paper also demonstrates a real-time concept drift computation method which measures the changes in the probability distribution of CU data. Our implemented demonstration includes: 1) spectrum analytics and concept drift computation device which is realized in practical implementation by prototyping it on a low-cost ZedBoard with AD9361 RF transceiver attached to it. ZedBoard is equipped with a Xilinx Zynq-7000 system on chip; 2) a laptop which is connected to the Zedboard and it provides graphical real-time displays of computed CU values, CU histograms, and concept drift computation values. A laptop is also used to develop a deep learning based model for prediction of future CU values. For the INFOCOM we will show a live demonstration of the complete prototyped system in which the device performs real-time computations in an unlicensed frequency channel following the implemented algorithms on the FPGA of a Zedboard.