University of Oulu

Khan, Z., Lehtomaki, J., Ganewattha, C., & Shahabuddin, S. (2020, March). Histograms to Quantify Dataset Shift for Spectrum Data Analytics: A SoC Based Device Perspective. 2020 2nd 6G Wireless Summit (6G SUMMIT). 2020 2nd 6G Wireless Summit (6G SUMMIT).

Histograms to quantify dataset shift for spectrum data analytics : a SoC based device perspective

Saved in:
Author: Khan, Zaheer1; Lehtomäki, Janne1; Ganewattha, Chanaka1;
Organizations: 1University of Oulu, Oulu, Finland
2Nokia, Oulu, Finalnd
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-05-29


Cloud/software-based wireless resource controllers have been recently proposed to exploit radio frequency (RF) data analytics for a network control, configuration and management. For efficient resource controller design, tracking the right metrics in real-time (analytics) and making realistic predictions (deep learning) will play an important role to increase its efficiency. This factor becomes particularly critical as radio environments are generally dynamic, and the data sets collected may exhibit shift in distribution over time and/or space. When a trained model is deployed at the controller without taking into account dataset shift, a large amount of prediction errors may take place. This paper quantifies dataset shift in real wireless physical layer data by using a statistical distance method called earth mover’s distance (EMD). It utilizes an FPGA to process in real-time the inphase and quadrature (IQ) samples to obtain useful information, such as histograms of wireless channel utilization (CU). We have prototyped the data processing modules on a Xilinx System on Chip (SoC) board using Vivado, Vivado HLS, SDK and MATLAB tools. The histograms are sent as low-overhead analytics to the resource controller server where they are processed to evaluate dataset shift. The presented results provide insight into dataset shift in real wireless CU data collected over multiple weeks in the University of Oulu using the implemented modules on SoC devices. The results can be used to design approaches that can prevent failures due to datashift in deep learning models for wireless networks.

see all

ISBN: 978-1-7281-6047-4
ISBN Print: 978-1-7281-6048-1
Pages: 1 - 5
Article number: 9083875
DOI: 10.1109/6GSUMMIT49458.2020.9083875
Host publication: 2020 2nd 6G Wireless Summit (6G SUMMIT)
Conference: 6G Wireless Summit
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Copyright information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.