University of Oulu

S. Shahabuddin, Z. Khan and M. Juntti, "Concept Drift Detection Methods for Deep Learning Cognitive Radios: A Hardware Perspective," 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021, pp. 1-5, doi: 10.1109/ISCAS51556.2021.9401358

Concept drift detection methods for deep learning cognitive radios : a hardware perspective

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Author: Shahabuddin, Shahriar1,2; Khan, Zaheer2; Juntti, Markku2
Organizations: 1Mobile Networks, Nokia, Oulu, Finland
2Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-10-21


Deep learning models usually assume that training dataset and target data have the same distribution. If this is not the case, model mismatch causes performance degradation when the model is used with the real data. With radio frequency (RF) measurements from real data traffic, the exact distribution of the measurements is unknown in many cases and model mismatch is unavoidable. This is known as concept drift, or model mis- specification in deep learning, which we are interested in for cognitive radio dynamic spectrum access predictions. In this paper, we present three concept drift detection methods and their corresponding very large scale integration (VLSI) circuits. The circuits are mapped on a Xilinx Virtex-7 field-programmable gate array (FPGA) and the resource utilization results are provided.

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Series: IEEE International Symposium on Circuits and Systems proceedings
ISSN: 0271-4302
ISSN-E: 2158-1525
ISSN-L: 0271-4302
ISBN Print: 978-1-7281-9201-7
Pages: 1 - 5
Article number: 9401358
DOI: 10.1109/ISCAS51556.2021.9401358
Host publication: 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021
Conference: IEEE International Symposium on Circuits and Systems
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
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