Concept Drift Detection Methods for Deep Learning Cognitive Radios: A Hardware Perspective

Abstract

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 misspecification 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.