Distributed deep reinforcement learning with wideband sensing for dynamic spectrum access

Abstract

Wideband spectrum sensing (WBS) has been a critical issue for communication system designers and specialists to monitor and regulate the wireless spectrum. Detecting and identifying the existing signals in a continuous manner enable orchestrating signals through all controllable dimensions and enhancing resource usage efficiency. This paper presents an investigation on the application of deep learning (DL)-based algorithms within the WBS problem while also providing comparisons to the conventional recursive thresholding-based solution. For this purpose, two prominent object detectors, You Only Learn One Representation (YOLOR) and Detectron2, are implemented and fine-tuned to complete these tasks for WBS. The power spectral densities (PSDs) belonging to over-the-air (OTA) collected signals within the wide frequency range are recorded as images that constitute the signal signatures (i.e., the objects of interest) and are fed through the input of the above-mentioned learning and evaluation processes. The main signal types of interest are determined as the cellular and broadcast types (i.e., GSM, UMTS, LTE and Analogue TV) and the single-tone. With a limited amount of captured OTA data, the DL-based approaches YOLOR and Detectron2 are seen to achieve a classification rate of 100% and detection rates of 85% and 69%, respectively, for a nonzero intersection over union threshold. The preliminary results of our investigation clearly show that both object detectors are promising to take on the task of wideband signal detection and identification, especially after an extended data collection campaign.