Abstract:
Spectrum sharing techniques can greatly promote spectrum efficiency and mitigate the congested wireless spectrum. During spectrum sharing, the lower access users, e.g., c...Show MoreMetadata
Abstract:
Spectrum sharing techniques can greatly promote spectrum efficiency and mitigate the congested wireless spectrum. During spectrum sharing, the lower access users, e.g., commercial users, must protect the privilege of the incumbent users, e.g., navy ship radar operators. To achieve this task, environment sensing capability (ESC)-based incumbent detection is a commonly used method, where multiple ESC nodes with a fixed geo-location sense the spectrum and upload the spectrum events to the spectrum access system (SAS). However, existing ESC-based methods may incur a high communication overhead and lead to the leakage of sensitive information, e.g., navy ship route information. To tackle these issues, in this paper, we propose a compressed sensing (CS)-based federated learning framework to achieve incumbent user detection for improving communication efficiency while protecting the privacy of training samples. In particular, the local learning models transmit the updating parameters instead of the raw spectrum data to the central server, and these parameters are aggregated based on a multiple measurement vector (MMV) CS model. The central server can gain a global learning model based on the aggregation of the parameters and get the updating of global parameters back to the local learning models to achieve federated learning. The security analysis and simulation results are provided to validate the effectiveness of the proposed schemes. In the proposed CS-based federated learning framework, the detection performance is as good as the scheme under the raw training samples, and the communication and training efficiency can be significantly improved.
Date of Conference: 29 June 2020 - 01 July 2020
Date Added to IEEE Xplore: 07 August 2020
ISBN Information: