LAUSR

With the recent development of wireless technologies, a massive number of Internet of Things (IoT) devices will access the Beyond 5G (B5G) networks for various advanced applications. How to support this huge amount of wireless data traffic over limited spectrum resource is a critical problem. To address this problem, dynamic spectrum sharing has been extensively investigated recently to improve the spectrum utilization efficiency for sub-6GHz frequency bands. The secondary users are allowed to access the spatio-temporal spectrum holes of the primary users with the help of a spectrum database. In this paper, we propose a green spectrum sharing framework in B5G era by exploiting crowdsensing. The goal is to construct an accurate crowdsourced spectrum database in a cost-efficient way. Specifically, a machine learning based classifier is used to select the spots that need to be interpolated by crowdsensing users, and both offline and online user incentive mechanisms are proposed to attract users to participate into the crowdsensing task. To validate the proposed framework, we conduct a vehicle-based measurement campaign over a 2km $\times $ 7km area, and evaluate the performance of the proposed framework by using the collected real data.