Machine Learning Approaches for Sharing Unlicensed Millimeter-Wave Bands in Heterogeneously Integrated Sensing and Communication Networks

Due to the increasing demand of high data rate, spectrum scarcity is a key problem for providing unprecedented capacity in diversified applications for future wireless networks. Therefore, the efficiently shared use of unlicensed bands is one of the promising solutions for addressing the spectrum scarcity issue. We study decentralized machine learning approaches using the paradigm of integrated sensing and communication (ISAC) for the shared use of unlicensed millimeter-wave bands. We first present a 5G-WiFi fusion protocol stack for sharing unlicensed millimeter-wave bands, and then design an ISAC-based access protocol and an ISAC based coexistence protocol integrated with decentralized learning function to achieve the efficiently shared use of unlicensed bands. Using the coexistence protocol, we propose promising decentralized machine learning approaches to share unlicensed millimeter-wave bands. Finally, simulations are provided to verify the performance of the proposed scheme, where the results have shown that the proposed scheme greatly reduces the search space of the solution and effectively protects the communication performance of the WiFi system compared to traditional schemes, which indicates that simultaneous transmissions of 5G-U and WiFi at the 60 GHz band are feasible under the proposed scheme.