Cooperative proactive resource management for 5G in unlicensed spectrum
We propose a cooperative proactive resource management for 5G in unlicensed spectrum. Unlicensed spectrum can be exploited by legacy systems such as 5G through licensed assisted access (LAA). LAA will be deployed into multiple small base stations (SBSs) providing a large-scale wireless network. Afterwards, a two-stage machine learning (ML) solution will be employed to the SBSs in order to provide learning of the activity in unlicensed spectrum. The ML module will be deployed in the multiple 5G SBSs that will be able to aggregate the unlicensed spectrum bands. The ML will rely on a double Q-learning algorithm that will take into account the interference also in such a wireless network deployment.