Dynamic quantized signature vector selection for a cloud radio access network
Abstract:
A communication system is disclosed. The communication system includes a plurality of radio points, each configured to exchange radio frequency (RF) signals with a plurality of wireless devices at a site. The communication system also includes a baseband controller communicatively coupled to the plurality of radio points. The communication system also includes a machine learning computing system communicatively coupled to the baseband controller. The machine learning computing system is configured to determine an expected average throughput associated with each of a plurality of global quantized signature vectors (QSVs), using a Q-function approximation, based on a current state of the communication system. The communication system is also configured to select a global QSV associated with a highest expected average throughput.
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