Commscope Technologies LLC

Naveen Shanmugaraju

Anil Bapat

Balaji B Raghothaman

Paresh Pednekar

Pavani Palukuri

A system includes remote unit(s) (RU(s)). The RU(s) is/are configured to transmit at least one signal on a plurality of different beam patterns, each beam pattern comprising an active subset an inactive subset of a plurality of fine beams covering an area of a cell. The RU(s) is/are also configured to receive, from each of a plurality of user equipment (UEs) in the area, a respective signal strength measurement for each of the plurality of different beam patterns. The system also includes a centralized unit communicatively coupled to the plurality of RUs via a fronthaul interface. The centralized unit is configured to determine a bit vector for each UE based on a combination of the signal strength measurements from the respective UE. The centralized unit is configured also to select a respective fine beam for each UE based on the respective bit vector for the UE.

Beam tracking in a base station

A communication system is disclosed. The communication system includes a plurality of cloud radio access networks (C-RANs), each C-RAN comprising a baseband controller and a plurality of radio points (RPs). Each RP in the plurality of C-RANs is located at a site and is configured to exchange radio frequency (RF) signals with a plurality of wireless devices at the site. The communication system also includes a dynamic sectorization system communicatively coupled to each baseband controller. The dynamic sectorization system is configured to determine at least one signature vector for each of the wireless devices at the site. The dynamic sectorization system also is configured to map each RP in the plurality of C-RANs to one of a number of sectors (K) based on the at least one signature vector for each of the wireless devices at the site.

Dynamic sectorization in a system with multiple cloud radio access networks

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.

Dynamic quantized signature vector selection for a cloud radio access network

A communication system that includes a plurality of radio points is disclosed. Each radio point is configured to exchange radio frequency (RF) signals with a wireless device that transmits a Sounding Reference Signal (SRS) from a first physical location in a site; and extract at least one SRS metric from the SRS. The communication system also includes a baseband controller communicatively coupled to the plurality of radio points. The baseband controller is configured to determine a signature vector based on the at least one SRS metric from each of 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 use a machine learning model to determine location data for the first physical location of the wireless device based on the signature vector.

Location determination with a cloud radio access network

One embodiment is directed to zone-based homing and scheduling techniques for a centralized radio access network (C-RAN) system with a cluster of baseband units.

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Bengaluru, India

Naveen Shanmugaraju