Netgear, Inc.

Chunyi Guo

Deeksha Kamath

Yu Te Lin

Fernando Lozano

A method includes obtaining a set of wireless statistics of a wireless communication device and a set of hardware-related information of the wireless communication device. The method further includes determining a set of test results of a testbed system based on the set of wireless statistics and the set of hardware-related information by configuring the testbed system based on the set of wireless statistics and the set of hardware-related information. The method further includes updating a set of decision parameters of a wireless configuration decision model using a machine learning model based on the set of test results. The method further includes sending the updated set of decision parameters to the wireless communication device, wherein the wireless communication device configures a wireless configuration parameter based on an instance of the wireless configuration decision model configured with the updated set of decision parameters.

Adaptive machine learning for dynamic wireless communication configuration

Apparatuses, methods, and systems for client steering in mesh networks are disclosed. A first access point (AP) of a network sends a quality of service (QoS) data packet to a client device. A received signal strength indication (RSSI) of the client device is captured using the QoS data packet. A distance between the AP and client device is determined using a Wi-Fi round trip time. A time and day of week is determined. Using machine learning, a second AP is identified for steering the client device to, based on the RSSI, the distance, and the time and day of week. A machine learning model is trained to steer each client device to a respective AP for increasing a throughput of the network based on features extracted from client behavior of the client devices. The second AP is connected to the client device.

Growing community of inventors

San Jose, CA, United States of America

Chunyi Guo