Cloud-based control of a Wi-Fi network
Systems and methods include obtaining measurements from a Wi-Fi network that includes i) a plurality of access points interconnected to one another and at least one connected to a gateway, and ii) a plurality of Wi-Fi client devices that each connect to an access point of the plurality of access points; learning usage patterns in the Wi-Fi network through analysis with one or more machine learning algorithms; and providing a configuration to the Wi-Fi network based on the learned usage pattern where the configuration includes a topology of the plurality of access points.
1. A non-transitory computer-readable storage medium having computer readable code stored thereon for programming a device to perform steps of:
obtaining, by a remote controller via the Internet, measurements from a Wi-Fi network, the measurements corresponding to a plurality of access points, a gateway, and a plurality of Wi-Fi client devices that each connect to an access point of the plurality of access points;
analyzing, by the remote controller, via execution of at least one machine learning algorithm, the measurements for the plurality of access points, gateway, and plurality of Wi-Fi client devices;
learning, by the remote controller, usage patterns in the Wi-Fi network based on the analysis of the measurements for the plurality of access points, gateway, and plurality of Wi-Fi client devices; and
providing, by the remote controller via the Internet, a configuration to the Wi-Fi network based on the learned usage pattern where the configuration includes at least a frequency selection, the configuration further includes modifications to a topology of the Wi-Fi network to configure backhaul links between the plurality of access points and routes between the plurality of access points, the gateway and the plurality of Wi-Fi client devices.
2. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further comprise:
obtaining measurements from a plurality of networks with statistics regarding behavior of a plurality number of Wi-Fi client devices; and
training the one or more machine learning algorithms with the statistics regarding the behavior of the plurality number of Wi-Fi client devices.
3. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further comprise:
classifying the plurality of Wi-Fi client devices based on their mobility.
4. The non-transitory computer-readable storage medium of claim 3 , wherein the steps further comprise:
predicting associations of mobile Wi-Fi client devices with the plurality of access points such that the configuration is based in part thereon.
5. The non-transitory computer-readable storage medium of claim 3 , wherein the classifying is utilized for a choice of frequency band.
6. The non-transitory computer-readable storage medium of claim 1 , wherein the measurements include time-series data collected both on transmit and receive.
7. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further comprise:
learning what types of Wi-Fi client devices connect to the Wi-Fi network and what types of steering approaches the types of Wi-Fi client devices utilize.
8. The non-transitory computer-readable storage medium of claim 1 , wherein the configuration includes a plurality of channel and bandwidth (BW) selection, routes, Request to Send/Clear to Send (RTS/CTS) settings, Transmitter (TX) power, clear channel assessment thresholds, client association steering, and band steering.
9. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further comprise:
periodically repeating the obtaining, learning, and providing.
10. A method comprising steps of:
obtaining, by a remote controller via the Internet, measurements from a Wi-Fi network, the measurements corresponding to a plurality of access points, a gateway, and a plurality of Wi-Fi client devices that each connect to an access point of the plurality of access points;
analyzing, by the remote controller, via execution of at least one machine learning algorithm, the measurements for the plurality of access points, gateway, and plurality of Wi-Fi client devices;
learning, by the remote controller, usage patterns in the Wi-Fi network based on the analysis of the measurements for the plurality of access points, gateway, and plurality of Wi-Fi client devices; and
providing, by the remote controller via the Internet, a configuration to the Wi-Fi network based on the learned usage pattern where the configuration includes at least a frequency selection, the configuration further includes modifications to a topology of the Wi-Fi network to configure backhaul links between the plurality of access points and routes between the plurality of access points, the gateway and the plurality of Wi-Fi client devices.
11. The method of claim 10 , wherein the steps further comprise:
obtaining measurements from a plurality of networks with statistics regarding behavior of a plurality number of Wi-Fi client devices; and
training the one or more machine learning algorithms with the statistics regarding the behavior of the plurality number of Wi-Fi client devices.
12. The method of claim 10 , wherein the steps further include classifying the plurality of Wi-Fi client devices based on their mobility.
13. The method of claim 12 , wherein the steps further comprise:
predicting associations of mobile Wi-Fi client devices with the plurality of access points such that the configuration is based in part thereon.
14. The method of claim 12 , wherein the classifying is utilized for a choice of frequency band.
15. The method of claim 10 , wherein the measurements include time-series data collected both on transmit and receive.
16. The method of claim 10 , wherein the steps further comprise:
learning what types of Wi-Fi client devices connect to the Wi-Fi network and what types of steering approaches the types of Wi-Fi client devices utilize.
17. The method of claim 10 , wherein the configuration includes a plurality of channel and bandwidth (BW) selection, routes, Request to Send/Clear to Send (RTS/CTS) settings, Transmitter (TX) power, clear channel assessment thresholds, client association steering, and band steering.
18. The method of claim 10 , wherein the steps further comprise:
periodically repeating the obtaining, learning, and providing.