Transparent roaming in virtual access point (VAP) enabled networks
In one embodiment, a supervisory device in a network forms a virtual access point (VAP) for a node in the network. A set of access points (APs) in the network are mapped to the VAP as part of a VAP mapping and the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network. The supervisory device receives measurements from the APs in the VAP mapping regarding communications associated with the node. The supervisory device identifies a movement of the node based on the received measurements from the APs in the VAP mapping. The supervisory device adjusts the set of APs in the VAP mapping based on the identified movement of the node.
1. A method comprising:
forming, by a supervisory device in a network, a virtual access point (VAP) for a node in the network, wherein a set of access points (APs) in the network are mapped to the VAP as part of a VAP mapping, and wherein the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network;
receiving, at the supervisory device, measurements from the APs in the VAP mapping regarding communications associated with the node;
determining, by the supervisory device and based on the measurements from the APs in the VAP mapping indicating reduced signal performance of the communications associated with the node, a predicted future location at a particular time and a trajectory of the node based on the measurements from the APs in the VAP mapping; and
adjusting, by the supervisory device and based on the predicted future and the trajectory location of the node, the set of APs in the VAP mapping to obtain test measurements regarding the communications associated with the node.
2. The method as in claim 1 , wherein the measurements from the APs in the VAP mapping comprise at least one of: a signal strength or signal quality of the communications associated with the node.
3. The method as in claim 1 , further comprising:
computing, by the supervisory device, a time difference of arrival of communications from the node received by the APs in the VAP mapping.
4. The method as in claim 1 , further comprising:
matching, by the supervisory device, the node to a particular user; and
receiving, at the supervisory device, scheduling information regarding the user.
5. The method as in claim 1 , further comprising:
using, by the supervisory device, a first machine learning model to predict the future location of the node based on the measurements received from the APs.
6. The method as in claim 5 , wherein adjusting the set of APs in the VAP mapping comprises:
using, by the supervisory device, a second machine learning model to predict an optimal set of changes to the set of APs in the VAP mapping based on the predicted future location of the node.
7. The method as in claim 6 , further comprising:
receiving, at the supervisory device, a notification that the predicted optimal set of changes to the set of APs in the VAP mapping caused the node to roam; and
retraining, by the supervisory device, the first machine learning model used to predict the predicted future location of the node or the second machine learning model used to predict the optimal set of changes to the set of APs in the VAP mapping, based on the received notification that the changes to the set of APs in the VAP mapping caused the node to roam.
8. The method as in claim 6 , wherein the second machine learning model to predict an optimal set of changes to the set of APs in the VAP mapping further bases the prediction on roaming criteria used by the node to determine when to roam.
9. An apparatus, comprising:
one or more network interfaces to communicate with a network;
a processor coupled to the one or more network interfaces and configured to execute a process; and
a memory configured to store the process executable by the processor, the process when executed configured to:
form a virtual access point (VAP) for a node in the network, wherein a set of access points (APs) in the network are mapped to the VAP as part of a VAP mapping, and wherein the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network;
receive measurements from the APs in the VAP mapping regarding communications associated with the node;
determine, based on the measurements from the APs in the VAP mapping indicating reduced signal performance of the communications associated with the node, a predicted future location at a particular time and a trajectory of the node based on the measurements from the APs in the VAP mapping; and
adjust, based on the predicted future location and the trajectory of the node, the set of APs in the VAP mapping to obtain test measurements regarding the communications associated with the node.
10. The apparatus as in claim 9 , wherein the measurements from the APs in the VAP mapping comprise at least one of: a signal strength or signal quality of the communications associated with the node.
11. The apparatus as in claim 10 , wherein the process when executed is further configured to:
using a machine learning model to predict the predicted future location of the node based on the measurements received from the APs.
12. The apparatus as in claim 11 , wherein the apparatus adjusts the set of APs in the VAP mapping by:
using a second machine learning model to predict an optimal set of changes to the set of APs in the VAP mapping based on the predicted future location of the node.
13. The apparatus as in claim 12 , wherein the process when executed is further configured to:
receive a notification that the predicted optimal set of changes to the set of APs in the VAP mapping caused the node to roam; and
retrain the machine learning model used to predict the predicted future location of the node or the second machine learning model used to predict the optimal set of changes to the set of APs in the VAP mapping, based on the received notification that the changes to the set of APs in the VAP mapping caused the node to roam.
14. The apparatus as in claim 12 , wherein the second machine learning model to predict an optimal set of changes to the set of APs in the VAP mapping further bases the prediction on roaming criteria used by the node to determine when to roam.
15. The apparatus as in claim 9 , wherein the process when executed is further configured to:
compute a time difference of arrival of communications from the node received by the APs in the VAP mapping.
16. The apparatus as in claim 9 , wherein the process when executed is further configured to:
match the node to a particular user; and
receive scheduling information regarding the user.
17. A tangible, non-transitory, computer-readable medium storing program instructions that, when executed by a supervisory device in a network, cause the supervisory device to perform a process comprising:
forming, by the supervisory device, a virtual access point (VAP) for a node in the network, wherein a set of access points (APs) in the network are mapped to the VAP as part of a VAP mapping, and wherein the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network;
receiving, at the supervisory device, measurements from the APs in the VAP mapping regarding communications associated with the node;
determining, by the supervisory device and based on the measurements from the APs in the VAP mapping indicating reduced signal performance of the communications associated with the node, a predicted future location at a particular time and a trajectory of the node based on the measurements from the APs in the VAP mapping; and
adjusting, by the supervisory device and based on the predicted future location and the trajectory of the node, the set of APs in the VAP mapping to obtain test measurements regarding the communications associated with the node.