Adaptive pursuit learning method to mitigate small-cell interference through directionality
A learning protocol for distributed antenna state selection in directional cognitive small-cell networks is described. Antenna state selection is formulated as a nonstationary multi-armed bandit problem and an effective solution is provided based on the adaptive pursuit method from reinforcement learning. A cognitive small cell testbed, called WARP-TDMAC, provides a useful software-defined radio package to explore the usefulness of compact, electronically reconfigurable antennas in dense small-cell configurations. A practical implementation of the adaptive pursuit method provides a robust distributed antenna state selection protocol for cognitive small-cell networks. Test results confirm that directionality provides significant advantages over omnidirectional transmission which suffers high throughput reduction and complete link outages at above-average jamming or cross-link interference power.
1. A method for avoiding interference for a communication link, comprising:
synchronizing a common time base between a transmitting node and a receiving node, wherein the transmitting node and the receiving node receive and transmit using directional antennas;
based on the synchronizing of the common time base, sending a link schedule to a client of the transmitting node, wherein the link schedule is for scheduling concurrent directional channel access;
responsive to the sending of the link schedule to the client, receiving an acknowledgment message from the client, wherein the acknowledgment message comprises pursuit statistics of the client, wherein the pursuit statistics comprise a reward metric or selection count; and
coordinating directional transmissions between the transmitting node and the receiving node based on the acknowledgement message.
2. The method of claim 1 , wherein the transmitter node and the receiver node are access points.
3. The method of claim 1 , wherein the coordinating comprises delivering link schedules via beacon control packets.
4. The method of claim 1 , wherein the link schedule is synchronously broadcast to the client of the transmitter node and a client of the receiver node.
5. The method of claim 1 , wherein the transmitter node has transmitter antenna or a receiver antenna that is a pattern-reconfigurable antenna.
6. The method of claim 1 , wherein the reward metric comprises a packet delivery ratio.
7. The method of claim 1 , wherein the coordinating comprises using a distributed antenna orientation algorithm based on machine learning to improve individual goodput of a communication link of the wireless network.
8. A computer readable storage medium that is not a signal storing computer executable instructions that when executed by a computing device cause said computing device to effectuate operations comprising:
synchronizing a common time base between a transmitting node and a receiving node, wherein the transmitting node and receiving node receive and transmit using directional antennas;
based on the synchronizing of the common time base, sending a link schedule to a client of the transmitting node, wherein the link schedule is for scheduling concurrent directional channel access;
responsive to the sending of the link schedule to the client, receiving an acknowledgment message from the client, wherein the acknowledgment message comprises downlink pursuit statistics of the client, wherein the pursuit statistics comprise a reward metric or selection count; and
coordinating directional transmissions between the transmitting node and the receiving node based on the acknowledgement message.
9. The computer readable storage medium of claim 8 , wherein the transmitter node and the receiver node are access points.
10. The computer readable storage medium of claim 8 , wherein the coordinating comprises delivering link schedules via beacon control packets.
11. The computer readable storage medium of claim 8 , wherein the link schedule is synchronously broadcast to the client of the transmitter node and a client of the receiver node.
12. The computer readable storage medium of claim 8 , wherein the transmitter node has transmitter antenna or a receiver antenna that is a pattern-reconfigurable antenna.
13. The computer readable storage medium of claim 8 , wherein the reward metric comprises a packet delivery ratio.
14. The computer readable storage medium of claim 8 , wherein the coordinating comprises using a distributed antenna orientation algorithm based on machine learning to improve individual goodput of a communication link of the wireless network.
15. An apparatus comprising:
a processor; and
a memory coupled with the processor, the memory storing executable instructions that when executed by the processor cause the processor to effectuate operations comprising:
synchronizing a common time base between a transmitting node and a receiving node, wherein the transmitting node and receiving node receive and transmit using directional antennas;
based on the synchronizing of the common time base, sending a link schedule to a client of the transmitting node, wherein the link schedule is for scheduling concurrent directional channel access;
responsive to the sending of the link schedule to the client, receiving an acknowledgment message from the client, wherein the acknowledgment message comprises downlink pursuit statistics of the client, wherein the pursuit statistics comprise a reward metric or selection count; and
coordinating directional transmissions between the transmitting node and the receiving node based on the acknowledgement message.
16. The apparatus of claim 15 , wherein the transmitter node and the receiver node are access points.
17. The apparatus of claim 15 , wherein the coordinating comprises delivering link schedules via beacon control packets.
18. The apparatus of claim 15 , wherein the link schedule is synchronously broadcast to the client of the transmitter node and a client of the receiver node.
19. The apparatus of claim 15 , wherein the transmitter node has a transmitter antenna or a receiver antenna that is a pattern-reconfigurable antenna.
20. The apparatus of claim 15 , wherein the coordinating comprises using a distributed antenna orientation algorithm based on machine learning to improve individual goodput of a communication link of the wireless network.