Interference and mobility management in UAV-assisted wireless networks

• US 9,986,440 B2
• Filed: 02/27/2017
• Issued: 05/29/2018
• Est. Priority Date: 10/23/2015
• Status: Active Grant

• Current Assignee: The Florida International University Board of Trustees (State University System of Florida)
• Original Assignee: The Florida International University Board of Trustees (State University System of Florida)
• Inventors: Guvenc, Ismail
• Primary Examiner(s): Doan, Kiet
• Application Number:
  US15/443,147
• Publication Number:
  US 20170171761A1
• Time in Patent Office: 456 Days
• Field of Search: 4554561, 4554041, 455408, 455411, 4554521, 455416, 4554221, 455441, 455458, 455423, 34087002, 370237, 706 15
• CPC Class Codes: H04B 7/18504 Aircraft used as relay or high altitude atmospheric platform
• CPC Class Codes: H04B 7/18506 Communications with or from aircraft, i.e. aeronautical mobile service
• CPC Class Codes: H04W 16/14 Spectrum sharing arrangements between different networks
• CPC Class Codes: H04W 16/22 Traffic simulation tools or models
• CPC Class Codes: H04W 16/28 using beam steering
• CPC Class Codes: H04W 36/20 for optimising the interference level
• CPC Class Codes: H04W 36/32 by location or mobility data, e.g. speed data
• CPC Class Codes: H04W 36/322 by location data
• CPC Class Codes: H04W 36/324 by mobility data, e.g. speed data
• CPC Class Codes: H04W 4/90 Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
• CPC Class Codes: H04W 76/15 Setup of multiple wireless link connections
• CPC Class Codes: H04W 84/042 Public Land Mobile systems, e.g. cellular systems

Abstract

Techniques and systems are disclosed for addressing the challenges in interference and mobility management in broadband, UAV-assisted heterogeneous network (BAHN) scenarios. Implementations include BAHN control components, for example, at a controlling network node of a BAHN. Generally, a component implementing techniques for managing interference and handover in a BAHN gathers state data from network nodes or devices in the BAHN, determines a candidate BAHN model that optimizes interference and handover metrics, and determines and performs model adjustments to the network parameters, BS parameters, and UAV-assisted base station (UABS) device locations and velocities to conform to the optimized candidate BAHN model. Also described is a UABS apparatus having a UAV, communications interface for communicating with a HetNet in accordance with wireless air interface standards, and a computing device suitable for implementing BAHN control or reinforcement learning components.

Claims

1. A method of managing interference and handover in a broadband, UAV-assisted heterogeneous network (BAHN), the BAHN comprising a plurality of base station devices and a master macrocell base station (MBS) device that is a base station control node of the BAHN, the method comprising:

determining, by the master MBS device, a candidate BAHN model from state data received from the base station devices of the BAHN, wherein the candidate BAHN model satisfies one or more factors including an interference metric, a load-balancing metric, a handover failure metric, a ping-pong probability metric, and a network throughput metric, wherein the base station devices include one or more of unmanned aerial base station (UABS) devices, macrocell base station (MBS) devices, small cell base station (SCBS) devices, and Cloud-RAN (C-RAN) base station devices;

capturing a random access channel (RACH) message, decoding the RACH message, and providing a real-time heat-map based on the RACH message;

depending on the candidate BAHN model, selecting, by the master MBS device, at least three model adjustments from a group consisting of;

a) modifying a duty cycle or pattern of reduced-power resources;

b) modifying a power reduction factor at the reduced-power resources;

c) altering a range-expansion bias at one or more SCBS and UABS;

d) modifying a scheduling threshold for transitioning user equipment devices to a different base station;

e) modifying an operating state of UABS features; and

f) aggregating a plurality of component carrier bands using a carrier aggregation feature of LTE;

performing, by the master MBS device, the at least three model adjustments; and

organizing mobility management based on at least one of a three-dimensional location, a velocity, and a mobility management parameter.

2. The method of claim 1, wherein the plurality of component carrier bands comprise unlicensed bands,

wherein one or more of LTE-LAA and LTE-U technologies utilize the unlicensed bands, and

wherein at least one of the user equipment devices are simultaneously connected to a plurality of base station devices using one or more communication bands.

3. The method of claim 1, wherein one or more of the user equipment devices connect to the base station devices using a millimeter wave band.

4. The method of claim 1, wherein the determining of the candidate BAHN model comprises determining an optimized base station location distribution in the BAHN with a Poisson hardcore process, a mattern-cluster process, a Gibbs point process, or a Strauss point process.

5. The method of claim 1, wherein the BAHN comprises a public safety communications system,

wherein determining the candidate BAHN model comprises applying different quality of service thresholds at first responder user equipment, victim user equipment, and regular user equipment, and

wherein the base station devices exchange the state data using a backhaul channel.

6. The method of claim 5, wherein the backhaul channel is transmitted from a secondary base station device, and wherein the secondary base station device is not the origin of the state data.

7. The method of claim 1, wherein specific UABS devices in the BAHN comprise a planar antenna array, and wherein modifying the operating state of UABS features comprises enabling a 3-D beamforming capability using the planar antenna array in at least one of the specific UABS devices.

8. The method of claim 1, wherein the mobility management parameter is one or more of:

a hysteresis margin;

a range expansion bias; and

a time-to-trigger timer.

9. The method of claim 1, wherein selecting the at least one model adjustment comprises an autonomous adjustment determined by a reinforcement learning component of the base station devices.

10. The method of claim 1, wherein the master MBS device selects and performs all of model adjustments a), b), c), d), e), and f).

11. An unmanned aerial base station (UABS) apparatus comprising:

an unmanned aerial vehicle (UAV);

a communications interface for communicating with a heterogeneous wireless network in accordance with wireless air interface standards, the heterogeneous wireless network including a plurality of base station devices and a master macrocell base station (MBS) device that is a base station control node of the heterogeneous wireless network;

a processing system;

one or more computer readable storage media; and

program instructions stored on the one or more computer readable storage media that, when executed by the processing system, direct the processing system to;

capture a random access channel (RACH) message, decode the RACH message, and provide a real-time heat-map based on the RACH message,

select, by the master MBS device, at least three model adjustments from a group consisting of;

a) modifying a duty cycle, pattern, or power reduction factor of a reduced-power resource of the communications interface;

b) modifying an operating state of the communications interface;

c) altering a range-expansion bias of the communications interface;

d) modifying a scheduling threshold for transitioning user equipment devices to a different base station;

e) directing a control module of the UAV to modify a three-dimensional location or a velocity of the UAV; and

f) modifying a mobility management parameter at a connected user equipment;

perform, by the master MBS device, the at least three model adjustments; and

organize mobility management based on at least one of the three-dimensional location, the velocity, and the mobility management parameter.

12. The apparatus of claim 11, further comprising program instructions that, when executed by the processing system, direct the processing system to receive random access channel (RACH) signals of user equipment devices attaching to the wireless network and report the signals to the base station control node.

13. The apparatus of claim 11, further comprising program instructions that, when executed by the processing system, direct the processing system to, in response to determining that state data cannot be shared with other base station devices of the heterogeneous wireless network, use a reinforcement learning component to determine an autonomous model adjustment.

14. The apparatus of claim 11, wherein, when executed by the processing system, the program instructions direct the processing system to select and perform, by the mater MBS device, all of model adjustments a), b), c), d), e), and f).

15. A system for managing interference and handover in a broadband, UAV-assisted heterogeneous network (BAHN), the BAHN including a plurality of base station devices and a master macrocell base station (MBS) device that is a base station control node of the BAHN, the system comprising:

one or more computer readable storage media;

a processing system; and

program instructions stored on the one or more computer readable storage media that, when executed by the processing system, direct the processing system to;

receive state data from the base station devices of the BAHN, wherein the base station devices include unmanned aerial base station (UABS) devices, macrocell base station (MBS) devices, small cell base station (SCBS) devices, and Cloud-RAN (C-RAN) base station devices;

determine, by the master MBS device, a candidate BAHN model from the state data received from the base station devices of the BAHN, wherein the candidate BAHN model satisfies the factors including an interference metric, a load-balancing metric, a handover failure metric, a ping-pong probability metric, and a network throughput metric; and

capture a random access channel (RACH) message, decode the RACH message, and provide a real-time heat-map based on the RACH message;

depending on the candidate BAHN model, select, by the master MBS device, at least three model adjustments from a group consisting of;

a) modifying a duty cycle or pattern of reduced-power resources;

b) modifying a power reduction factor at the reduced-power resources;

c) altering a range-expansion bias at one or more SCBS and UABS;

d) modifying a scheduling threshold for transitioning user equipment devices to a different base station;

e) modifying a three-dimensional location or a velocity of at least one UABS;

f) modifying a mobility management parameter at a selected user equipment;

g) modifying an operating state of UABS features; and

h) aggregating a plurality of component carrier bands using a carrier aggregation feature of LTE;

performing, by the master MBS device, the at least three model adjustments; and

organize mobility management based on at least one of the three-dimensional location, the velocity, and the mobility management parameter.

16. The system of claim 15, wherein the determining of the candidate BAHN model comprises determining an optimized base station location distribution in the BAHN with a Poisson hardcore process, a mattern-cluster process, a Gibbs point process, or a Strauss point process.

17. The system of claim 15, wherein specific UABS devices in the BAHN comprise a planar antenna array, and wherein modifying the operating state of UABS features comprises enabling a 3-D beamforming capability using the planar antenna array in at least one of the specific UABS devices.

18. The system of claim 15, wherein the mobility management parameter is one or more of:

a hysteresis margin;

a range expansion bias; and

a time-to-trigger timer.

19. The system of claim 15, wherein selecting the model adjustment comprises an autonomous adjustment determined by a reinforcement learning component of the base station devices.

20. The system of claim 15, wherein, when executed by the processing system, the program instructions direct the processing system to select and perform, by the mater MBS device, all of model adjustments a), b), c), d), e), f), g), and h).