UAV Cellular Communications: Practical Insights and Future Vision
G. Geraci, A. Garcia-Rodriguez, M. Hassan, and M. Ding
IEEE Globecom
Abu Dhabi, UAE
13 December 2018
2
Tutorial Outline
• Part I (1h30m)
– UAV users: An operator’s perspective
– 3GPP standardization work
– Performance in existing and future networks
• Part II (1h30m)
– UAV base stations
– Societal impact of UAVs
– Future directions
UAV Cellular Communications: Practical Insights and Future Vision
Part I
G. Geraci, A. Garcia-Rodriguez, M. Hassan, and M. Ding
IEEE Globecom
Abu Dhabi, UAE
13 December 2018
This presentation includes BellLabs research ideas/work andthere is no commitment by thebusiness divisions of Nokia tosupport them. The viewsexpressed in this tutorial aresolely the authors' and do notnecessarily reflect those ofNokia or the European Union.
4
Part I – Outline
• UAV users: An operator’s perspective
• 3GPP standardization work
• Performance in existing and future networks
• Summary
5
The speakers
6
Background
• Junior Leader FellowUniversitat Pompeu Fabra, Spain (2018 – present)
• Research ScientistNokia Bell Labs, Ireland (2016 – 2018)
• PostDoc at SUTD, Singapore (2014 – 2015)
• PhD from UNSW, Australia (2014)
About me
• Born in Sicily, at the heart of the Mediterranean
Giovanni Geraci
7
Background
• Research Scientist at Nokia Bell Labs (2016-present)
• PhD from University College London, UK (2016)
About me
• Born in Tenerife (Canary Islands), at the edge of the Atlantic
Adrian Garcia-Rodriguez
8
Funding:
• “la Caixa” Banking Foundation
• This project has received funding from the EuropeanUnion’s Horizon 2020 research and innovationprogramme under the Marie Sklodowska-Curie grantagreement No 765224.
Acknowledgments
9
Collaborators:
• D. Lopez-Perez and L. Galati Giordano (Nokia Bell Labs, Ireland)
• I. Kovács, J. Wigard, P. Mogensen, R. Amorim, and H. Nguyen (Nokia Bell Labs, Denmark)
• E. Björnson (Linköping University, Sweden)
Acknowledgments
UAV users:An operator’s perspective
11
UAV Communications – Why are they important?
12
UAV Communications – Why are they important?
13
An operator’s perspective
The pursuit of revenue
• Handle UAV-generated payload traffic: similar to current data traffic.
• Handle BVLoS, automated UAV command & control: new markets and opportunities.
The dream
• Seamlessly reuse existing, or soon-to-be-deployed, network infrastructure.
The reality
• Must prepare the ground to accommodateboth terrestrial and aerial users.
?
14
An operator’s perspective
Another dream
• Employ UAVs as mobile base stations (BSs).
• Enhance network performance through dynamic, on-demand BS repositioning.
15
An operator’s perspective
Another dream
• Employ UAVs as mobile base stations (BSs).
• Enhance network performance through dynamic, on-demand BS repositioning.
Reality, again
• Identify optimal UAV locations.
• Limited UAV flying times, regulations.
• Wireless backhaul.
• Provide reliable control to UAVs.
3GPP standardization work
17
Outline:
• Standardization timeline
• Study item: Enhanced LTE support for aerial vehicles
• Work item: Enhanced LTE support for aerial vehicles
• Way forward
18
Standardization timeline
19
1. Study item definition
LTE3GPP Rel. 15
Standardization timeline
2. Study item completion
3. Work item definition
4. Work item completion
March 2017
Dec. 2017 Sept. 2018
NR?3GPP Rel. 17
5. Work item proposals
LTE?3GPP Rel. 17
20
Study Item:Enhanced LTE support for aerial vehicles
21
Enhanced LTE support for aerial vehicles
Study item timeline:
1. UAV traffic requirements definition
2. Channel modelling
3. Performance analysis
4. Enhancing UAV communications
22
UAV traffic requirements definition
1. Command & control link
– Both in uplink and downlink directions:
• Latency requirements: 50 ms.
• Throughput requirements: 60 – 100 kbps.
• Reliability: Up to 10−3 packet error loss rate.
2. Application data
– Mostly in uplink direction for video streaming applications
• Latency requirements: similar to those for ground UEs.
• Throughput requirements: Up to 50 Mbps.
23
Channel modelling
1. UAV deployment
– Uniform height deployment between 0 and 300 meters.
– UAV travel speeds of {3, 30, 60, 160} km/h.
2. 3GPP 3D channel model update (TR 38.901)
– 3D channel directionality.
– Spatially correlated shadow fading.
– Space-time-frequency-correlated fast fading.
– Dependence of all propagation parameters (pathloss,probability of LoS, shadow fading, fast fading, …) ontransmitter and receiver heights.
24
UAV communications: Performance analysis
Key observations:
1. UAVs are likely to generate harmfuluplink interference to ground UEs.
– UAVs flying high experience good propagationconditions with many ground BSs.
2. UAVs are likely to perceive interferencefrom a large number of ground BSs.
– UAVs flying at 100 meters can receive signalsfrom BSs located 10 km away!
3. UAV experience a degraded mobilityperformance.
– Handover interruption time and failures areincreased w.r.t. ground UEs.
25
Enhancing UAV communications
Addressing UAV-related issues:
1. Uplink interference generation:
– Uplink power control.
– Full-dimension MIMO and multi-antenna UAVs.
2. Downlink interference reception:
– Full-dimension MIMO and multi-antenna UAVs.
– Cooperative multipoint (CoMP).
3. Mobility
– Exploit UAV flight information.
• Flying UE status identification as a meansto enhance all of the above.
26
Enhancing UAV communications
Addressing UAV-related issues:
1. Uplink interference generation:
– Uplink power control.
– Full-dimension MIMO and multi-antenna UAVs.
2. Downlink interference reception:
– Full-dimension MIMO and multi-antenna UAVs.
– Cooperative multipoint (CoMP).
3. Mobility
– Exploit UAV flight information.
• Flying UE status identification as a meansto enhance all of the above.
Conclusion:
• LTE cellular operators should becapable of satisfactorily serving UAVsas long as
– the network is not severely loaded, and
– the number of airborne devices is limited.
• There could be challenges in themanagement of uplink and downlinkinterference as well as mobility.
27
Work Item:Enhanced LTE support for aerial vehicles
28
Work item outcomes
1. Uplink power control
– Existing fractional power control mechanisms are enhanced through:
• the assignment of UAV-specific path loss compensation factor 𝛼, and
• the range extension of the 𝑃0 parameter.
2. Signalling
– Trigger new reporting events when the UAV altitude is above or below a BS-configurablethreshold.
3. Interference detection
– UEs can trigger a measurement report when the reference signals received from aconfigurable number of neighboring cells satisfy specific conditions.
Maximum UE power [W]
Average channel gain between the k-th UE and its serving BS
29
Work item outcomes
4. Subscription-based access
– To prevent the non-authorized cellular connectionof UAVs through new network signalling from thecore network to the cellular BS.
5. Mobility
– Included new radio resource control (RRC)signalling to facilitate the flight plancommunication from UAVs to their serving BS.
30
Way forward
31
Where is the 3GPP heading? [October 2018]
Technical Specification Group (TSG) Service and System Aspects (SA) studies
1. Remote identification of unmanned aerial systems (Rel.17?)
• Target: Document UAV identification and tracking requirements that allow authorised bodies(e.g., traffic control) to determine the UAV identity and its controller.
2. Study on enhancement for unmanned aerial vehicles (Rel. 17?)
• Target: Set new requirements and KPIs for industrial and public applications with UAVs.
New work item proposals
1. Further enhanced LTE support for aerial vehicles
2. New Radio (NR) support for aerial vehicles
32
Further enhanced LTE support for aerial vehicles
• Status:
– Work item not formally defined yet: Companies arecurrently presenting their views.
• Tentative objectives:
– Reducing the number of handovers and the handoverfailure rate (e.g., conditional handover).
– Implementing uplink interference mitigation techniquesfor random access, CRS, and downlink control channels.
– Enhancing flight path plan signalling (e.g., allowing UAVreporting without network polling).
– Enabling a faster tuning of the UAV radio resourcemanaging (RRM) configuration.
33
New Radio support for aerial vehicles
• Status:
– Work item not formally defined yet: Companies arecurrently presenting their views.
• Tentative objectives:
– Extend the LTE-based studies:
• Updated performance requirements, e.g., those for relaying.
• High-altitude UAVs (e.g., up to 1 Km).
• More antennas at the BS and UE sides.
• Network slicing.
• UE-specific power configuration.
– Specification of the NextGen (NG) signalling support foraerial UE subscription-based identification.
34
References
35
References
Standardization
[1] 3GPP Technical Report 36.777, “Technical specification group radio access network; Study onenhanced LTE support for aerial vehicles (Release 15),” Dec. 2017.
[2] 3GPP Technical Document RP 181644, “Summary for WI Enhanced LTE Support for AerialVehicles,” Sept. 2018.
[3] 3GPP Technical Specification 36.331, “Evolved Universal Terrestrial Radio Access (E-UTRA); RadioResource Control (RRC); Protocol specification,” Sept. 2018.
[4] 3GPP Technical Document RP 181046 , “New WID on Further Enhanced LTE Support for AerialVehicles,” Sept. 2018.
[5] 3GPP Technical Document RP 180369 , “New WI proposal: Further Enhanced LTE Support forAerial Vehicles,” Sept. 2018.
[6] 3GPP Technical Document RP 181070, “WID on NR Support for Aerial Vehicles,” Sept. 2018.
[7] 3GPP Technical Document RP 180359, “WID on NR Support for Aerial Vehicles,” Sept. 2018.
36
References
Video resources
1. Drone control over public LTE
– R. Amorim, H. Nguyen, P. Mogensen, I. Z. Kovács, J. Wigard and T. B. Sørensen, "RadioChannel Modeling for UAV Communication Over Cellular Networks," in IEEE WirelessCommunications Letters, vol. 6, no. 4, pp. 514-517, Aug. 2017.
– H. C. Nguyen, R. Amorim, J. Wigard, I. Z. Kovács, T. B. Sørensen and P. E. Mogensen, "Howto Ensure Reliable Connectivity for Aerial Vehicles Over Cellular Networks," in IEEE Access,vol. 6, pp. 12304-12317, 2018.
– I. Kovacs, R. Amorim, H. C. Nguyen, J. Wigard and P. Mogensen, "Interference Analysis forUAV Connectivity over LTE Using Aerial Radio Measurements," 2017 IEEE 86th VehicularTechnology Conference (VTC-Fall), Toronto, ON, 2017, pp. 1-6.
2. Faster search & rescue with Nokia drone networks
Performance in existing and future networks
38
Outline:
• System evaluation set-up
• UAV cell selection
• Downlink performance (C&C)
• Uplink performance (C&C + video streaming)
• Essential guidelines
• References
39
System evaluation set-up
40
System-level simulation platform3GPP TR 36.777 – Enhanced LTE support for aerial vehicles (Dec. 2017)
• Multi-user MIMO framework accounting for:
- Digital and/or analog precoding
- UL SRS allocation for realistic CSI acquisition
- User scheduling (multiple options available)
• 3GPP 3D (TR 38.901) channel model accounting for:
- 3D channel directionality
- Spatially correlated shadow fading
- Space-time-frequency-correlated fast fading
- Dependence of all propagation parameters (pathloss, probability of LoS, shadow fading, fast fading, …) on transmitter and receiver heights
- Directional antenna patterns at both BSs and UEs (if needed)
• Network deployment accounting for:
- Wrapped-around sectorized BS layout(variable downtilt, ISD, antenna array size, …)
- UAVs of various heights
- Ground UEs: both outdoor and indoor(in buildings of various #floors)
- Variable UAV/GUE ratio as per 3GPP cases
41
System parameters
42
UAV cell selection
43
UAV associationExisting cellular networks are optimized for ground users
44
UAV associationExisting cellular networks are optimized for ground users
45
UAV associationHigh UAVs select cells located far away
• Take the perspective of a 3-sector BS at the origin
• Red dots show 2D locations of associated UAVs (@150m)
• Association distance ranges exist (green-colored areas), corresponding to the side lobes
46
Downlink performance:Command & control
47
ScenariosSingle-user vs Massive MIMO paradigms
1. Single-user mode (SU):
• 8x1 array X-POL, one RF chain, one user per physical resource block (PRB)
2. Massive MIMO mode (mMIMO):
• 8x8 array X-POL, 128 RF chains, spatially multiplexing several users per PRB
• ZF precoding/combining, realistic CSI acquisition with pilot reuse 3
48
UAV performance in single-user mode
For UAVs beyond 150m, even the 5%-best SINR per PRB falls below the minimum MCS threshold
For high UAVs, C&C target rate of 100kbps can only be achieved for a small percentage of cases:<40% at 50m-75m, <2% at 150m+
49
UAV performance w multi-user massive MIMO(perfect CSI)
SINRs are largely improved due to:(i) beamforming gain from serving BS, and (ii) most interfering beams pointed downwards
C&C target rate of 100 kbps achieved in at least 96% of the cases for all UAV heights
• K = 16 devices spatially multiplexed
50
UAV-GUE performance interplay(realistic CSI acquisition)
Pilot contamination degrades performance of both UAVs and GUEs (~50% median rate loss)
UL power control especially helpful for GUEs
Massive MIMO alone might not be enough to support reliable UAV communications in fully loaded networks
• UAVs uniformly distributed between 1.5 and 300 meters
• One UAV per cellular sector
51
ScenariosUAV-side upgrades
3. UAVs with adaptive arrays (aaUAV):
• UAVs integrate a 2x2 adaptive array of omnidirectional elements, one RF chain
• UAVs can perform precise (analog) beamsteering towards serving BS
52
ScenariosNetwork-side upgrades
4. Massive MIMO BSs with null-steering (mMIMOnulls) :
• Spatially separate non-orthogonal pilots transmitted by different UAVs
• Place 16 radiation nulls towards vulnerable out-of-cell users
53
UAV downlink C&C channel
One UAV per cell, eight devices multiplexed per PRB Massive MIMO key for providing reliable C&C link in highly loaded networks. Inter-cell interference suppression provides large gains
54
UAV-GUE downlink interplay
All UAVs flying at 150 m
UAVs cause significant pilot contamination, degrading GUEs’ SINR, especially at cell-edge
Adaptive arrays at UAVs partially compensate for this performance loss
Inter-cell interference suppression almost preserves the cell-edge GUE performance
55
Uplink performance:Command & control and
video streaming
56
UAV uplink C&C channel and data streaming
UAVs at random heights between 1.5 m and 300 m Severe inter-UAV interferenceInterference suppression recommended
57
UAV-GUE uplink interplay
In the presence of several UAVs, massive MIMO alone may not be enough
Equipping UAVs with adaptive arrays provides limited performance improvement
Interference suppression yields the best gains
mMIMO-GUEsplit: PRBs are split between GUEs and UAVs, GUEs benefit, but UAVs deprived of resources
58
Essential guidelines
59
• Guideline 1: Take it or leave itScale up network complexity with number of connected UAVs and their height.Else, restrict the maximum height at which cellular service is guaranteed.
60
• Guideline 1: Take it or leave itScale up network complexity with number of connected UAVs and their height.Else, restrict the maximum height at which cellular service is guaranteed.
• Guideline 2: It’s all about focusBy focusing multiple signals towards multiple users, mMIMO may be the key to limit the impact that UAV-generated interference has on legacy ground communications.
61
• Guideline 1: Take it or leave itScale up network complexity with number of connected UAVs and their height.Else, restrict the maximum height at which cellular service is guaranteed.
• Guideline 2: It’s all about focusBy focusing multiple signals towards multiple users, mMIMO may be the key to limit the impact that UAV-generated interference has on legacy ground communications.
• Guideline 3: No pain no gain- Efficacy of mMIMO fades for many high UAVs.- Gains of resource splitting confined to GUE UL. Not really viable under full load.- UAV manufacturers demanding service at high heights may need to equip UAVs with beamforming capabilities.- Interference suppression may be the best bet.
62
• Guideline 1: Take it or leave itScale up network complexity with number of connected UAVs and their height.Else, restrict the maximum height at which cellular service is guaranteed.
• Guideline 2: It’s all about focusBy focusing multiple signals towards multiple users, mMIMO may be the key to limit the impact that UAV-generated interference has on legacy ground communications.
• Guideline 3: No pain no gain- Efficacy of mMIMO fades for many high UAVs.- Gains of resource splitting confined to GUE UL. Not really viable under full load.- UAV manufacturers demanding service at high heights may need to equip UAVs with beamforming capabilities.- Interference suppression may be the best bet.
• Guideline 4: The sky is the limitIn a future with a rocketing number of UAVs, mobile network operators should design novel cellular architectures with dedicated resources and cellular BSs pointing towards the sky.
63
Future outlook
64
Future outlookPossible complementary techniques to further improve:
• the performance of UAVs
• their interplay with traditional GUEs
Solution Domain Approach Challenges/drawbacks
Interference blanking
Time and frequency
Neighboring cells relieve UAVs of DL interference by employing ABSs
Less efficient for high-height UAVs, since they require blanking from large cell clusters
Opportunistic scheduling
Time and frequency
Neighboring cells schedule their respective UAVs of different PRBs
Scheduling coordination among cells; less viable for high density of UAVs
Fractional pilot reuse
Time, frequency, and space
Conservative pilot reuse for UAVs,more aggressive pilot reuse for GUEs
SRS coordination among cells; larger pilot overhead for high UAV densities
Cooperative MIMO (CoMP)
Space Turn interference into useful signal X2 interface between many cells
65
References
66
References
[1] 3GPP Technical Report 36.777, “Technical specification group radio access network; Study onenhanced LTE support for aerial vehicles (Release 15),” Dec. 2017.
[2] G. Geraci, A. Garcia-Rodriguez, L. Galati Giordano, D. Lopez-Perez, and E. Björnson,“Understanding UAV cellular communications: From existing networks to massive MIMO,” to appearin IEEE Access (available as arXiv:1804.08489), 2018.
[3] A. Garcia-Rodriguez, G. Geraci, D. Lopez-Perez, L. Galati Giordano, M. Ding, and E. Björnson, “Theessential guide to realizing 5G-connected UAVs with massive MIMO,”arXiv:1805.05654, 2018
[4] D. Lopez-Perez, M. Ding, H. Li, L. Galati Giordano, G. Geraci, A. Garcia-Rodriguez, Z. Lin, and M.Hassan, “On the Downlink Performance of UAV Communications in Dense Cellular Networks”, inProc. IEEE Globecom, Abu Dhabi, UAE, Dec. 2018.
[5] G. Geraci, A. Garcia-Rodriguez, L. Galati Giordano, D. Lopez-Perez, and E. Björnson, “SupportingUAV Cellular Communications through Massive MIMO”, in Proc. IEEE ICC Workshops, Kansas City MO,USA, June 2018.