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

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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.

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Part I – Outline

• UAV users: An operator’s perspective

• 3GPP standardization work

• Performance in existing and future networks

• Summary

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The speakers

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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@upf.edu

Giovanni Geraci

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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@nokia-bell-labs.com

Adrian Garcia-Rodriguez

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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

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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

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UAV Communications – Why are they important?

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UAV Communications – Why are they important?

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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.

?

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An operator’s perspective

Another dream

• Employ UAVs as mobile base stations (BSs).

• Enhance network performance through dynamic, on-demand BS repositioning.

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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

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Outline:

• Standardization timeline

• Study item: Enhanced LTE support for aerial vehicles

• Work item: Enhanced LTE support for aerial vehicles

• Way forward

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Standardization timeline

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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

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Study Item:Enhanced LTE support for aerial vehicles

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Enhanced LTE support for aerial vehicles

Study item timeline:

1. UAV traffic requirements definition

2. Channel modelling

3. Performance analysis

4. Enhancing UAV communications

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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.

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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.

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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.

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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.

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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.

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Work Item:Enhanced LTE support for aerial vehicles

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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

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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.

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Way forward

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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

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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.

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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.

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References

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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.

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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

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Outline:

• System evaluation set-up

• UAV cell selection

• Downlink performance (C&C)

• Uplink performance (C&C + video streaming)

• Essential guidelines

• References

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System evaluation set-up

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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

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System parameters

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UAV cell selection

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UAV associationExisting cellular networks are optimized for ground users

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UAV associationExisting cellular networks are optimized for ground users

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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

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Downlink performance:Command & control

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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

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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+

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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

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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

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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

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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

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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

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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

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Uplink performance:Command & control and

video streaming

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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

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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

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Essential guidelines

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• 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.

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• 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.

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• 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.

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• 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.

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Future outlook

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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

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References

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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.