End-to-End Design and Evaluation of mmWave Cellular Networks - - PowerPoint PPT Presentation

▶
end to end design and evaluation of mmwave cellular
SMART_READER_LITE
LIVE PREVIEW

End-to-End Design and Evaluation of mmWave Cellular Networks - - PowerPoint PPT Presentation

Sep 08, 2022 167 likes •577 views

End-to-End Design and Evaluation of mmWave Cellular Networks Michele Polese Department of Information Engineering End-to-end mmWaves University of Padova, Italy polesemi@dei.unipd.it Supervisor: Prof. Michele Zorzi Outline Introduction

slide-1
SLIDE 1

Michele Polese

Department of Information Engineering University of Padova, Italy polesemi@dei.unipd.it Supervisor: Prof. Michele Zorzi

End-to-End Design and Evaluation of mmWave Cellular Networks

End-to-end mmWaves

slide-2
SLIDE 2

Outline

  • Introduction
  • A case for end-to-end, full-stack evaluations
  • Architectures for 5G mmWaves
  • End-to-end protocols for mmWaves
  • Data-driven 5G network optimization
  • Conclusions and research directions

End-to-end mmWaves

2

slide-3
SLIDE 3

3GPP NR: novelties

Bandwidth 𝐶 − max 400 MHz per carrier Frame – 10 ms Physical Resource Block N subcarriers

Subframe – 1 ms Examples of slot numbers with different subcarrier spacing

Subcarrier spacing 60 kHz

Slot – 0.25 ms

Subcarrier spacing 120 kHz

Slot – 0.125 ms

Symbol – 8.9 μs

Flexible frame structure

LTE SA deployment

5G Core

NSA deployment

4G EPC PGW/SGW MME HSS Network slicing and NFV AMF SMF UPF PCF AMF SMF UPF PCF AMF SMF UPF PCF

5G Core Network options mmWave directional communications Multi RAT access

NR NR

End-to-end mmWaves

3

slide-4
SLIDE 4

3GPP NR: timeline

Goal: deployment by 2020

Non Stand-alone specifications

  • Dec. 2017

June 2018 Stand-alone specifications 5G phase 1 5G phase 2 March 2020 Release 15 Release 16

End-to-end mmWaves

4

slide-5
SLIDE 5

3GPP NR: mmWaves in cellular networks

End-to-end mmWaves

3GPP NR Release 16 will support frequencies up to 52.6 GHz

  • Z. Pi and F. Khan, "An introduction

to millimeter-wave mobile broadband systems," in IEEE Communications Magazine, vol. 49, no. 6, pp. 101-107, June 2011.

5G increases the

datarate [Gbps] latency [ms]

5G decreases the 5

slide-6
SLIDE 6

3GPP NR: challenges for mmWaves

End-to-end mmWaves

UAV with mmWave radio Ground station

blockage

6

slide-7
SLIDE 7

End-to-end design and performance: why?

  • Sometimes, link-level is enough
  • Real networks, however, have several

components in-between the user, the link and the content he/she needs

PHY MAC RLC TCP/IP APP PDCP RRC

Core network Internet

TCP/IP APP

End-to-end mmWaves

Focus of my research end-to-end, system-level design & evaluation of 5G mmWave networks

7

slide-8
SLIDE 8

End-to-end mmWaves

The Architecture

System Level Design of 5G mmWave Networks

The Protocols

End-to-End and Cross-Layer Analysis of 5G mmWave Networks

The Intelligence

Data-Driven 5G Networks Optimization

8

slide-9
SLIDE 9

The tool: ns-3 mmWave module

Channel model Application and network stack 3GPP cellular stack 3GPP cellular stack Application and network stack

Packet

Propagation Fading Beamforming Interference

SINR

Error model

End-to-end mmWaves

https://github.com/nyuwireless-unipd/ns3-mmwave https://github.com/signetlabdei/ns3-mmwave-iab https://github.com/signetlabdei/quic https://github.com/signetlabdei/mmwave-psc-scenarios

9

slide-10
SLIDE 10

System Level Design of 5G mmWave Networks

Multi connectivity, beam management and Integrated Access and Backhaul

The Architecture

The Architecture

System Level Design of 5G mmWave Networks

The Protocols

End-to-End and Cross- Layer Analysis of 5G mmWave Networks

The Intelligence

Data-Driven 5G Networks Optimization

slide-11
SLIDE 11

System-level challenges at mmWaves

The Architecture

Issues: high propagation loss and blockage

Large antenna arrays increase the link budget, but the power is focused on narrow beams Ultra-dense deployments

Provide backhaul to all the base stations 2

1

High number

  • f handovers

3

Need to track the narrow beams when moving

11

slide-12
SLIDE 12

System level solutions at mmWaves

The Architecture

Integrated Access and Backhaul

Low cost, high density mmWave deployments

1

Multi-connectivity

Low-latency, highly reliable handovers

2 3

Beam management

Seamless tracking

12

slide-13
SLIDE 13
  • Goal: design a system resilient to fluctuations and outages
  • Contribution:

Multi-connectivity architecture to combine sub-6 GHz and mmWave benefits

Mobility management

The Architecture

13

1

slide-14
SLIDE 14

Results: latency with TCP traffic

  • No handover (always keep the same BS)
  • Single connectivity (traditional HO architecture)
  • Multi connectivity (fast handovers – no service interruption)

The Architecture

Proposed solution 14

slide-15
SLIDE 15

Integrated Access and Backhaul

  • Goal: provide wireless backhaul to ultra-dense mmWave

networks

  • Contributions:

IAB module for ns-3 mmWave Analysis of IAB end-to-end performance Distributed path selection policies

The Architecture

3GPP Work Item for Release 16

SDAP PDCP RLC MAC RLC MAC

Adaptation

RLC MAC

Adaptation

RLC MAC GTP-U UDP IP GTP-U UDP IP SDAP PDCP PHY PHY

Adaptation

RLC MAC

Adaptation

RLC MAC PHY PHY F1* Uu

IAB-donor IAB-node IAB-node UE DU CU-UP

F1*

MT Core Network Internet

2

slide-16
SLIDE 16

End-to-end Performance for IAB

The Architecture

Impact of synchronous vs. bursty traffic

Webpage loading time with browsing model Throughput with full buffer source

20

slide-17
SLIDE 17
  • Goal: perform directional initial access and tracking
  • Contributions:

Study of 3GPP NR beam management schemes Analysis of their performance with design insights

Beam management in 3GPP NR

28 GHz

  • mnidirectional range

28 GHz directional range

The Architecture

15

3

slide-18
SLIDE 18

1. Beam sweeping 2. Beam measurement 3. Beam determination 4. Beam reporting

Beam Management in NR

The 3GPP has specified a set of procedures for the control of multiple beams at mmWave frequencies which are categorized under the term BEAM MANAGEMENT

The Architecture

Initial Access in a standalone deployment

RACH preamble

gNB UE

SS Burst UE decides which is the best beam SS Blocks to get RACH resources UE receives RACH resource allocation

Beam sweep and measurement Beam determination Beam reporting

16

slide-19
SLIDE 19

Reactiveness: how much time does it take to perform IA? Accuracy: what is the probability

  • f receiving an SS block?

Accuracy-reactiveness tradeoff in NR

The Architecture

Number of antennas at gNB and UE gNB density Number of SS blocks per burst

17

slide-20
SLIDE 20

Beam management for UAVs

The Architecture Proposed location-based beam management for UAVs Experimental evaluation

slide-21
SLIDE 21

End-to-End and Cross-Layer Analysis of 5G mmWave Networks

TCP issues in mmWave networks

The Protocols

The Architecture

System Level Design of 5G mmWave Networks

The Protocols

End-to-End and Cross- Layer Analysis of 5G mmWave Networks

The Intelligence

Data-Driven 5G Networks Optimization

slide-22
SLIDE 22

TCP issues in mmWave networks

The Protocols

PHY MAC RLC TCP APP PDCP SDAP

End-to-end data plane

IP

mmWave channel: volatile, highly variable capacity, large bandwidth Link view

  • Retransmissions
  • Reordering
  • Buffering

How do these components interact?

Host-to-host “abstract” view

  • Congestion and flow control
  • Retransmissions

22

slide-23
SLIDE 23

The Protocols

LOS NLOS

After transition from LOS

time

LOS

time

LOS NLOS

RLC buffer

  • ccupancy

congestion window

Large buffer Bufferbloat High latency Small buffer Buffer overflow Low throughput

a) DUPACK retx (CW/2) b) RTO retx (CW=1) time

a) b)

LOS NLOS

RLC buffer

  • ccupancy

congestion window Slow ramp-up when back in LOS

1 2 3

23

slide-24
SLIDE 24

Possible solutions

  • Goal: improve TCP end-to-end performance on mmWaves
  • Contributions:

Edge deployments: a shorter control loop, to react faster CC algorithms: faster window ramp-up mechanisms Exploit multiple paths: mobility management or MP-TCP milliProxy: cross-layer approach to better control the TCP sending rate

The Protocols

Flow Buffer Flow window management module ACK management module milliProxy instance server UE end-to-end flows server UE flow Buffer flow window management module ACK management module milliProxy instance

24

slide-25
SLIDE 25

milliProxy – a TCP proxy for mmWaves

Reduce buffering latency + increase goodput

▪ Transparent to the end-to-end flow ▪ Installed in the gNB – or at the edge ▪ Cross-layer approach

▪ Per-UE data rate ▪ RLC buffer occupancy ▪ RTT estimation

▪ Modular

▪ Plug-in different flow control algorithms

(inspired to [1])

Flow Buffer Flow window management module ACK management module milliProxy instance server UE end-to-end flows server UE flow Buffer flow window management module ACK management module milliProxy instance

The Protocols

[1] M. Casoni et al., “Implementation and validation of TCP options and congestion control algorithms for ns- 3,” in Proc. WNS3, 2015

25

slide-26
SLIDE 26

milliProxy – flow control

▪ Interaction with the TCP sender

▪ TCP sending rate is min(CW,ARW) ▪ milliProxy modifies the ARW in the ACKs, according to the flow control policy used

▪ Bandwidth-Delay Product (BDP) based ARW = BW*RTT ▪ More conservative ARW = min([RTT*PHYrate]-B, 0)

Congestion window (computed

by the sender)

Advertised window (receiver’s

feedback sent on ACK packets)

The Protocols

26

slide-27
SLIDE 27

Results: scenario with many LOS/NLOS transitions

Throughput Latency

Latency reduction w milliProxy Throughput gain w milliProxy

The Protocols

27

slide-28
SLIDE 28

Data-Driven 5G Networks Optimization

Machine Learning at the Edge

The Intelligence

The Architecture

System Level Design of 5G mmWave Networks

The Protocols

End-to-End and Cross- Layer Analysis of 5G mmWave Networks

The Intelligence

Data-Driven 5G Networks Optimization

slide-29
SLIDE 29
  • Goal: deploy intelligent and data-driven techniques in 5G

networks

  • Contributions:

Mobile-edge controller-based architecture Data-driven dynamic clustering of base stations Prediction accuracy of the number of UEs per base station

Machine learning at the edge

The Intelligence

Data collection Policy enforcement

PHY-high MAC RLC PDCP SDAP RRC

RU CU CU CU RU RU RAN Controller CU CU CU RAN Controller RU RU RU RU RU CU RU CU CU CU RU RU RAN Controller Cloud Network Controller

3GPP RAN layers

Fast contro l loop

Cluster

RAN Edge Cloud

PHY-low RF

DU DU DU DU DU DU DU DU DU DU

29

slide-30
SLIDE 30

Data-driven clustering example

The Intelligence

Base station locations Each color is a cluster

30

slide-31
SLIDE 31

Prediction of the number of UEs

  • Spatial correlation (cluster- vs local-based) is more

impactful than temporal correlation

  • Exploit geographic constraints on mobility flows
  • When considering all the 472 eNBs (in 22 clusters):

The Intelligence

53% RMSE reduction 5% RMSE reduction when increasing 𝑋

fix flo

1 2 3 4 5 6 7 8 9 6 8 10 12 14 16 Lag L [5 minutes] RMSE ˆ σ Cluster-based GPR Local-based BRR

[ˆ σ

Approach based

  • n the proposed

architecture

31

slide-32
SLIDE 32

Conclusions

Conclusions

slide-33
SLIDE 33

Conclusions

  • System–level, end-to-end approach throughout all the

topics

  • Considered different components of a complex system

and introduce novel contributions for

  • Architectures
  • Protocols
  • Intelligence
  • Thorough and realistic performance evaluation
  • End-to-end, full-stack analysis may uncover

unexpected behaviors

Conclusions

33

slide-34
SLIDE 34

Future work

  • Future research will still be focused on a system-level

approach, combined with testbeds and experimental results

  • What happens when you consider increasingly complex

systems?

Conclusions

Intel NUC GNSS, Compass, Gyro, MAG, Acceler.

Facebook Terragraph mmWave Radio

Motor 1 Motor 2 Motor 3 Motor 5 6 battery slots Motor 4 Motor 6

mmBAC

Python 3.7 to DJI Onboard SDK Python 3.7 L1: PHY L0: Motion L2: MAC

Flight mgmt commands

mmWave Radio

60 GHz RF

Intel NUC

Beam selection

USB 3.0 Eth0

+ _ + _ + _ + _ + _ + _

Power Module

MOTOR 1 MOTOR 2 MOTOR 3 MOTOR 4

ESC

MOTOR 6 MOTOR 5

Eth0

Beam mgmt commands DJI Flight Controller Unit (FCU)

DC power input

DC-DC step up Location readings

Wi-Fi

GNSS Comp. Gyro MAG Accel.

I/O Board

DJI M600 Pro

SNR readings

A3 Pro DJI FC

JTAG

DJI API

34

slide-35
SLIDE 35

Journals

  • [1] M. Polese, M. Giordani, M. Mezzavilla, S. Rangan, and M. Zorzi, “Improved Handover Through Dual Connectivity in 5G mmWave Mobile

Networks,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 9, pp. 2069–2084, September 2017.

  • [2] M. Polese, R. Jana, and M. Zorzi, “TCP and MP-TCP in 5G mmWave Networks,” IEEE Internet Computing, vol. 21, no. 5, pp. 12–19,

September 2017.

  • [3] M. Mezzavilla, M. Zhang, M. Polese, R. Ford, S. Dutta, S. Rangan, and M. Zorzi, “End-to-end simulation of 5G mmwave networks,” IEEE

Communications Surveys and Tutorials, vol. 20, no. 3, pp. 2237–2263, Third quarter 2018.

  • [4] M. Mezzavilla, M. Polese, A. Zanella, A. Dhananjay, S. Rangan, C. Kessler, T. S. Rappaport, and M. Zorzi, “Public Safety Communications

above 6 GHz: Challenges and Opportunities,” IEEE Access, vol. 6, pp. 316–329, 2018.

  • [5] M. Dalla Cia, F. Mason, D. Peron, F. Chiariotti, M. Polese, T. Mahmoodi, M. Zorzi, and A. Zanella, “Using Smart City Data in 5G Self-

Organizing Networks,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 645–654, April 2018.

  • [6] M. Zhang, M. Polese, M. Mezzavilla, J. Zhu, S. Rangan, S. Panwar, and a. M. Zorzi, “Will TCP Work in mmWave 5G Cellular Networks?” IEEE

Communications Magazine, vol. 57, no. 1, pp. 65–71, January 2019.

  • [7] M. Giordani, M. Polese, A. Roy, D. Castor, and M. Zorzi, “Standalone and Non-Standalone Beam Management for 3GPP NR at mmWaves,”

IEEE Communications Magazine, vol. 57, no. 4, pp. 123–129, April 2019.

  • [8] M. Giordani, M. Polese, A. Roy, D. Castor, and M. Zorzi, “A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies,” IEEE

Communications Surveys and Tutorials, vol. 21, no. 1, pp. 173–196, First quarter 2019.

  • [9] M. Polese, F. Chiariotti, E. Bonetto, F. Rigotto, A. Zanella, and M. Zorzi, “A Survey on Recent Advances in Transport Layer Protocols,” IEEE

Communications Surveys and Tutorials, pp. 1–1, 2019.

  • [10] F. Meneghello, M. Calore, D. Zucchetto, M. Polese, and A. Zanella, “IoT: Internet of Threats? A survey of practical security vulnerabilities

in real IoT devices,” IEEE Internet of Things Journal, pp. 1–1, 2019.

  • [11] M. Polese, R. Jana, V. Kounev, K. Zhang, S. Deb, and M. Zorzi, “Machine Learning at the Edge: A Data-Driven Architecture with

Applications to 5G Cellular Networks,” submitted to IEEE Transactions on Mobile Computing, 2019. [Online]. Available: https://arxiv.org/pdf/1808.07647.pdf

  • [12] M. Polese, M. Giordani, T. Zugno, A. Roy, S. Goyal, D. Castor, and M. Zorzi, “Integrated Access and Backhaul in 5G mmWave Networks:

Potentials and Challenges,” IEEE Communications Magazine (to appear), 2019. [Online]. Available: https://arxiv.org/pdf/1906.01099.pdf

  • [13] M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “Towards 6G Networks: Use Cases and Technologies,” IEEE

Communications Magazine (to appear), March 2019. [Online]. Available: https://arxiv.org/pdf/1903.12216.pdf

Conclusions

35

slide-36
SLIDE 36

Conferences - 1

  • [14] M. Polese, M. Centenaro, A. Zanella, and M. Zorzi, “M2M massive access in LTE: RACH performance evaluation in a Smart

City scenario,” in 2016 IEEE International Conference on Communications (ICC), May 2016, pp. 1–6.

  • [15] M. Polese, M. Mezzavilla, and M. Zorzi, “Performance Comparison of Dual Connectivity and Hard Handover for LTE-5G Tight

Integration,” in Proceedings of the 9th EAI International Conference on Simulation Tools and Techniques, ser. SIMUTOOLS’16, Prague, Czech Republic, 2016, pp. 118–123.

  • [16] F. Chiariotti, D. Del Testa, M. Polese, A. Zanella, G. M. Di Nunzio, and M. Zorzi, “Learning methods for long-term channel

gain prediction in wireless networks,” in International Conference on Computing, Networking and Communications (ICNC2017), January 2017.

  • [17] M. Polese, R. Jana, and M. Zorzi, “TCP in 5G mmWave Networks: Link Level Re- transmissions and MP-TCP,” in 2017 IEEE

Conference on Computer Communications Workshops (INFOCOM WKSHPS), May 2017.

  • [18] E. Lovisotto, E. Vianello, D. Cazzaro, M. Polese, F. Chiariotti, D. Zucchetto, A. Zanella, and M. Zorzi, “Cell Traffic Prediction

Using Joint Spatio-Temporal Information,” in 6th International Conference on Circuits and Systems Technologies (MOCAST), May 2017.

  • [19] M. Zhang, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “ns-3 Implementation of the 3GPP MIMO Channel Model for

Frequency Spectrum above 6 GHz,” in Proceedings of the 9th Workshop on ns-3, Porto, Portugal, 2017, pp. 71–78.

  • [20] T. Azzino, M. Drago, M. Polese, A. Zanella, and M. Zorzi, “X-TCP: A Cross Layer Approach for TCP Uplink Flows in mmWave

Networks,” in 16th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net’17), June 2017.

  • [21] M. Dalla Cia, F. Mason, D. Peron, F. Chiariotti, M. Polese, T. Mahmoodi, M. Zorzi, and A. Zanella, “Mobility-aware Handover

Strategies in Smart Cities,” in International Symposium on Wireless Communication Systems (ISWCS), August 2017.

  • [22] M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “Mobility Management for TCP in mmWave Networks,” in Proceedings of

the 1st ACM Workshop on Millimeter-Wave Networks and Sensing Systems 2017, ser. mmNets ’17. Snowbird, Utah, USA: ACM, 2017, pp. 11–16.

  • [23] M. Gentil, A. Galeazzi, F. Chiariotti, M. Polese, A. Zanella, and M. Zorzi, “A deep neural network approach for customized

prediction of mobile devices discharging time,” in 2017 IEEE Global Communications Conference (GLOBECOM), Dec 2017, pp. 1– 6.

Conclusions

36

slide-37
SLIDE 37

Conferences - 2

  • [24] M. Polese, M. Mezzavilla, M. Zhang, J. Zhu, S. Rangan, S. Panwar, and M. Zorzi, “milliProxy: A TCP proxy architecture for 5G

mmWave cellular systems,” in 2017 51st Asilomar Conference on Signals, Systems, and Computers, Oct 2017, pp. 951–957.

  • [25] M. Polese, M. Mezzavilla, S. Rangan, C. Kessler, and M. Zorzi, “mmwave for future public safety communications,” in

Proceedings of the First CoNEXT Workshop on ICT Tools for Emergency Networks and DisastEr Relief, ser. I-TENDER ’17. Incheon, Republic of Korea: ACM, 2017, pp. 44–49. [Online]. Available: http://doi.acm.org/10.1145/3152896.3152905

  • [26] M. Drago, T. Azzino, M. Polese, C. Stefanovic, and M. Zorzi, “Reliable Video Streaming over mmWave with Multi Connectivity

and Network Coding,” in International Conference on Computing, Networking and Communications (ICNC), March 2018, pp. 508– 512.

  • [27] T. Zugno, M. Polese, and M. Zorzi, “Integration of Carrier Aggregation and Dual Connectivity for the ns-3 mmWave Module,”

in Proceedings of the 10th Workshop on ns-3, ser. WNS3 ’18, Surathkal, India, 2018, pp. 45–52.

  • [28] M. Polese and M. Zorzi, “Impact of Channel Models on the End-to-End Performance of mmWave Cellular Networks,” in

Proceedings of the 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), June 2018.

  • [29] M. Giordani, M. Polese, A. Roy, D. Castor, and M. Zorzi, “Initial access frameworks for 3GPP NR at mmWave frequencies,” in

2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), June 2018, pp. 1–8.

  • [30] M. Polese, M. Giordani, A. Roy, S. Goyal, D. Castor, and M. Zorzi, “End-to-End Simulation of Integrated Access and Backhaul

at mmWaves,” in IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Net- works (CAMAD), September 2018.

  • [31] M. Polese, M. Giordani, A. Roy, D. Castor, and M. Zorzi, “Distributed Path Selection Strategies for Integrated Access and

Backhaul at mmWaves,” in IEEE Global Communications Conference (GLOBECOM), Dec 2018.

  • [32] M. Rebato, M. Polese, and M. Zorzi, “Multi-Sector and Multi-Panel Performance in 5G mmWave Cellular Networks,” in IEEE

Global Communications Conference (GLOBECOM), Dec 2018.

  • [33] M. Polese, R. Jana, V. Kounev, K. Zhang, S. Deb, and M. Zorzi, “Exploiting spatial correlation for improved user prediction in

5G cellular networks,” in Proceedings of the Information Theory and Applications Workshop, ser. ITA ’19, San Diego, 2019.

Conclusions

37

slide-38
SLIDE 38

Conferences - 3

  • [34] W. Xia, M. Polese, M. Mezzavilla, G. Loianno, S. Rangan, and M. Zorzi, “Millimeter Wave Remote UAV Control and

Communications for Public Safety Scenarios,” in Proceedings of the 1st International Workshop on Internet of Autonomous Unmanned Vehicles, ser. IAUV ’19, Boston, MA, 2019.

  • [35] M. Polese, T. Zugno, and M. Zorzi, “Implementation of Reference Public Safety Scenarios in ns-3,” in Proceedings
  • f the 11th Workshop on ns-3, ser. WNS3 ’19, Florence, Italy, 2019.
  • [36] A. De Biasio, F. Chiariotti, M. Polese, A. Zanella, and M. Zorzi, “A QUIC Implementation for ns-3,” in Proceedings
  • f the 11th Workshop on ns-3, ser. WNS3 ’19, Florence, Italy, 2019.
  • [37] T. Zugno, M. Polese, M. Lecci, and M. Zorzi, “Simulation of Next-Generation Cellular Networks with ns-3: Open

Challenges and New Directions,” in Proceedings of the Work- shop on Next-Generation Wireless with ns-3, ser. WNGW ’19, Florence, Italy, 2019.

  • [38] M. Polese, F. Restuccia, A. Gosain, J. Jornet, S. Bhardwaj, V. Ariyarathna, S. Man- dal, K. Zheng, A. Dhananjay, M.

Mezzavilla, J. Buckwalter, M. Rodwell, X. Wang, M. Zorzi, A. Madanayake, and T. Melodia, “MillimeTera: Toward A Large-Scale Open- Source mmWave and Terahertz Experimental Testbed,” in Proceedings of the 3rd ACM Workshop

  • n Millimeter-Wave Networks and Sensing Systems, ser. mmNets ’19. Los Cabos, Mexico: ACM, 2019.
  • [39] L. Bertizzolo, M. Polese, L. Bonati, A. Gosain, M. Zorzi, and T. Melodia, “mmBAC: Location-aided mmWave

Backhaul Management for UAV-based Aerial Cells,” in Pro- ceedings of the 3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, ser. mmNets ’19. Los Cabos, Mexico: ACM, 2019.

  • [40] M. Drago, M. Polese, S. Kucera, D. Kozlov, V. Kirillov, and M. Zorzi, “QoS Provisioning in 60 GHz Communications

by Physical and Transport Layer Coordination,” IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Nov 2019.

Conclusions

38

slide-39
SLIDE 39

Book Chapter

  • [41] M. Polese, M. Giordani, and M. Zorzi, “3GPP NR: the standard for 5G

cellular networks,” in 5G Italy White eBook: from Research to Market, 2018.

Conclusions

39

slide-40
SLIDE 40

Michele Polese

Department of Information Engineering University of Padova, Italy polesemi@dei.unipd.it

End-to-End Design and Evaluation of mmWave Cellular Networks

polese.io mmwave.dei.unipd.it

End-to-end mmWaves