1. Petar Popovski Aalborg University Denmark petarp@es.aau.dk
ultra-reliable low latency communications: a communication-theoretic view
Petar Popovski
Aalborg University
Denmark
CmMmW5G WCNC, Barcelona, April 15, 2018

2. outline
URLLC in 5G
URLLC performance and statistics
building blocks

3. massive and ultra-reliable wireless communication

4. wireless 5G connectivity
2/3 of 5G is MTC/IoT

5. new modes: massive and ultra-reliable access
traditional wireless vs. new mode
1 Mbps from 100 devices or 10 kbps from devices
# devices
data rate
access protocol limit
100 Mbps 95% of the time
or 100 kbps % of the time
error probability
data rate
reliability limit for control information

6. adoption of ultra-reliable communication
we need to divide the applications into two groups
cable replacement how would we design a system if we could trust to the wireless as much as to the wired?
”native” wireless applications which new systems can we think of once we are empowered with wireless connectivity?

7. URLLC performance and statistics

8. latency-reliability characterization
tR: time of data reception
reliability Pr ( 𝑡 𝑅 ≤𝑡) 1
diversity:
time?
frequency antennas interfaces
1-Pe
latency t

9. 1 1 design targets broadband rate-oriented systems
ultra-reliable low latency communication URLLC
reliability
reliability 1 1
latency
latency

10. modeling sources of uncertainty
a communication engineer models known unknowns
𝑦=ℎ∙𝑥+𝑧+𝑖
channel state
noise
interference
URLLC requires to bound the impact of the unknown unknowns in the models
interference in unlicensed spectrum is almost unknown unknown

11. Pr 𝐸 = Pr 𝛾 𝑠 1+ 𝛾 𝐼 < 𝛾 𝑡ℎ a simple error model
SINR
in absence of interference, we need to characterize the lower tail of 𝛾 𝑆
if 𝛾 𝑆 is known, we need to characterize the upper tail of 𝛾 𝐼

12. channel uncertainty in URLLC
assume that the interference is absent.
we (somehow) know that the channel is Rayleigh.
the target error rate is 𝜀 𝑈 , average SNR is 𝛾 𝑆
how do we choose the rate R?
Pr 𝐸 = Pr log 𝛾 𝑠 <𝑅
𝑅= log 𝛾 𝑆 ln 1 1− 𝜀 𝑈
where is the problem?

13. channel uncertainty in URLLC
the knowledge of average SNR is based on n collected samples
when n is low, the rate should R be chosen very conservatively
online update of the estimate and rate (or power) adaptation
P. Popovski et al., ” Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC)”, in preparation

14. building blocks for URLLC

15. transmission of short packets high diversity
channel models
transmission of short packets
high diversity
lean protocol design with respect to latency
focus on control information
network architecture
wireless slicing and coexistence with other services

16. transmission of short packets high diversity
channel models
transmission of short packets
high diversity
lean protocol design with respect to latency
focus on control information
network architecture
wireless slicing and coexistence with other services

17. wireless channel model behavior in ultra-reliable regime
currently there is lack of experimental evidence for URC-relevant statistics of wireless channels
initial analysis of common wireless channel models in URC regime
block fading
𝑃 𝑅 is the minimal SNR to decode data rate 𝑅
the analysis reveals the URC-behavior:
Pr 𝑃 𝑅 𝑃 <𝐿 ≈𝜀≈𝛼 𝑃 𝑅 𝑃 𝛽

18. outdoor physical setup
two-wave model with equal amplitudes represents one of the worst cases

19. indoor physical setup
indoor case dominated by diffuse components, good for high reliability
P. Eggers, M. Angjelichinoski, and P. Popovski, ”Wireless Channel Modeling Perspectives for Ultra-Reliable Low Latency Communications”, available on Arxiv, 2018.

20. an example of a short packet format
UNB (ultra narrowband) system
reliability of the packet reception is a product of the reliabilities of different parts
Pr 𝑠𝑢𝑐𝑐𝑒𝑠𝑠 = Pr 𝑃𝐴 Pr 𝑠𝑦𝑛𝑐 Pr 𝐼𝐷 Pr 𝑑𝑎𝑡𝑎 …

21. repetition coding for control information inefficient
the death of the ideal feedback
Philippe Petit,

22. communication theory and protocol information

23. fundamental theory of finite blocklength transmission
at short blocklengths there is a penalty that keeps the rate away from capacity.
AWGN SNR 0 dB

24. probability of error 10e-3
gain in reliability
low SNR
10 bytes control information
10 bytes data
same amount of channel uses
probability of error 10e-3
probability of error
10e-6

25. mixing data and control information has energy cost
M D
frequency M
frequency D
time
time
the notion of frame in cellular systems should be revisited

26. connection between packetization and energy efficiency
separated data and metadata
Alice
useful for energy efficiency
data for Bob, Carol turns off her receiver after the metadata
Bob
Carol
joint data and metadata
Alice
better coding of the metadata
however, everybody decodes everything
Bob
Carol

27. some observations
basic tradeoff between energy efficiency and ultra-reliability
departure from the common causal relationship metadata -> data
low latency usually means sending with few channel uses (DoF)
DoF can be increased in e.g. frequency or space

28. example: a rigorous framework for revisiting downlink framing
downlink communication to K users
a user is active and there is a packet for her with probability q.
the message for each active user is drawn randomly from a set of predefined message sizes.
metadata should inform about
who is active
the message size.
K. F. Trillingsgaard and P. Popovski, "Downlink Transmission of Short Packets: Framing and Control Information Revisited," in IEEE Transactions on Communications, vol. 65, no. 5, pp , May 2017.

29. example: a rigorous framework for revisiting downlink framing
conventional framing with pointers
alternative framing

30. latency-energy tradeoff
new tradeoff arises for short packets
latency is minimized when all packets are jointly encoded;
power is minimized when each packet is encoded separately.
K=32 users
nhe conventional frame-based transmission and the joint encoding of all packets represent only two points on the tradeoff curve.

31. massive MIMO and ultra-reliability
pros
very high SNR links
quasi-deterministic links, fading immunity
extreme spatial multiplexing capability
cons
expensive CSI acquisition procedure
additional protocol steps

32. massive MIMO/URLLC: mitigating the CSI problem
downlink beamforming based on channel structure non-coherent energy detection in the uplink
A. Sabin-Bana, M. Angjelichinoski, E. de Carvalho and P. Popovski, ”Massive MIMO for Ultra-reliable Communications with Constellations for Dual Coherent-noncoherent Detection”, in IEEE WSA, Bochum, 2018.

33. multi-interface transmission interface diversity
cloning
2-out-of-3
transmission strategies

34. interface diversity experimental results
results based on lab measurements
1 day, 100 ms interval
Wi-Fi
achieves 10 ms for 90% of packets
but 99% requires almost 100 ms
cellular: LTE and HSPA
also requires ~100 ms for 99%
cloning (1 copy per IF)
99% at 25 ms
99.999% at 60 ms latency
J. J. Nielsen, R. Liu, and P. Popovski, “Ultra-Reliable Low Latency Communication (URLLC) using Interface Diversity”, IEEE Transactions on Communications, accepted, available at ArXiv, 2017.

35. final remarks: protocol challenge is immensely largerUE
eNB
1. Random Access preamble
2. Random access response
access Attempt
3. RRC Conn. Request
4.a. Contention resolution
4.b. RRC Conn. Setup
5. RRC Conn. Setup Complete
6. RRC Security Mode Command
connection establishment
7. RRC Security Mode Complete
8. RRC Conn. Reconfiguration
9. RRC Conn. Reconf. Compl.
* Source: Ciscus Sarasota
10. Small Data Payload

36. final remarks
ultra-reliable wireless has the potential to profoundly change systems and devices
essential:
short packet transmission
communication-theoretic attention to the control information
every step in the protocol needs a careful reliability design
careful use of diversity
large number of steps in real protocols impair reliability and latency
lean protocol design

37. additional references
P. Popovski, J. J. Nielsen, C. Stefanovic, E. de Carvalho, E. Ström, K. F. Trillingsgaard, A.-S. Bana, D. M. Kim, R. Kotaba, J. Park, R. B. Sørensen, "Ultra-Reliable Low-Latency Communication (URLLC): Principles and Building Blocks", IEEE Network Magazine, Special issue on 5G for Ultra-Reliable Low Latency Communications, in revision, 2017.
K. F. Trillingsgaard, and P. Popovski, “Generalized HARQ Protocols with Delayed Channel State Information and Average Latency Constraints”, accepted for publication in IEEE Transactions on Information Theory, 2017.
A. Kalør, R. Guillaume, J. Nielsen, A. Mueller and P. Popovski, “Network Slicing in Industry 4.0 Applications: Abstraction Methods and End-to-End Analysis”, IEEE Transactions on Industrial Informatics (SS on From Industrial Wireless Sensor Networks to Industrial Internet-of-Things), 2017.
G. Durisi, T. Koch and P. Popovski, "Toward Massive, Ultrareliable, and Low-Latency Wireless Communication With Short Packets," Proceedings of the IEEE, vol. 104, no. 9, pp , Sept
P. Popovski, ”Ultra-Reliable Communication in 5G Wireless Systems”, 1st International Conference on 5G for Ubiquitous Connectivity, Levi, Finland, November 2014.
P. Popovski, K. F. Trillingsgaard, O. Simeone, and G. Durisi, ”5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View”, available on Arxiv, 2018.