Presented by Dr.Mehdi Bennis, Centre for Wireless Communications, University of Oulu, Finland at The International Conference on Wireless Networks and Mobile Communications (WINCOM'17), November 01-04, 2017, Rabat, Morocco ** SHARED WITH PERMISSION **

1. Building the foundations of
Ultra-RELIABLE and Low-LATENCY Wireless
Communication
ATale of Risk at Scale
Dr. Mehdi Bennis
Centre for Wireless Communications
University of Oulu, Finland
1
Tail

2. 2
Table of Contents
Motivation
Latency and reliability definitions
State-of-the-art (SOTA): Gist of it
Key enablers for low latency
Key enablers for high reliability
Tradeoffs
Mathematical tools + applications to wireless
Conclusions

3. University of Oulu
Motivation
Testimonials:
”Obtaining reliability plots requires time-consuming Monte-Carlo simulations”
(Qualcomm 2017)
”If you have a proposal on uRLLC, we will very much welcome it”
(Nokia Bell-Labs 2017)
”It would be great to have a framework for URLLC for understanding the costs.”
(Huawei ITA 2016)
[PAST] Up until now wireless networks geared towards network capacity with little attention to
latency/reliability
[CURRENT] Buzz around URLLC in 5G to enable mission-critical applications, low-latency and ultra-
reliability
Yet, no tractable nor fundamental framework is available
[FUTURE] If successful, URLLC will empower applications thus far deemed impossible…
At its core, enabling URLLC mandates a departure from mean performance utility-based
approaches (average throughput, average response time, etc.) towards a tail/risk/scale-
centric design.

4. A (short) historical perspective of URLLC
1948: Reliable communication has been a fundamental problem in information
theory since Shannon’s landmark paper showing the possibility to communicate
with vanishing probability of error at non-zero rates.
Error exponents via reliability functions provide insights by characterizing the
exponential rates at which error probabilities decay for large coding block-lengths.
Previous works on critical communications such as TETRA networks for public safety,
cut-off rate in information theory back in 1968 with Gallager (prior to the short
packet communication theory).
An obsession since the 80’s towards spectral efficiency until the advent of mission-
critical applications (e.g., industry 4.0).

5. URLLC is use case dependent
AR/VR/XR
Factory 2.0
V2X
eMBB
Robotics, UAVs
CPS
Telemedicine
etc
URLLC Scenarios:
- Hyperlocal: air-interface latency
- local area/short range: latency due to access part
- remote/long-range communication: latency across
backhaul, cloud/edge and core segments.
Need for a holistic approach that spans not just the wireless
access but also wireless core and cloud architecture

6. University of Oulu
Is it really possible to have both low latency and ultra reliable networks?
Back to the basics: how do we define reliability + Latency?
Why do we need URLLC? What new service applications will it enable?
How to achieve low latency and ultra-reliability in 5G?
What are the key technology components of 5G New Radio for providing URLLC services?
What 5G technologies can make the 5G ultra-reliability, low latency system a reality?
Can we apply the same design principles as in eMBB?
The What, Why and How of uRLLC?
6
Source: URLLC 2017 event, Nov 14 @ London

7. 4G vs. 5G (in a nutshell)
7
4G 5G
important crucial
Long (MBB) Short (URLLC)
Long (eMBB)
Throughput-centric
No latency/reliability constraints
Average delay good enough
Latency and reliability centric
Tails DO MATTER
Ergodic Outage capacity
95% or less 1-10^-x x=[3,4,5,6,8,9] use case specific
Shannonian (long packets) Rate loss due to short packets
~15ms RTT based on 1ms
subframe
1ms and less (use case specific)
Shorter TTI, HARQ RTT
unbounded bounded
Exponential decay
using effective bandwidth
Faster decay than exponential
sub 6GHz
A few users/devices
Sub-and-Above 6GHz
(URLLC @sub-6GHz)
billion devices
Metadata,
control channel
Packet size
Design
Reliability
Rate
Latency
Queue size
Delay violation
probability
Frequency bands
Scale
eMBB can
still be average based
*
*

8. End-to-end (E2E) latency: scheduling delay+ queuing delay+
transmission delay+ receiver-side processing and decoding
delay+ multiple HARQ RTT
User plane latency (3GPP) [1]: one-way time it takes to
successfully deliver a packet…
Control plane latency (3GPP) [1]: transition time from a most
“battery efficient” state (e.g., Idle state) to the start of
continuous data transfer (e.g. active state).
Latency and Reliability (definitions)
[1] 3GPP, “Service requirements for the 5g system” in 3rd Generation Partnership Project (3GPP), TS 22.261 v16.0.0, 06 2017, 2017.
NO packet drop
NO delayed packet
NO erroneously decoded packet
Reliability per node: transmission error probability, queuing
delay, violation probability and proactive + dropping
probability
Reliability (3GPP): successfully transmit 32byte message
over the 5G radio Interface within 1ms with a success
probability of 1-10^-5
Availability: probability that a given service is available (i.e.,
coverage). Higher availability entails lower reliability
Reliability
• ITU and 3GPP require 5G to successfully transmit 32byte message over the 5G radio Interface within 1ms with a 1-10^-5 success probability --
------------------- maximum BLER of 10^-5
• 3GPP further requires 5G to be able to achieve an average latency over the 5G radio interface of 0.5ms
• While URLLC are E2E requirements, 3GPP and ITU consider only one way latency over 5G RAN

9. Significant contribution towards understanding ergodic capacity for a few users and average queuing
performance of wireless networks focusing on large blocklengths.
However, crisp insights for reliability and latency issues & understanding non-asymptotic tradeoffs of latency,
throughput and reliability are MISSING.
Gist of State-of-the-Art (SOTA)
Latency
 At PHY level: throughput-delay tradeoffs, error exponents, delay-limited
link capacity, finite blocklength channel coding.
Focus on minimizing average latency instead of worst-case latency.
 At network level: rich literature on queue-based resource allocation
(Lyapunov optimization) w/ limited number of queues, effective
capacity and other large-deviation type (LDT) results used.
However, while stability is important in queuing networks, fine-grained
metrics (delay distribution and probabilistic bounds (i.e., tails)) are needed.
Recently. Non-asymptotic bounds of performance metrics via stochastic network
calculus with applications to MEC, and industrial 4.0 [Al-Zubaidy] + Short-packet
theory [Polyanskiy, Poor, Popovski]+ edge caching, grant-free NOMA …
Reliability
• Packet duplication [Popovski]
• Multi-connectivity [Fettweis]
• Diversity-oriented approaches (MISO,
STBC, network coding, cooperative
relaying, multi-path, etc)
• Densification (devices, BSs, paths)
• Slicing
Scalability
 Many users information theory! [Guo, Yu]
 Scaling of #users, Blocklengh not well
understood
 Ultra-dense networks for eMBB [Bennis et al.]

10. University of Oulu
10
Ultra-Reliable Communication
(URC)
Low-Latency
Communication
(LLC)
URLLC
Latency (ms)
Reliability (1 − 10−𝑥) Best Effort
1 10 100
-9
-5
-2
0.1
ENABLERS
• Finite Blocklength
• Packet duplication
• HARQ
• Multi-connectivity
• Slicing
• Network Coding
• Spatial diversity
• Slicing
ENABLERS
• Short TTI
• Caching
• Densification
• Grant-free + NOMA
• UAV/UAS
• MEC/FOG/MIST
• Network Coding
• On-device machine
learning
• Slicing
ENABLERS
• Short TTI
• Spatial diversity
• Network Coding
• Caching, MEC
• Multi-connectivity
• Grant-free + NOMA
• On-device machine learning
• Slicing
-
-
-
-
-
-
-
ITS
Factory 2.0
URLLC requirements

11. • Reduce TTI duration (few OFDM symbols per TTI + shortening OFDM symbols via wider subcarrier spacing)+
HARQ RTT so that more HARQ retx are allowed to achieve high reliability
More delay margin to tolerate more queuing delay before deadline
Reducing OFDM symbol duration increases spacing and hence fewer RBs are available in frequency domain causing more queuing
effect
Shorter TTI causes more control overhead  reducing capacity  alleviated via Grant-free transmission
[ TTI and RTT durations must be carefully selected ]
• Grant-free access
• eMBB/URLLC multiplexing
• Network densification
• MEC/FOG/MIST + edge caching, computing and network slicing
• Manufacturing diversity via network coding and relaying: especially for spatial diversity
• Low-earth orbit (LEO) satellites and unmanned aerial vehicles/systems
• Non-orthogonal multiple access (NOMA) w/ grant free scheduling
• Network coding
• Machine learning
2 OFDM symbols = 71.43 microseconds with a spacing of 30KHz
Key Enablers for Latency
*
*

12. • Multi-connectivity and harnessing time/frequency/spatial/RATs diversity + multi-user
diversity to overcome bad fading events
• Multicast, Single frequency networks (SFNs) [?]
• Data (contents and computations) replication
• HARQ + short frame structure, short TTI
• Network slicing
• Network coding
• Reliability of the feedback??
• On-device machine learning
Key Enablers for Reliability

13. Fundamental Trade-offs
• Finite vs. large blocklength
• Spectral efficiency vs. latency
• Energy vs. latency
• Energy expenditures vs. reliability
• Reliability vs. latency
• Reliability vs. rate
• SNR vs. diversity
• Short/long TTI vs. control overhead
• Open vs. closed loop
• Outage capacity-bandwidth-latency
• Channel estimation: training length depends not only on average SNR but also on latency
and reliability budget
• Density of users vs. dimensions (antennas, frequency bands, blocklength size)
When idealized assumptions break down, need to study
sensitivity to:
- Channel reciprocity
- Quasi static fading
- Spatial independence of channel fades

14. SCALE
TAIL
RISK
URLLC
• Antennas, TTI, blocklength
• Millions of devices
• Untractability
• Dynamics
• Uncertainty
• Decision making
• Robustness
• Beyond averages
• Beyond central-limit theorems
• Focus on percentiles
• Extreme and rare events
Mean field Game theory
Machine learning
Mathematical
finance
Extreme
value
theory
Network
calculus
Meta distribution
Rényi entropy
Statistical physics
URLLC = TAIL + RISK + SCALE
Tail behavior of wireless systems
 random traffic demand
 intra/inter-cell interference
 cell edge users, power-limited,
 deep fade
Random matrix
theory
Large-deviation
Theory (LDT)
 LDT valid for LONG delays + CONSTANT
bit rate processes.
• Lyapunov drift theory based on myopic queue-length based
optimization seeks stability (no reliability)

15. University of Oulu
Spectral efficiency - reliability - latency tradeoff is crucial as operators want to know how much eMBB capacity
would be lost to achieve URLLC
CCDF of queuing and/or delay latency
Fraction of users who do [not] achieve rate/latency/reliability targets?
− What are the inherent tradeoffs of rate/latency/reliability?
− Delay violation probability (d,epsilon)
Ergodic Outage capacity
Latency vs. reliability
Outage vs. reliability
SINR vs. reliability
Worst case latency vs. SNR (for different node density)
− impact of power
Worst case latency vs. Node density (for different SNR)
− Impact of tx power
URLLC-specific KPIs
15
Moderate UltraLowUnreliable
Reliability Regime 𝟏 − 𝟏𝟎−𝟗4G 6G

16. 16
Technical (Plumbing) Part
Use cases: MEC, mmWave, mxConn, VR

17. Optimizing Multi-Connectivity (1/2)
Set of 𝑈 UEs and 𝐵 BSs with the capability of
multi-connectivity in a noise-limited
environment.
UEs' and BSs’ power consumption for multi-
connectivity  number of simultaneous
connections
− UEs can reduce power using multi-connectivity.
17
Optimization problem:
− Maximize: 𝜙(𝒙, 𝒉) = (UE SNR) – (Power consumption for multi-connectivity)
− Subject to: all UEs are served by at least one BS
Goal:
− Derive an anlaytical closed form expression for 𝜙∗ = 𝔼 𝒉[𝜙∗(𝒉)] as
𝑈, 𝐵 ⟶ ∞ with a fixed ratio of 𝜁 = 𝑈/𝐵.
Tools:
− Statistical physics: partition sum & replica trick
Connectivity between
UEs and BSs
Channel vector
Optimal utility for a
given set of channels
Scale

18. Optimizing Multi-Connectivity (2/2)
Analytical expression validation via Monte-Carlo
simulations. Optimal values of the objective
function 𝜙∗ = 𝔼 𝒉[𝜙∗
(𝒉)] are compared for
different numbers of BSs and UEs.
18
Reliability in terms of fraction of UEs that satisfy a
given threshold for different number of UEs-BSs
ratios 𝜁 = 𝑈/𝐵 with 𝑈 = 100. Here, the total power
consumption of all BSs in the network remains fixed
few powerful BSs vs. many low-power BSs?

19. Risk-sensitive learning (mmWave) (1/2)
Scenario: small cell network deployment operating at 28 GHz band.
Challenge: channel sensitivity to blockage, lack of LOS
Problem: How does each small cell optimize its own transmit beamwidth
and power in a decentralized manner?
Modeled as a risk-sensitive learning problem to maximize the mean,
while mitigating the variance (mean-variance approach).
Baselines:
− Classical learning: time average utility.
− Baseline 1: transmit beamwidth with fixed maximum transmit power.
19
Risk

20. Risk-sensitive learning (mmWave) (2/2)
CDF of the rate of RSL, CSL, and BL1 for blockage
and NLOS
RSL provides a uniform distribution of rates to every
user.
Reliability versus network density
The fraction of UEs that achieves a
target rate r_0
20

21. Mobile edge computing + URLLC (1/2)
21
While MEC is a key enabler for latency providing latency
guarantees in a network-wide scenario is a challenging problem.
Fundamentally: given traffic arrival rates at users, should the task
be computed locally or remotely?
− Local computing is great but incurs high power consumption.
− Remote task offloading is great but incurs large over the air
transmission and computing delays.
System design
(i) Need a totally distributed solution while smartly leveraging the
cloud;
(ii) Latency and reliability constraints must be taken into account
Tails
C.F. Liu et. al. “Latency and Reliability-Aware Task Offloading and Resource Allocation for Mobile Edge Computing,”
(IEEE GLOBECOM 2017) https://arxiv.org/pdf/1710.00590

22. Mobile edge computing + URLLC (2/2)
22
Leveraging EVT, the statistics of the low-
probability extreme queue length can be
characterized by a general Pareto
distribution (GPD).
Once the estimation of the GPD is
obtained, we can proactively tackle the
occurrence of extreme events.
Consider a multi-user MEC architecture.
With MEC servers, tasks are executed faster
with smaller queuing time.
MEC architecture has less bound violation
events, i.e., higher reliability.
Reliability enhancement more prominent for
higher task arrivals.

23. University of Oulu
Wireless VR + URLLC (1/2)
‒ Tremendous attention towards VR (5G killer app?)
‒ Single VR vs. social/group VR
‒ Unicast vs. multicast VR
‒ A motion-to-photon (MTP) delay < 25 ms is
required to avoid motion sickness.
‒ High data rate of 1 Gbps (or more) needed for a
truly immersive VR experience.
‒ Mutli-connectivity (MC) is an enabler for reliable
VR network.
‒ MmWave can provide such rates, but reliability is a
concern due to blockage and deafness.
MxConn

24. University of Oulu
Wireless VR + URLLC (2/2)
‒ User reliability expressed as the ratio of users with
an average transmission delay below a delay
threshold.
‒ Multiconnectivity (MC) ensures all users are within
the delay budget even with low number of servers.
‒ Reliability: how often transmission delay threshold
(10 ms) is violated?
‒ A higher number of servers (i.e., BSs) leads to lower
delay violation.
‒ MC guarantees reliable service delay at different
network conditions.

25. Conclusions
URLLC is one of the most important building blocks of 5G and beyond..
A principled URLLC framework is sorely lacking
More work is needed in terms of fundamentals and system design
End-to-end URLLC is what matters instead of looking at every sub-part
separately
− An AI-driven approach may be the way to go but HOW?
This presentation paves the way for more work to come…… 25
features outputs

26. 26
Thanks to all those who provided their feedback
and inputs to this presentation