Building the foundations of Ultra-RELIABLE and Low-LATENCY Wireless

Communication

ATale ofRisk atScale

Dr. Mehdi BennisCentre for Wireless Communications

University of Oulu, Finland

1

Tail

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

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-basedapproaches (average throughput, average response time, etc.) towards a tail/risk/scale-

centric design.

A (short) historical perspective of URLLC

1948: Reliable communication has been a fundamental problem in informationtheory since Shannon’s landmark paper showing the possibility to communicatewith vanishing probability of error at non-zero rates.

Error exponents via reliability functions provide insights by characterizing theexponential 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 shortpacket communication theory).

An obsession since the 80’s towards spectral efficiency until the advent of mission-critical applications (e.g., industry 4.0).

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

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?

6Source: URLLC 2017 event, Nov 14 @ London

4G vs. 5G (in a nutshell)

7

4G 5G

important crucial

Long (MBB) Short (URLLC)Long (eMBB)

Throughput-centricNo 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 6GHzA few users/devices

Sub-and-Above 6GHz(URLLC @sub-6GHz)

billion devices

Metadata, control channel

Packet size

Design

Reliability

Rate

Latency

Queue size

Delay violationprobability

Frequency bands

Scale

eMBB can

still be average based*

*

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, queuingdelay, violation probability and proactive + droppingprobability

Reliability (3GPP): successfully transmit 32byte messageover the 5G radio Interface within 1ms with a successprobability 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

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-limitedlink 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-grainedmetrics (delay distribution and probabilistic bounds (i.e., tails)) are needed.

Recently. Non-asymptotic bounds of performance metrics via stochastic networkcalculus 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, cooperativerelaying, multi-path, etc)

• Densification (devices, BSs, paths)• Slicing

Scalability

Many users information theory! [Guo, Yu]

Scaling of #users, Blocklengh not wellunderstood

Ultra-dense networks for eMBB [Bennis et al.]

University of Oulu

10

Ultra-Reliable Communication

(URC)

Low-LatencyCommunication

(LLC)

URLLCLatency (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

• 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 effectShorter 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*

*

• 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

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 studysensitivity to:- Channel reciprocity- Quasi static fading- Spatial independence of channel fades

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 basedoptimization seeks stability (no reliability)

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

Technical (Plumbing) Part

Use cases: MEC, mmWave, mxConn, VR

Optimizing Multi-Connectivity (1/2)

Set of 𝑈 UEs and 𝐵 BSs with the capability ofmulti-connectivity in a noise-limitedenvironment.

UEs' and BSs’ power consumption for multi-connectivity number of simultaneousconnections

− 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∗ = 𝔼𝒉[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

Optimizing Multi-Connectivity (2/2)

Analytical expression validation via Monte-Carlosimulations. Optimal values of the objectivefunction 𝜙∗ = 𝔼𝒉[𝜙

∗(𝒉)] are compared fordifferent numbers of BSs and UEs.

18

Reliability in terms of fraction of UEs that satisfy agiven threshold for different number of UEs-BSsratios 𝜁 = 𝑈/𝐵 with 𝑈 = 100. Here, the total powerconsumption of all BSs in the network remains fixedfew powerful BSs vs. many low-power BSs?

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 beamwidthand 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

Risk-sensitive learning (mmWave) (2/2)

CDF of the rate of RSL, CSL, and BL1 for blockageand NLOS

RSL provides a uniform distribution of rates to everyuser.

Reliability versus network density

The fraction of UEs that achieves a target rate r_0

20

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

Mobile edge computing + URLLC (2/2)

22

Leveraging EVT, the statistics of the low-probability extreme queue length can becharacterized by a general Paretodistribution (GPD).

Once the estimation of the GPD isobtained, we can proactively tackle theoccurrence 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.

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

University of Oulu

Wireless VR + URLLC (2/2)

‒ User reliability expressed as the ratio of users withan average transmission delay below a delaythreshold.

‒ Multiconnectivity (MC) ensures all users are withinthe 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.

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-partseparately

− 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

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