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Building the foundations of
Ultra-RELIABLE and Low-LATENCY Wireless
Communication
ATale of Risk at Scale
Dr. Mehdi Bennis
...
2
Table of Contents
Motivation
Latency and reliability definitions
State-of-the-art (SOTA): Gist of it
Key enablers for lo...
University of Oulu
Motivation
Testimonials:
”Obtaining reliability plots requires time-consuming Monte-Carlo simulations”
...
A (short) historical perspective of URLLC
1948: Reliable communication has been a fundamental problem in information
theor...
URLLC is use case dependent
AR/VR/XR
Factory 2.0
V2X
eMBB
Robotics, UAVs
CPS
Telemedicine
etc
URLLC Scenarios:
- Hyperloca...
University of Oulu
Is it really possible to have both low latency and ultra reliable networks?
Back to the basics: how do ...
4G vs. 5G (in a nutshell)
7
4G 5G
important crucial
Long (MBB) Short (URLLC)
Long (eMBB)
Throughput-centric
No latency/rel...
End-to-end (E2E) latency: scheduling delay+ queuing delay+
transmission delay+ receiver-side processing and decoding
delay...
Significant contribution towards understanding ergodic capacity for a few users and average queuing
performance of wireless...
University of Oulu
10
Ultra-Reliable Communication
(URC)
Low-Latency
Communication
(LLC)
URLLC
Latency (ms)
Reliability (1...
• Reduce TTI duration (few OFDM symbols per TTI + shortening OFDM symbols via wider subcarrier spacing)+
HARQ RTT so that ...
• Multi-connectivity and harnessing time/frequency/spatial/RATs diversity + multi-user
diversity to overcome bad fading ev...
Fundamental Trade-offs
• Finite vs. large blocklength
• Spectral efficiency vs. latency
• Energy vs. latency
• Energy expend...
SCALE
TAIL
RISK
URLLC
• Antennas, TTI, blocklength
• Millions of devices
• Untractability
• Dynamics
• Uncertainty
• Decis...
University of Oulu
Spectral efficiency - reliability - latency tradeoff is crucial as operators want to know how much eMBB...
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 of
multi-connectivity in a noise-limited
en...
Optimizing Multi-Connectivity (2/2)
Analytical expression validation via Monte-Carlo
simulations. Optimal values of the ob...
Risk-sensitive learning (mmWave) (1/2)
Scenario: small cell network deployment operating at 28 GHz band.
Challenge: channe...
Risk-sensitive learning (mmWave) (2/2)
CDF of the rate of RSL, CSL, and BL1 for blockage
and NLOS
RSL provides a uniform d...
Mobile edge computing + URLLC (1/2)
21
While MEC is a key enabler for latency providing latency
guarantees in a network-wi...
Mobile edge computing + URLLC (2/2)
22
Leveraging EVT, the statistics of the low-
probability extreme queue length can be
...
University of Oulu
Wireless VR + URLLC (1/2)
‒ Tremendous attention towards VR (5G killer app?)
‒ Single VR vs. social/gro...
University of Oulu
Wireless VR + URLLC (2/2)
‒ User reliability expressed as the ratio of users with
an average transmissi...
Conclusions
URLLC is one of the most important building blocks of 5G and beyond..
A principled URLLC framework is sorely l...
26
Thanks to all those who provided their feedback
and inputs to this presentation
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Building the foundations of Ultra-RELIABLE and Low-LATENCY Wireless Communication

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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
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Published in: Technology

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

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 16 Technical (Plumbing) Part Use cases: MEC, mmWave, mxConn, VR
  17. 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. 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. 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. 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. 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. 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. 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. 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. 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. 26 Thanks to all those who provided their feedback and inputs to this presentation

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