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Predictable URLLC: Algorithms and Prototyping
Hongwei Zhang
[email protected], +1 515 294 2143
http://www.ece.iastate.edu/~hongwei
Ultra-Reliable, Low-Latency Communication (URLLC)
telesurgery
transportation
public safety
agriculture industrial systems
For high-reliability
Accurate channel estimation: by adding more resources to the pilot and
using advanced channel estimation techniques
Leveraging diversity in time, frequency, antenna, and space
For low-latency
Pipelined processing of pilot, control, and data parts of frames
Frame structure for small TTI (transmission time interval)
Reservation-based scheduling
H. Ji, S. Park, J. Yeo, Y. Kim, J. Lee, and B. Shim, “Ultra Reliable and Low Latency Communications in 5G Downlink: Physical Layer Aspects,” IEEE Wireless Communications, vol. 25, no. June, pp. 124–130, 2018.
URLLC: Single Link/Cell Techniques
Signal-Map-Based Protocol Signaling
Single-Hop Communiation
PRK Model Parameterization
Rea
l-T
ime
Cap
acit
y M
od
el
Ph
ysical P
rocess:
wireless sig
nal p
rop
agatio
n, v
ehicle m
ob
ility
Wireless
Networking
Cyber Domain Physical Domain
topology,
real-time
capacity region
real-time capacity requirements,
vehicle movement prediction
mobility
mobility,
signal prop.
Networked
Control Control-Oriented Real-time Capacity
Allocation
Networked & Distributed Control
Networked Estimation
Coordination Topology
Adaptation
Multi-Hop &Cellular Communication
human factor
Predictable URLLC for Co-Design of Networking and Vehicle Control
Challenges of Predictable URLLC in Networks
Wireless channel fading & interference
Application traffic dynamics, diverse & time-varying QoS requirements
Node/vehicle mobility
Infrastructure & infrastructure-less settings
Co-Channel Interference Control: A Fundamental Problem in Wireless Communication
Open problem for over 40 years
ALOHA protocol considered interference from concurrent transmitters
(1970)
Hidden terminal issue first identified by Dr. Leonard Kleinrock (1975)
Lack of field-deployable approaches to predictable interference control
Wireless Interference Model: A Basis for Interference-Oriented Scheduling
Predicts whether a set of concurrent transmissions may interfere with
one another
Two commonly-used interference models
Protocol model
Physical model
Protocol Interference Model
Interference range = K communication range
RTS-CTS based approach implicitly assumed K =1
Strengths
Local, pair-wise interference relation
Good for distributed protocol design
Weakness
Approximate model
May well lead to low performance
S R C
),( range ceInterferen , RSDKI RS Exclusion region
Physical Interference Model
Strength
High fidelity: based on communication theory
Weaknesses
Interference relation is non-local and combinatorial
Not suitable for distributed protocol design in dynamic, uncertain settings
A transmission is successful if the signal-to-interference-plus-noise ratio (SINR) is above a certain threshold
𝑃 𝑆𝑖, 𝑅𝑖
𝑁𝑖 + 𝑃 𝑆𝑗 ,𝑅𝑖𝑗≠𝑖
≥ 𝛾
𝑆𝑖
𝑅𝑖 ?
Suitable for designing distributed protocols:
Both signal strength and link reliability are locally measurable
K is locally controllable
Given a transmission from S to R, a concurrent transmitter C is regarded as not interfering with the reception at R iff.
Physical-Ratio-K (PRK) Interference Model
RSTRSK
RSPRCP
,,,
,,
S R
C
RSTRSK
RSP
,,,
,
signal strength from S to R
function of required PDR TS,R
Interference power from C to R
H. Zhang, X. Che, X. Liu, X. Ju, “Adaptive Instantiation of the Protocol Interference Model in Wireless Networked Sensing and Control”, ACM Transactions on Sensor Networks, 10(2), 2014
A Major Challenge of PRK-Based Scheduling
Impact of network and environmental conditions
Lack closed-form characterization + uncertainty
On-the-fly instantiation of the PRK model parameter ?
RSTRSK,,,
S R
C
RSTRSK
RSP
,,,
,
H. Zhang, X. Liu, C. Li, Y. Chen, X. Che, F. Lin, L. Wang, G. Yin, “Scheduling with Predictable Link Reliability for Wire less Networked Control”, IEEE Transactions on Wireless Communications (TWC), 16(9), 2017
Predictable Link Reliability in Infrastructure-less, Distributed PRK-based Scheduling (PRKS)
Predictable link PDR through localized PRKS model adaptation
Concurrency and spatial reuse statistically equal or close to that in state-of-the-art centralized scheduling
Current Practice (1): Improve Reliability by Retransmission
Significantly longer delay in existing protocols
due to retransmission
Current Practice (2): Improve Reliability by Reducing Traffic Load
Significantly lower throughput in existing protocols
due to low utilization of channel capacity
UCS (Unified Cellular Scheduling): Cellular with D2D
tx.
status
Control Plane Data Plane
link reliability feedback
parameter
signal map
set of concurrent
transmission links
Signal Map
Maintenance
Multi-Channel ONAMA Scheduling
Protocol Signaling
Link Reliability
Estimation
Multi-Channel
PRK Modeling
Data Packet
Transmission
RSTRSK,,,
samples of signal
power attenuation
Mode Selection
BS & UE FunctionNotation: BS Function
Yuwei Xie, Hongwei Zhang, Pengfei Ren, “Unified Scheduling for Predictable Communication Reliability in Industrial Cellular Networks”, IEEE International Conference on Industrial Internet (ICII), 2018
Implemented
& Evaluated
using OAI!
Predictable URLLC: Network Algorithmics
PRK-Based scheduling for predictable interference control
Ad hoc and cellular with D2D architectures, static and mobile networks
Open challenges
From predictable mean communication reliability to predictable per-
packet reliability (e.g., joint scheduling and power control)
From reliability guarantee to timeliness guarantee
Wireless communication is probabilistic in nature
Probabilistic real-time communication framework
Real-time admission control & scheduling
On-the-fly real-time capacity modeling & application adaption
Prototyping & App Integration: CyNet for Smart Ag and Transportation
ISU main campus
Curtiss Farm InTrans @
Research Park
CyNet for Smart Ag
Plant Orientation
1,000 camera arrays UGVs Plant tatoo UAVs
CyNet for AR/VR-based Multi-Mode CAT Emulator
Current Status
Lab/indoor testing
Field deployment in July, 2019
Desired Features of Prototyping Hardware & Software Platforms
Motes & TinyOS for wireless sensor networks in early 2000s
High performance for application integration
At-scale, high-fidelity simulation +
Easy transition across simulation/emulation/real-world-deployment
Ease of learning & use, from research engineers to classrooms
Design & user documentation, coding comments, recorded tutorials,
software architecture & code evolution
Predictable URLLC: Algorithms and Prototyping
Hongwei Zhang
Iowa State University
[email protected], +1 515 294 2143
http://www.ece.iastate.edu/~hongwei