Predictable URLLC: Algorithms and Prototyping

Hongwei Zhang

hongwei@iastate.edu, +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

hongwei@iastate.edu, +1 515 294 2143

http://www.ece.iastate.edu/~hongwei