Achieving Achieving LOwLOw--LAtencyLAtency in in

Wireless Communications Wireless Communications

Project Manager: Raymond Knopp , Eurecom

Tel: +33 (0)4 93 00 81 54

Email: raymond.knopp@eurecom.fr

Project Website: http://www.ict-lola.eu

Wireless Communications Wireless Communications

The research leading to these results has received funding from the European

Community's Seventh Framework Programme under grant agreement n° 248993

PartnersPartners

2 – TCF (F)

3 – TUV (A)

4 – LIU (S)

7 – MTS (SRB)

2

1 – EURE (F)

2 – TCF (F)

5 – AT4 (E)

6 – EYU (SRB)

OutlineOutline

� About LOLA

� M2M System Architecture Based on LTE/LTE-A

� Realtime M2M Application Scenarios and Analysis

� Traffic Characteristics � Traffic Characteristics

� Avenues for L1/L2 latency improvements

� Testbeds

� Application Scenarios

� Conclusion

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ABOUT LOLAABOUT LOLA

4

Why LOLA?Why LOLA?

� Two emerging massive applications require low latency

� High-performance online gaming

� M2M/Sensory data communications

5

Why LOLA?Why LOLA?

6

About LOLA?About LOLA?

� Low layer procedures for low-latency communications in

LTE/LTE-A

� Reduce energy consumption for M2M devices

� Co-existence of M2M/gaming traffic with conventional services� Co-existence of M2M/gaming traffic with conventional services

� Public-safety / professional networks

� Convergence (air-interface) with LTE/LTE-A

� Low-latency is crucial and hard to achieve in mesh networks due to

multihop nature

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About LOLA?About LOLA?

� Delay Analysis in MTS Network for gaming and M2M applications

� The client is directly attached via the access networks to the server itself

� Latency can be improved

� By improving transmission latency at the access layer

� By providing service locally closer to the client(s)

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3 Objectives3 Objectives

• Fundamental

� Characterization of traffic for M2M/Online Gaming

� Innovation with respect to PHY/MAC procedures in support of low-latency applications

• Experimental

� Rapid prototyping on three carefully chosen testbeds based on existing open-source technologyopen-source technology� Testbed 1 : Large-scale system emulator and traffic measurement testbench

(PHY/MAC/L3) using real applications and Network

� Testbed 2 : Real-time Link validation platform (PHY)

� Testbed 3 : CHORIST FP6 demonstrator (rapidly-deployable mesh, full-system demonstrator with mini field-trial)

• Standardization

� Input to 3GPP and M2M

� Two Rel-10 items : Latency reductions for LTE, Relays for LTE

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LOLA Project StructureLOLA Project Structure

� Duration:

01/2010 –

12/2012

� Funding scheme:

STREPSTREP

� Total Cost: €

4,177,711

� EC Contribution:

€ 2,628,323

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M2M SYSTEM ARCHITECTURE & M2M SYSTEM ARCHITECTURE &

APPLICATION SCENARIOSAPPLICATION SCENARIOS

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M2M Components M2M Components

� M2M Capillary� Incorporating smart devices and their gateways using a number of short or

wide range communication technologies, reusable across a number ofapplication domains

� M2M Access

� Giving adequate support for M2M services

� M2M Core� M2M Core

� Providing interconnectivity and extendable by relevant M2M services(registry, request analyzer, control), 3rd party services (e.g. location,charging, processing of data) and LTE services (e.g. AAA, IMS)

� M2M Application

� Including domain specific processing and visualization of information, andthe end user applications interacting with the smart devices through acommon platform

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M2M System ArchitectureM2M System Architecture

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AutoAuto--pilot for Intelligent Transport Systempilot for Intelligent Transport System

� M2M Capillary: 4.5 – 25 ms

� M2M Application : 1 – 3 m� M2M Access : 57 – 378 ms

� M2M core : 15 – 150 ms

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RealReal--time M2M Application Scenarios and time M2M Application Scenarios and

AnalysisAnalysis

� Virtual Race in Machine Gaming� M2M Capillary : 6 – 10 ms

� M2M Application : NA

� M2M Access : 112 – 748 ms

� M2M core : 42 – 122 ms

� Smart Environment in Ambient assisted living� M2M Capillary : 6 – 10 ms� M2M Capillary : 6 – 10 ms

� M2M Application : NA

�M2M Access : 112 – 748 ms

� M2M core : 42 – 122 ms

� Sensor-based Alarm and Event-Detection

� Mobile Surveillance System for Security

� Life support system for health monitoring

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Gaming ScenariosGaming Scenarios

Scenario Latency

Bottlenecks

Related

Parameters

Latency Target Current

Latency

� Interactive multiplayer gaming on mobile devices

� Medium data rates (transfer of graphics and motion primitives) : < 5 Mbits

� Sparse UL traffic (scheduling issues)

� 10-20 ms round-trip latency (tough even for LTE under loaded conditions)

Bottlenecks Parameters Latency

On-line Gaming Network delay User density80 ms

(U-plane, 1 way)

Depends on

access techn.

Gaming on

Sport Events

PHY/MAC layers,

Network layer

(external IP),

Application layer

Cell capacity, User

density

25 ms

(U-plane)40 ms

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M2M ScenariosM2M Scenarios

Scenario Latency

Bottlenecks

Related

Parameters

Latency

Target

Current

Latency

Auto Pilot

PHY/MAC layers,

Network layer (external

IP), Application layer

Cell capacity,

Handover,

Network density

30 ms

(U-plane)46 ms

Sensor-based

Alarm or Event

Detection

PHY/MAC layers if

dedicated PHY link

Power consumption,

Network capacity,

Reliability

2-12 ms

(1 way,

U- and C-

planes)

Depends on

technology

On-line No particular or On-line

interaction for life

support systems

C-plane time for setting

up the data channel

No particular or

outstanding influencing

parameter

< 500 ms NA

M2M GamePHY/MAC layers,

Network layer

User density,

Cell capacity,

Cell load,

Handover

55 ms

(U-plane)75 ms

Team Tracking

Connection

establishment,

Network delay,

Queue management at

application layer

Link outage,

Robustness,

Mobility

1 s

(U- and C-

planes)

1 s – 10 s

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Human Remote Control, Alarm and Event Detection Human Remote Control, Alarm and Event Detection

Scenarios Scenarios

Scenario Latency

Bottlenecks

Related

Parameters

Latency Target Current

Latency

Remote

Medicine

PHY/MAC layers, Network

layer (external IP),

Application layer

User density,

Handover,

Throughput (for

video)

200 ms

(U-plane)> 200 ms

PHY/MAC layers (multi-hop < 100 ms

Remote Control

PHY/MAC layers (multi-hop

links, relays),

Network layer

Throughput,

Reliability

< 100 ms

(U-plane +

displaying)

Hundreds of

ms

Ad hoc Public

Safety

Communications

PHY/MAC layers (multi-hop

links, relays, priority

management)

Throughput,

Reliability

300 ms – 1 s (call

setup)

< 500 ms

(voice)

Depends on

topology and

service

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V2V and V2I over LTE ScenarioV2V and V2I over LTE Scenario

� LTE for 802.11p-like

applications

�V2V and V2I

�Autopilot

Infrastructure InternetUBR : roadside entity

�

� Need short round-trip

latency for control apps

� Need short (random)

access times for security

apps

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LTE Access Network

(two-hop V2V)

UBRUBRAccess

Point

V2I communications

Sensor Network ScenarioSensor Network Scenario

� Fundamental study

� Correlated multiple-access

� Hybrid digital/analog transmission

(analog sources)

� Low source/channel bandwidth ratio

� Event-driven traffic

� Feedback reduces latency� Feedback reduces latency

� Mapping to LTE

� Dominant UL

� PRACH for low-latency?

� Frame configuration?

� Specific example for Testbed B

� Use of PRACH for Low-latency protocol

test for multiuser scenario

� Fast PRACH detection

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Distributed Relaying/HARQ ScenarioDistributed Relaying/HARQ Scenario

� Fundamental study

� Code/HARQ /quantization design

for distributed transmission

� Study Error-exponents with

feedback (effect on code

complexity/latency)

eNB

RS1

UE2

UE1

complexity/latency)

� Implementation on Testbed C

� Yield latency Improvement over

layer-3 based forwarding (two

relays, single source)

� Potential Input to LTE-A

relay work item

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RS2

UE2

TRAFFIC CHARACTERISTICS &TRAFFIC CHARACTERISTICS &

L1/L2 LATENCY IMPROVEMENTS L1/L2 LATENCY IMPROVEMENTS

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Traffic Measurement and Modeling Traffic Measurement and Modeling

Defined

Scenarios

Trace

Recording

……

Modeling

Traffic

Gaming

M2M

…

LTE /Gaming

Mesh / M2M

Definition of

QoS, MOS

Gaming

M2M

…

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Trace Driven

Generator

LTE /Gaming

Mesh / M2M

…

Model Driven

Generator

Gaming

M2M

…

Performance

Metrics

Gaming

M2M

…

Traffic Characteristics Traffic Characteristics

� Collision avoidance system for Intelligent Transport System

� All the sensors (car, road sensors) send the information to the M2M backend

system within the predefined period

� Event-driven, short bursts emergency signals from the M2M backend to the M2M

devices (warning and actuation commands)

� Virtual Race in Machine Gaming

� periodic CBR data transmission between M2M devices with increasingly shorter� periodic CBR data transmission between M2M devices with increasingly shorterperiods as the end of the game is getting closer

� Smart Environment in Ambient Assisted Living

� Event driven, low data rate burst of control messages amongst M2M devicesand/or from M2M users;

� Event-driven, high data rate burst of data amongst M2M devices/gateways and/orto M2M servers/users.

� Traffic pattern varies depending on application scenarios but the general

traffic trends follows the ON-OFF traffic model

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L1/L2 Latency IssuesL1/L2 Latency Issues

� Low duty-cycle sensors on LTE

� Fast time/freq synch for minimal UE power consumption

�Efficient eNB signaling for dense sensor nets

�Low-latency/overhead protocols to maximize aggregate spectral efficiency

� Scheduling algorithms for � Scheduling algorithms for

� Small packets on the UL for dense M2M/sensor networks to allow coexistence with conventional applications

�Uplink traffic should be scheduled via special random-access procedures

�Event-driven traffic scheduling policies (again UL)

� Scheduling in two-hop networks (i.e. with relays, where aggregate traffic seen by relays has to be scheduled)

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L1/L2 Latency ImprovementsL1/L2 Latency Improvements

� Contention-based random access on the LTE/LTE-A in uplink� UL latency in FDD mode is 7-11ms (SR periodicity 5ms) => 7-11 times latency

reduction for M2M/sensor applications with 1ms contention-based random access

� Further 2x reduction for small packets with 500us TTI duration

� Protection and transmission of more detailed HARQ feedback� A 10% latency reduction is foreseen

� Estimation of missing mutual information (SNR/statistical models)

� Adaptive Modulation and Coding Scheme/Transmission Power� Increase power of delayed flows� Increase power of delayed flows

� Use more robust MCS for HARQ retransmissions

� Send more redundancy in unloaded conditions

� Up to 10% latency gain can be achieved with these techniques

� Coordinated Multi-Point Joint Processing/Joint Transmission� Improves throughput → improves latency for non-sparse traffic sources (e.g.

video surveillance cameras)

� Reduces nº retransmissions for cell-edge users → improves latency

� Carrier Aggregation aware scheduling� Unloaded carrier components can be scheduled for delayed users

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TESTBEDSTESTBEDS

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TestbedTestbed 1 : Low1 : Low--layerProtocolslayerProtocols and applicationsand applications

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Testbed 2 : PHY proceduresTestbed 2 : PHY procedures

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Software Module

L3 Layer

L2 LayerL1 Layer

NAS

RRC

PDCP

RLC

MAC

PHY

Hardware

Module

RadioFrequ

ency

ModuleProcessing

Module

TestbedTestbed 3 : FP6 CHORIST MESH Demonstrator3 : FP6 CHORIST MESH Demonstrator

Backhaul link

Location tracking

Rapidly-DeployableFemto-mesh routers

Local CommandCenter

IP Network

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Ground Sensor network

UAV sensors

CONCLUSIONCONCLUSION

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ConclusionConclusion

� LOLA is just beginning

� Scenarios identified

� L1/L2 latency bottlenecks and improvements identified

� Initial studies on M2M and gamming traffic characteristics

�Potential contributions to 3GPP identified�Potential contributions to 3GPP identified

� Key elements for prototyping/demonstration under

definition

� Low-latency UL on LTE (scheduling, PRACH usage)

�Two-hop relaying scenarios (HARQ and AMC issues)

�Traffic modeling for M2M/Gaming to tune L2 procedures

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