Copyright © 2010 National Institute of Information and Communications Technology. All Rights Reserved 1 R&D and Standardization Activities on Distributed.

Copyright © 2010 National Institute of Information and Communications Technology. All Rights Reserved1

R&D and Standardization ActivitiesR&D and Standardization Activitieson Distributed Spectrum Sensingon Distributed Spectrum Sensing

for Dynamic Spectrum Accessfor Dynamic Spectrum Access(Invited Paper)(Invited Paper)

R&D and Standardization ActivitiesR&D and Standardization Activitieson Distributed Spectrum Sensingon Distributed Spectrum Sensing

for Dynamic Spectrum Accessfor Dynamic Spectrum Access(Invited Paper)(Invited Paper)

Ha Nguyen Tran, Yohannes D. Alemseged, Ha Nguyen Tran, Yohannes D. Alemseged, Chen Sun, Hiroshi HaradaChen Sun, Hiroshi Harada

New Generation Wireless Communication New Generation Wireless Communication Research Center, NICT, JapanResearch Center, NICT, Japan

The 3rd International Conference on Communications and ElectronicsICCE 2010, Aug 11-13, Nha Trang, Viet Nam.

OutlineOutline

Dynamic Spectrum Access and Spectrum Sensing

Distributed spectrum sensing Use cases

Some related research topics

Performance evaluation method

Cross-layered MAC design

Sensing database

Interface for Sensing information exchange and standardization activities

Implementation examples

The XG Vision, RFC v2.0DARPA neXt Generation (XG)

Dynamic Spectrum Access (DSA)Dynamic Spectrum Access (DSA)

Background: fixed spectrum allocation regulation

DSA: “the real-time adjustment of spectrum utilization in response to changing circumstances and objectives”

Radio environment

Radio environment

SensingDecisionmaking

ReconfigurationSpectrumutilization

Spectrum SensingSpectrum Sensing

Single device sensing

H0: target channel is freeH1: target channel is occupiedh: communication channel, n: noise, s: source signal, r: received signal

Multiple device sensingUtilizes results from multiple sensorsBetter sensing quality

Minimize shadow, fading effectsMinimize sensing timeImprove detection probability

s(t)

r(t)

h Sensor

s1

s2

s3

Distributed sensing scenariosDistributed sensing scenarios

Parallel sensing

Sensing with gatewayData fusion

Sensor selection

Use casesUse cases

Cognitive radio with distributed sensorsPeer to peer sensing: closely located CR negotiate and exchange sensing informationCooperative sensing: CR collects and analyses independent sensing information from multiple sensorsCollaborative sensing: multiple sensors exchange information and may perform local processing before providing information to CRSelective sensing: one sensor is selected to perform sensing

Peer to peer cognitive radiosCR utilizes sensing information of its own sensor and sensors in other CRFor short range communication (without BS), adhoc network, and power efficient communication

Research topics (1)Research topics (1)

Performance evaluation of distributed sensing use cases

AssumptionSensing method: energy detectionFalse alarm rate 0.004Rayleigh fading environment3 distributed spectrum sensors

Results (detection probability Pd)Cooperative sensing: Pd increased from 0.02 to 0.3Collaborative sensing: Pd increased from 0.02 to 0.4

Ref: C. Sun et al., “Spectrum sensing architecture and use cases study: Distributed sensing over Rayleigh fading channels”, IEICE Trans. Comm., Dec. 2009.

Research topics (2)Research topics (2)

Media access scheme for sensing information exchange

Assumption: spectrum sensors and cognitive radios exchange sensing information over a common wireless channelTopic: how to design an efficient MAC protocol?Results

Scheduled scheme is better than non-scheduled (e.g. collision based) schemeCross-layered design (with considering of both media access parameters and sensing parameters) provides best performance

Ref. Yohannes et al., “A Study on Media Access Scheme for Distributed Spectrum Sensing”, IEICE SR TR, Mar. 2010.

Research topics (3)Research topics (3)

A sensing database (SDB) for collecting and managing sensing information

Assumption: sensing information is collected into SDB for further processing in order to provide better aggregated sensing results.Topic: effect of the local database to network traffic and to the improvement of sensing qualityResults: with the deployment of SDB

Traffic to global network is reducedNumber of sensing activities is reduced by 20%Sensing quality is 1.3 higher

Ref: H.N. Tran et al., “Distributed sensing database for cognitive radio systems”, IEICE Trans. Comm. (submitted)

Interface for sensing information exchangeInterface for sensing information exchange

Background: to share sensing information among cognitive radio devices in order to improve sensing qualityTarget: to design a logical interface and to define the data structure for sensing information exchange

Logical interface is defined as a set of Service Access Points (SAP)Data structure is defined with data name, data type, data size and range of possible values

Outcome: contribution to the IEEE P1900.6 standard

About IEEE P1900.6 Working GroupAbout IEEE P1900.6 Working Group

Established: 2008Scope: “defines the information exchange between spectrum sensors and their clients in radiocommunication systems. The logical interface and supporting data structures used for information exchange are defined abstractly without constraining the sensing technology, client design, or data link between sensor and client.”Current status

first round of sponsor ballot: May 2010Under preparation for the sponsor ballot recirculation

Contribution from NICTMore than 50% of technical contributionsServes as WG editor and secretary4 active members with voting rights

The next slides introduce some major technical contributions from NICT

Logical entities and interfacesLogical entities and interfaces

Logical entitiesCognitive Engine (CE)

Data Archive (DA)

Sensor (S)

Instances of interfaceCE/DA-S

CE-CE/DA

S-S

Reference modelReference model

Application SAPTo control and obtain sensing results for application purposes

Measurement SAPTo control the measurement module and to acquire measurement data

Communication SAPTo transport sensing information

For each SAP, a set of primitives is defined

Information categoryInformation category

Information categorySensing information

Measurement data e.g. frequency band, channel condition, timestamp, local decision results etc.

Sensing control informationControl of sensing activity e.g. sensing duration, target performance (threshold), priority control, power control etc.

Sensor informationSensor specifications, sensor capability, sensor id etc.

Regulatory informationSensing frequency, sensing duration, sensitivity level etc.

Example of data structureExample of data structure

Implementation examples (1): Spectrum sensorImplementation examples (1): Spectrum sensor

Co-located sensor(data transport issupported byinternal bus)

Remote sensor(data transport issupported bywired/wirelessprotocols)

Implementation examples (2): Data archiveImplementation examples (2): Data archive

DA is implemented as a sensing database

Collect and manage sensing information

Relay regulatory information from regulatory DB

Provide information about secondary users

Sensing database interface

Implementation scenario

ConclusionsConclusions

This presentation introduced R&D activities in distributed spectrum sensing for dynamic spectrum access

Use cases description

Performance evaluation for each use case

MAC design

Database design

This presentation introduced standardization activities in logical interface and data structure for sensing information exchange which supports the distributed spectrum sensing approach.