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