1. Copyright © 2010 National Institute of Information and Communications Technology. All Rights Reserved 1 R&D and Standardization Activities on Distributed Spectrum Sensing for Dynamic Spectrum Access (Invited Paper) Ha Nguyen Tran, Yohannes D. Alemseged, Chen Sun, Hiroshi Harada New Generation Wireless Communication Research Center, NICT, Japan The 3 rd International Conference on Communications and Electronics ICCE 2010, Aug 11-13, Nha Trang, Viet Nam.

2. Outline 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

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

4. 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 environmen t Sensing Decision making Reconfiguration Spectrum utilization

5. Spectrum Sensing Single device sensing H0: target channel is free H1: target channel is occupied h: communication channel, n: noise, s: source signal, r: received signal Multiple device sensing Utilizes results from multiple sensors Better sensing quality Minimize shadow, fading effects Minimize sensing time Improve detection probability s(t) r(t) h Sensor s1 s2 s3

6. Distributed sensing scenarios Parallel sensing Sensing with gateway Data fusion Sensor selection

7. Use cases Cognitive radio with distributed sensors Peer to peer sensing: closely located CR negotiate and exchange sensing information Cooperative sensing: CR collects and analyses independent sensing information from multiple sensors Collaborative sensing: multiple sensors exchange information and may perform local processing before providing information to CR Selective sensing: one sensor is selected to perform sensing Peer to peer cognitive radios CR utilizes sensing information of its own sensor and sensors in other CR For short range communication (without BS), adhoc network, and power efficient communication

8. Research topics (1) Performance evaluation of distributed sensing use cases Assumption Sensing method: energy detection False alarm rate 0.004 Rayleigh fading environment 3 distributed spectrum sensors Results (detection probability Pd) Cooperative sensing: Pd increased from 0.02 to 0.3 Collaborative 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.

9. Research topics (2) Media access scheme for sensing information exchange Assumption: spectrum sensors and cognitive radios exchange sensing information over a common wireless channel Topic: how to design an efficient MAC protocol? Results Scheduled scheme is better than non- scheduled (e.g. collision based) scheme Cross-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.

10. 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 quality Results: with the deployment of SDB Traffic to global network is reduced Number 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)

11. Interface for sensing information exchange Background: to share sensing information among cognitive radio devices in order to improve sensing quality Target: 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

12. About IEEE P1900.6 Working Group Established: 2008 Scope: “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 2010 Under preparation for the sponsor ballot recirculation Contribution from NICT More than 50% of technical contributions Serves as WG editor and secretary 4 active members with voting rights The next slides introduce some major technical contributions from NICT

13. Logical entities and interfaces Logical entities Cognitive Engine (CE) Data Archive (DA) Sensor (S) Instances of interface CE/DA-S CE-CE/DA S-S

14. Reference model Application SAP To control and obtain sensing results for application purposes Measurement SAP To control the measurement module and to acquire measurement data Communication SAP To transport sensing information For each SAP, a set of primitives is defined

15. Information category Sensing information Measurement data e.g. frequency band, channel condition, timestamp, local decision results etc. Sensing control information Control of sensing activity e.g. sensing duration, target performance (threshold), priority control, power control etc. Sensor information Sensor specifications, sensor capability, sensor id etc. Regulatory information Sensing frequency, sensing duration, sensitivity level etc.

16. Example of data structure

17. Implementation examples (1): Spectrum sensor Co-located sensor (data transport is supported by internal bus) Remote sensor (data transport is supported by wired/wireless protocols)

18. Implementation 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

19. Conclusions 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.