An Optimal Sensing Framework based on Spatial RSS-profile in Cognitive Radio Networks

1. An OptimalSensing Framework based on Spatial RSS-profile in Cognitive Radio Networks Alexander W. Min and Kang G. Shin Real-Time Computing Laboratory (RTCL) The University of Michigan

2. Introduction • Cognitive Radio Networks (CRNs) • Improve spectrum efficiency via opportunistic spectrum access •Primary and secondary users • IEEE 802.22 WRANs •Infrastructure-based network •Primary : TV transmitter •Secondary : CPEs (i.e., houses) Provide wireless broadband access in rural areas by reusing TV bands Q) How to reliably detect the existence of primary signal

3. Spectrum sensing in IEEE 802.22 • Requirements for in-band sensing in IEEE 802.22 • Secondary users must detect: -- Veryweak primary signal (< -20 dB) Constraints -- Small delay (CDT < 2 seconds) -- High accuracy (PFA, PMD < 0.1) • Simple energy detection -- Cannot be achieved with one-time sensing with a single sensor • MAC-layer solution approaches Our focus Sensor cooperation Sensing scheduling + MAC PHY Energy detection

4. Motivation t0 t0 time time • Two stage sensing in IEEE 802.22 Feature detection : Accurate, BUT expensive • Energy detection vs. feature detection CDT (2 seconds) … t0 time Q1) How many and which sensors? … (Sensor cooperation) Q2) How many times to sense? (Sensing scheduling) •Tradeoff between detectability vs. sensing overhead

5. Why RSS-profile based sensing? • Spatio-temporal variations in RSSs Sensors with high RSSs Sensors with low RSSs KEY OBSERVATIONS: • Static sensors in IEEE 802.22 • No correlation among sensors  Simplifies the sensor selection problem MAIN BENEFITS: • A priori statistics of primary signals for detection  Log-likelihood ratio test (LRT) for one-time sensing  Sequential hypothesis testing for sensing scheduling

6. Our Approach GOAL To minimize the sensing overhead, while achieving the required detection performance Exploitspatio-temporal variations in received primary signal strengths KEY IDEA • RSS-profile based detection rule • • Detection performance with one-time sensing • • Impact of shadow fading • Optimal sensor selection • • Use sensors with high performance HOW? Jointly optimized • Optimal stopping time for sensing • • Sequential analysis based on measured RSSs

7. How to build RSS-profile • Characterizing sensing results • Output of the energy detector: -- Measured RSSs at sensors follow Gaussian -- N-dimensional Gaussian distribution Very low SNR  P+NB ≈ NB • Construction of RSS-profile H1 x x H0 Simple distance-based detection rule

8. Performance of RSS-profile-based sensing ARSS profile: • • We derived theoretical detection performance: • -- for AWGN and shadow fading channels • -- in terms of PFA and PMD H1 H0 • • Performance depends on: (1)measurement error ( ) : Sensing duration (2) distance ( ) : Average RSS & Channel gain • • Observation: -- Shadow fading provides spatial diversity in RSSs  Improves the sensing performance

9. Proposed Sensing Framework • Joint optimization ofcooperative sensing & sensing scheduling • At each sensing period, the BS updates decision statistic based on: 1) sensing results 2) RSS-profile • The BS stopsscheduling sensing when the decision statistic reaches the thresholds

10. When to stop sensing? • Sequential Hypothesis Testing Problem Collaborating sensors Base station … Decision statistic Sensor 1 ΛN H1 • A … • Sensor 2 Λ3 • Λ2 • Λ1 ? … … stop sensing • report • 1 2 3 N B … H0 Sensor n time t0 t0 time • Decision thresholds (A and B) guarantee a desired detection performance • Decision is made based on the entire sensing history -- Data-fusion in temporal domain OR-rule based sensing scheduling

11. How to select sensors? • Selection of an optimal set of sensors -- An optimal set of sensors minimizes sensing overhead, which consists of: • •Averagenumber of sensingsrequired • •Reporting time of the sensing results • •Probability that a decision is made • within a CDT> Pth • We propose a sensor selection algorithm: • -- Optimal • -- Low computational cost

12. Single-time detection: averageRSS • Outperforms conventional OR-rule • Spatial diversity gain due to shadow fading

13. Single-time detection: sensor cooperation • RSS profile maximizes sensor cooperation gain

14. Sensing scheduling • Improve detection performance in a very low SNR regions • Detect weak primary signal such as -30 dB SNR

15. Optimal sensor selection • Sensor selection further minimizes sensing overhead

16. Impact of sensing time • Longer sensing duration minimizes the number of sensings

17. Related Work • Cooperative sensing •Impact of shadow fading correlation [Ghasemi and Sousa 05] •Sensor selection for independent sensor [Selen 08] • Static sensors are uncorrelated • Sensing scheduling • Optimal sensing-transmission policy [Huang 09] • OR-rule based sensing scheduling [Kim and Shin 08] • Optimal framework for spectrum selection and scheduling [Lee and Akyildiz 08] • No joint optimization for sensor cooperation and scheduling

18. Summary & Future work • Sensing optimization in IEEE 802.22 •Joint optimization framework for -- optimal set of sensor -- optimal stopping time for sensing • Spatial RSS-profile based detection • Spatio-temporal variations in RSSs can reduce sensing cost • Future work • Detection of primary user spoofing attack • Impact of sensor mobility

19. Thank you Alexander W. Min alexmin@eecs.umich.edu Visit Real-Time Computing Lab (RTCL) http://kabru.eecs.umich.edu