Multistage Spectrum Sensing for Cognitive Radios UCLA CORES

Outline 1. Introduction 2. Problem Statement 3. Proposal 4. Markov Chain Model 5. Results 2

Spectral Vacancy Frequency PSD Spectral Vacancy Introduction to Spectrum Sensing The Frequency Spectrum is mostly allocated Spectral vacancies exist in: 1. Unallocated frequency bands. 2. Allocated bands where the Primary Users (PUs) are spatially absent or temporarily idle. Cognitive radios (CRs) find spectral vacancies by performing spectrum sensing. Spectrum Sensing Design Objectives 1. Maximize the CR throughput 2. Minimize delay in vacating channel for an incoming PU 3. Minimize collisions between CRs and PUs 3 Spectral Vacancy

Cognitive Radio Communication Radio PHY RF Sensing Radio OSI Model of Cognitive Radios 4 Channel Collaborative Sensing Application Bandwidth MAC PHY RF PHY RF MAC Sensing Method Design Parameters: 1.Sensing Algorithm 2.Sensing Time Design Parameters: 1.Narrowband 2.Wideband PU Traffic

Conventional Single Stage Sensing i. No PU ii. PU arrives Problem Statement 5 CR ActiveSensingCR ActiveSensingCR ActiveSensing Time CR ActiveSensing No FA False Alarm (FA) -Throughput Waste- CR Stops Transmission CR ActiveSensing MD -Collision with PU- -Delay in detecting PU- No Misdetection (MD) CR Stops Transmission PU 0 T 2T 3T SSS

Multistage Sensing 6 i. No PU ii. PU arrives Degrees of freedom: 1. Number of Sensing Stages (S) 2. Sensing Methods 3. Sensing Times CR Active Sensing Stage 1 No FA FA CR Stops Transmission No FA CR Active Sensing Stage 2 CR Active Sensing Stage S No FA FA PU S1S1 S2S2S CR Active Sensing Stage 1 MD No MD CR Stops Transmission MD CR Active Sensing Stage 2 CR Active Sensing Stage S MD No MD S1S1 S2S2 S

Previous Work features 2-stage sensing: Coarse and Fine sensing. Jeon et al* propose a multistage sensing algorithm * W. S. Jeon, D. G. Jeong, J. A. Han, G. Ko, and M. S. Song, "An efficient quiet period management scheme for cognitive radio systems," IEEE Trans. Wireless Comm., vol. 7, no. 2, Feb Limited model; single channel, single sensing algorithm, no collaboration, simple traffic model. Literature lacks a unified analytical framework that includes Multistage Sensing 7

Proposal We introduce a unified analytical framework that models: Multistage Sensing Number of sensing stages Sensing Methods Algorithm Sensing Time Bandwidth Narrowband Wideband CR traffic models CBR – VBR Buffered – Unbuffered Goal is to analyze the impact of varying parameters on: 1. CR throughput 2. Delay in vacating channel for a PU 3. CRs and PUs collisions 8

Discrete Time Markov Chain Analysis is based on Markov Chain: Well established Math tool for modeling discrete space stochastic processes Future evolution of process depends solely on current state. Provides closed form/numerical solutions for steady state probabilities and process variables 9

Assumptions 1. PUs and CRs arrive and depart at discrete times that are multiples of T 2. CRs communicating together are synchronized through a control channel 3. Time taken to switch between communicating and sensing modes is negligible 10

Model Overview Model is divided into 3 levels: 1. CR traffic level 2. Multistage sensing level 3. Spectrum Sensing level 11

Implementation Level 1 – CR Traffic Level 12 CR Idle CR Sensing and/or Transmitting P CR 1 - P CR Q CR 1 - Q CR P CR ≡ Probability of arrival of a CR Q CR ≡ Probability of departure of a CR P CR and Q CR are tuned to accommodate for different traffic models: CBR – VBR Buffered – Unbuffered (Buffer Size)

Implementation Level 2 - Multistage Sensing Level 13 CR Sensing and/or Transmitting S ≡ Number of Sensing Stages CR Sensing and/or Transmitting CR Idle P CR 1 - P CR Q CR 1 - Q CR CR Active Stage 1 CR Active Stage 2 CR Active Stage S CR Quiet PU detected or False Alarm PU misdetected or no False Alarm PU detected or False Alarm CR Active Stage i Implementation Level 1 – CR Traffic Level

Implementation Level 3 – Spectrum Sensing Level 14 CR Active Stage 1 PU Absent CR Active Stage i PU Absent CR Active Stage i + 1 PU Absent CR Active Stage 1 PU Present CR Active Stage I PU Present CR Active Stage i + 1 PU Present (1-P PU ).P i (1-Q PU ).(1-Q i ) Q PU.P i P PU.(1-Q i ) (1-P PU ).(1-P i ) (1-Q PU ).Q i P PU ≡ Prob of arrival of a PU Q PU ≡ Prob of departure of a PU P i ≡ Prob of False Alarm at Stage i Q i ≡ Prob of Misdetection at Stage i P PU and Q PU reflect the PU traffic model P i and Q i are tuned to describe the sensing method CR Active Stage i CR Active Stage i CR Active Stage 1 CR Active Stage 2 CR Active Stage S CR Quiet Implementation Level 2 - Multistage Sensing Level

1. 2. Implementation – Bandwidth Narrowband Sensing, N Channels CR Idle Multistage Sensing CR QuietCR Idle Multistage Sensing

16 CR Idle Sens. /Tx Ch 1 MSS Ch 1 Quiet Ch 2 MSS Ch 2 Quiet Ch N MSS Ch N Quiet CR Idle Sens. /Tx Ch 1 MSS Ch 2 MSS Ch N MSS Implementation 1:Implementation 2: Time CR Quiet Frequency CR Active Sensing Stage 1 CR Active Sensing Stage 1 Example: Single stage, 3 channels: Time CR Quiet Frequency CR Active Sensing Stage 1 CR Active Sensing Stage 1 CR Active Sensing Stage 1 Example: Single stage, 3 channels: Implementation – Bandwidth Narrowband Sensing, N Channels

Throughput for the Narrowband Case 17 Simulation Configuration

Delay in finding PU 18 Simulation Configuration

Implementation – Bandwidth Wideband Sensing 19 IDLETx P P 1-P N Channels Time CR Active Sensing Stage 1 CR Active Sensing Stage 2 CR Active Sensing Stage 3 CR Active Sensing Stage 4 CR Active Sensing Stage 3 CR Active Sensing Stage 1 CR Idle CR Active Sensing Stage 1 CR Active Sensing Stage 1 CR Active Sensing Stage 1 CR Quiet Frequency Example: Single stage, 3 channels:

Throughput for the Wideband Case 20 Simulation Configuration

Appendix 21

Simulation Configuration 22 AWGN independent channels. Sensing time =, where i is the stage number, L 0 = 50 us, T s = 20 ms, and δ = 2. Sensing time for the last sensing stage = T s. Energy Detection parameters: P r = -104 dBm. Noise Floor = -163 dBm. BW = 6 MHz SNR = -8.8 dB. Energy Threshold = dBm. # of Sensing Nodes = 5. SU Arrival Probability = 0.2. SU Departure Probability = 0.2. PU Arrival Probability = PU Departure Probability = Switching time = 0.2xTs. 2 Channels 1000 stages. 10,000 cycles. Back