To ensure that cognitive radios would not interfere with primary users, spectrum sensing is required to be efficient and accurate by reliably detecting primary user signals. In this work, we implemented a spectrum sensing methodology based on the Truncated Sequential Probability Ratio Test (TSPRT). The TSPRT is a combination of SPRT and Neyman-Pearson. We created and simulated the model and observed the variation of quantization error, noise variance and dynamic range of the signal to achieve the minimum average sample number (ASN) and desired error probabilities of detection and false alarm for sine wave and similar input signals. This report comprises of theoretical analysis and practical implementation of spectrum sensing circuit in Xilinx system generator. Simulations are done to observe the effect of various parameters on ASN and shown.
TABLE OF CONTENTS
Decleration of Authorship
Abstract
Acknowledgement
List of Figures
List of Tables
1. Introduction
2. Background
2.1 Cognitive Radio Overview
2.2 Spectrum Sensing
2.2.1 Matched Filter
2.2.2 Cyclostationary Feature Detector
2.2.3 Energy Detector
2.2.4 Auto-correlation Based Detector
2.2.5 Cooperative Detector
2.3 Literature Survey
2.3.1 Binary Hypothesis
2.3.2 Neyman-Pearson Test
2.3.3 Sequential Test
2.3.4 Sequential probability Ratio Test (SPRT)
2.3.5 Truncated Sequential probability Ratio Test (TSPRT)
3. SIMULINK
3.1 Introduction to SIMULINK
3.2 Flow chart describing model
3.3 SIMULINK model
4. Xilinx Implementation Implementation
4.1 Brief about FPGA
4.2 Introduction to system generator in XILINX
4.3 Process Flow
4.4 Xilinx Model
5. Results and Analysis
5.1 ADC bit variation and its effect on ASN
5.2 Variation of noise parameter σn
5.3 Effect of q/σ and dynamic range of signal
5.4 Detecting false alarms
6. Conclusion
7. Limitations and Future Work
Bibliography
- Quote paper
- Neha Pal (Author), 2012, Implementation of Cognitive Radio Spectrum sensing circuit using TSPRT algorithm, Munich, GRIN Verlag, https://www.grin.com/document/352324
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