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
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
Research Objectives and Key Topics
The primary objective of this dissertation is to design and implement an efficient spectrum sensing methodology for cognitive radio networks using the Truncated Sequential Probability Ratio Test (TSPRT). The research aims to balance the trade-off between sensing accuracy and the Average Sample Number (ASN) required for signal detection, ultimately verifying the model through implementation in MATLAB/SIMULINK and Xilinx System Generator for FPGA deployment.
- Theoretical analysis of Sequential Probability Ratio Test (SPRT) versus TSPRT.
- Modeling of cognitive radio spectrum sensing circuits in SIMULINK.
- Implementation of signal detection using FPGA-based Xilinx hardware.
- Optimization of ADC resolution and noise parameters for minimal sensing time.
- Evaluation of detection performance under varying Signal-to-Noise Ratio (SNR) and error probabilities.
Excerpts from the Book
2.3.5 Truncated Sequential Probability Ratio Test (TSPRT)
Truncated SPRT deals with the situation where random variables x1, x2, . . ., are independent & identically distributed. In our case, the observations x1, x2, . . ., are continuous random variables whose distribution parameters changes with time and thus form a non-stationary process. After studying the behaviors of operating characteristics & ASN functions of TSPRT, Madsengave approximate stopping bounds and truncation point using numerical integration. Aroian and Robison showed that for a small truncation point, error probabilities can be numerically computed to any desired degree of accuracy.
When using TSPRT it is desirable to specify a truncation point such that the resulting point gives the minimum expected number of observations with a constraint on desired error probabilities. TSPRT is a modified version of signal probability ratio test (SPRT) with a combination of Neyman-Pearson test to truncate the sensing process at a finite point.
Summary of Chapters
1. Introduction: This chapter outlines the increasing scarcity of the radio spectrum and introduces cognitive radio as a solution for opportunistic spectrum sharing.
2. Background: Provides an overview of various spectrum sensing techniques, including matched filters, energy detection, and the statistical foundations of sequential hypothesis testing.
3. SIMULINK: Describes the design and simulation of the spectrum sensing model in the MATLAB/SIMULINK environment.
4. Xilinx Implementation Implementation: Details the transition from a SIMULINK model to hardware realization using Xilinx System Generator and FPGA technology.
5. Results and Analysis: Analyzes the performance of the sensing model by varying parameters such as ADC bit-depth, noise variance, and signal dynamic range.
6. Conclusion: Summarizes the key findings and verifies the successful implementation of the receiver model for FPGA synthesis.
7. Limitations and Future Work: Addresses technical challenges encountered during the SIMULINK-to-Xilinx translation process and suggests improvements for future iterations.
Keywords
Cognitive Radio, Spectrum Sensing, TSPRT, SPRT, Neyman-Pearson Test, FPGA, Xilinx System Generator, MATLAB, SIMULINK, Signal-to-Noise Ratio, ADC, ASN, Wireless Communication, Detection Probability, False Alarm.
Frequently Asked Questions
What is the primary focus of this research?
The research focuses on developing an efficient spectrum sensing method for cognitive radios to ensure secondary users do not interfere with licensed primary users.
What are the central thematic fields covered?
The work covers signal detection theory, sequential hypothesis testing, MATLAB/SIMULINK modeling, and hardware-level implementation using FPGAs.
What is the core research objective?
The goal is to implement a spectrum sensing circuit based on the Truncated Sequential Probability Ratio Test (TSPRT) to achieve an optimal balance between sensing time (ASN) and detection accuracy.
Which scientific methodology is utilized?
The study uses statistical hypothesis testing, specifically a modified version of the SPRT combined with the Neyman-Pearson test, implemented through computer simulations and hardware synthesis.
What does the main body address?
It covers the theoretical background of sensing, the construction of the receiver model in SIMULINK, and the practical implementation of this model using Xilinx tools for FPGA.
Which keywords best characterize this work?
Key terms include Cognitive Radio, Spectrum Sensing, TSPRT, FPGA, and Sequential Probability Ratio Test.
Why is TSPRT preferred over traditional energy detection?
TSPRT offers significantly reduced sensing time (lower ASN) compared to fixed-sample-size schemes while maintaining comparable detection performance.
What role does the Xilinx System Generator play?
It serves as the bridge between the high-level SIMULINK design and the low-level hardware realization, allowing for the automatic generation of VHDL code for FPGAs.
What effect does ADC bit variation have on the system?
The research concludes that varying ADC bits has minimal impact on the Average Sample Number, suggesting that lower-bit ADCs are more economical for simple sensing tasks.
- 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
