Automatic Modulation Recognition (AMR) System

Overview

This project tackles the critical challenge of accurately classifying modulation types in telemetry, tracking, and command (TTC) systems, particularly in low carrier-to-noise density ratio (CNR) environments. By leveraging AI/ML techniques and FPGA-based hardware, the system achieves real-time signal recognition and efficient data processing, ensuring reliable communication.

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Key Features

• AI/ML-Based Modulation Classification: Combines CNN and LSTM layers for high accuracy (>92%) over a signal-to-noise ratio (SNR) range of [0,18].
• Threshold-Based Logic: Implements predefined signal characteristics for fast, real-time decision-making.
• FPGA Integration: Utilizes a PYNQ-Z2 FPGA for efficient, low-latency signal classification.

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Problem Statement

Traditional AMR systems face challenges in low CNR environments due to:

1. Low Accuracy: High noise levels degrade signal quality.
2. High Computational Complexity: Resource-intensive algorithms hinder real-time applications.
3. Limited Signal Identification: Inability to handle complex distortions.

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Proposed Solution

Dual-Approach Strategy:

1. AI/ML-Based Approach:

   • Model: CNN + LSTM with ~108k parameters.
   • Dataset: DeepSig RadioML 2016.10A dataset with 11 modulation types.
   • Accuracy: >92% for SNR [0,18].

2. Threshold-Based Approach:

   • Utilizes lightweight logic for real-time classification.
   • Achieves >95% accuracy with minimal computational overhead.

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FPGA Implementation

• Advantages:
  • Parallel Processing: Enables real-time operation.
  • Flexibility: Supports reconfiguration for evolving requirements.
  • Low Latency: Ensures processing delays of <100ms.

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Benefits

• Enhanced Data Accuracy: Reliable modulation classification minimizes errors.
• Real-Time Processing: Low-latency FPGA implementation ensures rapid decision-making.
• Scalability: Adaptable to diverse modulation schemes and communication systems.
• Energy Efficiency: Runs on 5V, consuming 40% less power than alternatives.

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Applications

• Aerospace & Defense: Real-time secure communication and signal intelligence.
• Telecommunications: Supports 5G/6G networks with efficient spectrum usage.
• IoT: Reliable communication in dense, connected environments.
• Autonomous Systems: Ensures robust communication for vehicles and drones.

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Implementation Details

Steps:

1. Develop AI/ML algorithms using CNN and LSTM.
2. Validate models on MATLAB simulations and the DeepSig dataset.
3. Implement algorithms on FPGA using HDL.
4. Test and benchmark the system for real-time performance.

Hardware:

• Primary Device: PYNQ-Z2 FPGA
• Possible Alternative: Nvidia Jetson Nano for hybrid deployment.

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Results

• ML Model Accuracy: >92% on SNR range [0,18].
• Thresholding Approach Accuracy: >95% for selected modulation schemes.
• Latency: <100ms processing time.
• Power Efficiency: Operates smoothly on 5V.

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Team

• Team Members: Bharat Agarwal,Saurabh Singh, Yash Verma, Rudraksh Lande, Karan Seth, Mohit
• Mentors: Dr. Robin Khosla, Nikhil Chauhan

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Future Work

• I/O Device Integration: Adding input and output interfaces for direct interaction with the FPGA.
• Model Deployment: Optimizing the ML model for FPGA compatibility.
• Testing & Benchmarking: Validating the system under diverse real-world conditions.

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License

This project is licensed under WTFPL.

For any inquiries, feel free to contact us.