https://github.com/ipc-lab/kaira - Kaira v0.2.1 Release

Kaira v0.2.1 Release Notes

Release Date: June 15, 2025

We're excited to announce the release of Kaira v0.2.1! This release focuses on improving code quality, expanding test coverage, and enhancing the development experience with better CI/CD pipeline and documentation improvements.

🚀 What's New

Comprehensive Test Suite

• Enhanced Metrics Testing: Extensive tests for SSIM and EVM metrics with stateful methods and error handling
• Improved Test Coverage: Better validation of core functionality across the framework

PlottingUtils Module

• Centralized Visualization: New unified plotting utilities for consistent visualization across all examples
• Better Chart Quality: Enhanced plotting capabilities for research and educational materials

Enhanced CI/CD Pipeline

• Improved Reliability: Better caching, error handling, and more robust BPG tools installation
• Multiple Download Sources: Fallback mechanisms for improved installation reliability
• Faster Builds: Optimized workflow execution with intelligent caching

🔧 Improvements

Code Quality & Structure

• Refactored Codebase: Improved code structure and readability across examples and tests
• Better Type Handling: Enhanced type handling in modulation examples
• Optimized Examples: Streamlined example code for better learning experience

Documentation & Visualization

• Enhanced Guidelines: Improved contributing guidelines and clearer tooltip descriptions
• Better Animations: Enhanced visualizations for FEC and LDPC decoding examples
• Educational Content: More intuitive visual demonstrations of algorithms

Development Experience

• Path Handling: Improved path handling with PosixPath to string conversion for consistency
• Error Messages: Better error handling in setup.py and file reading logic
• Mock Improvements: Enhanced mock file handling for more reliable testing

🐛 Bug Fixes

Installation & Dependencies

• BPG Download Links: Updated to use correct sources for improved installation reliability
• System Dependencies: Enhanced installation with better checks and alternative methods
• CI Workflow: Improved workflow reliability with better error handling

Core Functionality

• File Operations: Fixed path handling inconsistencies
• Setup Process: Improved error handling in setup.py for better user experience
• Testing Framework: More stable test execution with better isolation

🧹 Cleanup

Code Structure

• CapacityAnalyzer Removal: Removed for improved code organization
• Example Cleanup: Removed redundant educational vs professional LDPC codes comparison
• Visualization Cleanup: Streamlined repetition code visualization examples

Configuration

• Codecov Optimization: Removed patch coverage configuration for cleaner reporting
• Dependencies: Cleaned up unused dependencies and imports

🔗 Installation

PyPI Installation

bash pip install pykaira==0.2.1

📊 Technical Details

Requirements

• Python: ≥3.10 (supports Python 3.13.2)
• PyTorch: Compatible with latest stable releases
• Dependencies: See requirements.txt for full list

Compatibility

• Platforms: Linux, macOS, Windows
• Architectures: x86_64, ARM64 (Apple Silicon)

🎯 Migration from v0.2.0

This release includes one breaking change due to code structure improvements:

Breaking Changes

• CapacityAnalyzer Removal: The CapacityAnalyzer component has been removed for improved code organization. If your code uses this component, you'll need to remove references to it or implement an alternative solution.

Migration Steps

1. Update your installation: bash pip install --upgrade pykaira

2. Remove CapacityAnalyzer usage: If your code imports or uses CapacityAnalyzer, remove these references: ```python

   Remove this import

   from kaira.utils import CapacityAnalyzer

# Replace with alternative implementation if needed ```

1. Test your code: Run your existing code to ensure it works with the updated version.

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details on: - Code style and standards - Testing requirements - Documentation guidelines - Pull request process

📚 Documentation

• Full Documentation: kaira.readthedocs.io
• API Reference: Complete API documentation with examples
• Tutorials: Step-by-step guides for getting started
• Examples: Comprehensive example gallery

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Read the Docs

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Full Changelog: v0.2.0...v0.2.1

- Python
Published by selimfirat 12 months ago

https://github.com/ipc-lab/kaira - Kaira v0.2.0 Release

🚀 Kaira v0.2.0 - Major Forward Error Correction Update

We're excited to announce Kaira v0.2.0, a major release that significantly expands the framework with comprehensive Forward Error Correction (FEC) capabilities and enhanced communication system simulation tools.

🎯 What's New

🔧 Forward Error Correction Framework

• LDPC Codes: Complete implementation with RPTU database integration
  • Belief Propagation Decoder with configurable iterations
  • Min-Sum LDPC Decoder with scaling and offset parameters
  • Comprehensive benchmarking suite with multiple code configurations
  • Educational and practical LDPC code examples
• Polar Codes: 5G standard compliant implementation
  • Systematic and non-systematic encoders with rank-based frozen bit selection
  • Successive Cancellation and Belief Propagation decoders
  • Polar code visualization with decoding step demonstrations
• Algebraic Codes: Reed-Muller, BCH, Reed-Solomon, and Hamming codes
• Advanced Decoders: Berlekamp-Massey, Brute Force ML, and Wagner soft-decision decoders

🌐 Enhanced Communication Systems

• Uplink MAC Channel implementation for multiple access scenarios
• Improved AWGN Channel with better noise modeling
• Enhanced integration between channels, modulators, and FEC components
• Channel State Information (CSI) handling with ChannelAwareBaseModel
• BPG Compressor integration for image compression applications

🧠 Neural Network Components

• Neural compressor with fixed quality modes and early stopping
• DeepJSCC implementations with channel awareness
• ChannelAwareBaseModel for advanced channel state handling

📊 New Metrics & Benchmarking

• Error Vector Magnitude (EVM) metric implementation
• MultiScaleSSIM implementation using torchmetrics
• Advanced benchmarking with variable block length support
• LDPC benchmarking suite with decoder performance comparison
• Statistical reliability improvements with configurable parameters

📚 Documentation & Examples

• FEC tutorial examples covering encoder and decoder usage
• Visualization examples for decoding algorithms
• Enhanced API reference documentation for FEC components
• Performance comparison guides and educational materials

🔧 Major Improvements

🏗️ Development Infrastructure

• Python 3.13.2 support added across codebase
• GitHub Actions workflows updated with artifact handling and token management
• Auto-examples generation system with incremental builds
• Documentation pipeline with Read the Docs integration
• MyPy type checking with comprehensive type annotations

⚡ Performance Optimizations

• Memory optimization for belief propagation in large LDPC codes
• Vectorized operations for faster polar code decoding
• Convergence detection reducing iteration overhead
• Parallel processing in benchmark execution
• Efficient tensor management in DeepJSCC models

🔄 Breaking Changes

⚠️ Important: Please review these changes before upgrading:

• Python Version: Minimum requirement increased to Python 3.10
• FEC API Changes:
  • bp_iterations → bp_iters in belief propagation decoders
  • Enhanced parameter validation with specific exceptions for invalid configurations
• Import Paths: Internal FEC utility imports reorganized
• Benchmark Configuration: Legacy parameter formats deprecated

🛠️ Migration Guide

For users upgrading from v0.1.x to v0.2.0:

1. Update Python environment to ≥3.10
2. Review imports from kaira.models.fec.utils for changes

🔒 Security & Quality Improvements

• GitHub token management for CI/CD workflows
• Dependency updates to latest secure versions
• Enhanced parameter validation across FEC components
• Improved error handling throughout the codebase

📦 Installation

bash pip install pykaira==0.2.0

🔗 Resources

• 📖 Documentation
• 🐛 Report Issues
• 💬 Discussions
• 🤝 Contributing Guide
• 📋 Full Changelog

🙏 Acknowledgments

Special thanks to all contributors who made this release possible. Kaira continues to grow as a comprehensive toolkit for communication systems research and development, empowering engineers and researchers to architect (Kayra) advanced communication models and ensure messages are transmitted at the right moment (Kairos).

---

Full Changelog: https://github.com/ipc-lab/kaira/compare/v0.1.1...v0.2.0

- Python
Published by selimfirat 12 months ago

https://github.com/ipc-lab/kaira - Kaira v0.1.1 Release

Kaira v0.1.1 Release

Summary

We're excited to announce the release of Kaira v0.1.1! This maintenance release includes important fixes and improvements to our PyTorch-based toolkit for simulating communication systems.

Release Date

May 23, 2025

What's Changed

Installation and Configuration Updates

• Updated Python version requirement to >=3.10 for better compatibility with modern Python features
• Fixed installation command in documentation to use 'pykaira' instead of 'kaira', ensuring users can correctly install the package via PyPI

Build and Maintenance Improvements

• Enhanced project metadata and version extraction mechanism for more reliable package identification
• Refactored deployment script for better reliability during release processes
• Cleaned up build artifacts to provide a more streamlined distribution

Removals and Cleanup

• Removed legacy CircleCI configuration in favor of our GitHub Actions workflows
• Removed release drafter settings to align with our manual release process
• Removed changelog update script - the changelog is now maintained manually for better control over release notes

Installation

You can install the latest version of Kaira using pip:

bash pip install pykaira

For existing users, update to v0.1.1:

bash pip install --upgrade pykaira

Requirements

• Python >=3.10
• PyTorch (see requirements.txt for specific version)

Documentation

For detailed documentation, examples, and tutorials, please visit: - Documentation - GitHub Repository

Full Changelog: https://github.com/ipc-lab/kaira/compare/v0.1.0...v0.1.1

Looking Forward

We're actively working on improving Kaira and would appreciate your feedback on this release. Please report any issues on our GitHub repository and stay tuned for future updates as we continue to enhance our toolkit for communication systems research.

Thank you for your interest and support of the Kaira project!

---

The Kaira Team

- Python
Published by selimfirat about 1 year ago

https://github.com/ipc-lab/kaira - Kaira v0.1.0 - Initial Release

Kaira v0.1.0 - Initial Release

We are thrilled to introduce Kaira v0.1.0, an open-source toolkit built on PyTorch aimed at advancing communication systems through simulation and innovation.

Key Features

• Comprehensive Channel Models: Implementations of AWGN, Binary Erasure, Binary Symmetric and other fundamental channel models
• Advanced Modulation Schemes: Support for various digital modulation techniques including PSK, QAM, OFDM, and differential modulation variants (DPSK)
• Forward Error Correction (FEC): Implementation of powerful error correction codes including LDPC with belief propagation decoding
• PyTorch Integration: Seamless integration with PyTorch for deep learning-based communications research
• Metrics and Evaluation: Built-in metrics for performance evaluation of communication systems
• Extensive Documentation: Detailed guides, tutorials and examples for getting started quickly
• Research-Focused: Tailored to expedite research in communications.
• Versatile: Supports a wide range of data types and neural network architectures.
• User-Friendly: Designed for easy integration with existing PyTorch projects.
• Open Source: Encourages community contributions and enhancements.

Important Note

This is an beta release, and the API may undergo changes in future versions as we continue to refine and expand the toolkit's capabilities.

Contributing

We warmly invite the community to explore Kaira, provide feedback, and contribute to its development. If you encounter any issues or have suggestions, please open an issue or submit a pull request on our GitHub repository.

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Full Changelog: https://github.com/ipc-lab/kaira/commits/v0.1.0

- Python
Published by selimfirat about 1 year ago