Welcome to the documentation for mlcomm!

The next-generation telecommunications in Beyond 5G, 6G, and Internet of Things (IoT) concepts require reliabilty and low-latency to fuel the increasing requirement for fast and available data. Leveraging practices in Machine Learning to enhance and modify existing features in the current standards enable these future capabilities.

Check out the Installation section for further information, including how to install the project.

Getting Started

• Installation

Functional Blocks

• mlcomm.algorithms
  • ABT
  • AlgorithmTemplate
  • DBZ
  • EKF
  • HBA
  • HOSUB
  • HPM
  • MotionTS
  • NPHTS
  • OffsetMAB
  • PF
  • TASD
• mlcomm.codebooks
  • BinaryHierarchicalCodebook
  • Node
  • TernaryPointedHierarchicalCodebook
  • build_hybrid_vector()
  • load_codebook()
  • quantize()
  • save_codebook()
• mlcomm.channels
  • AWGN
  • BasicChannel
  • Channel
  • DynamicMotion
  • NYU1
  • NYU2
  • NYU_preset
  • RicianAR1
• mlcomm.util
  • avec()
  • convert_seconds()
  • deg2rad()
  • generate_rgb_gradient()
  • init_figs()
  • pad_and_mean()
  • pad_and_mean_last()
  • pad_vec()
  • pad_vec_last()
  • rad2deg()
  • randcn()

Tutorials:

• Simulation Quickstart
• Codebook Construction
  • Binary Codebook
  • Ternary Codebook
• Algorithm Prototypes and Templates
• Getting a RTD Started for Git Repo
  • Your Code Documentation
  • The Repository and Configuration
  • Building the Project
  • Hosting on Readthedocs