pdf_gross.gifAI, ML & Deep Learning in the Evolution of 5G & 6G

[duration: 1 full day or 2 x ½ day, Euro 1.450.- (net) per participant]

Simple AI Model: AI, ML & Deep Learning in the Evolution of 5G & 6G

Table of Contents:

Chapter 0: Before we start...

  • De-Mystification: y = f(x) and how it relates to AI
  • Asking Chat-GPT a few questions
    • 1) tell me a joke
    • 2) what can AI do for cellular radio?
  • Some Look at LLMs: Large Language Models and their use cases
  • Types & 1st rough Classification of Neural Networks: Types of Artificial Intelligence, History & Future

Chapter 1: Back to the Roots: AI Basics

  • Basic Terminology or: What everybody already knows :-)
  • Perceptron, Neuron & Activation Function
    • operation principles, inputs/features, weights, activation function with examples (sigmoid, binary step, tanh, ReLU)
  • Life Cycle of any AI-model
  • Classification of Neural Networks...
    • ...by Architecture
    • ...by Types of Learning

Chapter 2: Hands in the Mud: Handwriting Recognition

  • Overview & Task Description
  • Presentation of our Neural Network
  • A Look at the Command Line
  • Training & Test Error Results

Chapter 3: In Medias Res I => AI in 3GPP Cellular

  • Collaboration Levels on the Radio Interface as defined by 3GPP
  • AI Lifecycle Management according to 3GPP (NG-RAN)
  • 3GPP Work Items Part 1: AI in NG-RAN
  • 3GPP Work Items Part 2: AI on NR-Radio Interface
  • Overview: AI-related study/work items in 3GPP Rel 18 & 19
  • Detailed Look at CSI Feedback Enhancement
  • Detailed Look at Positioning Enhancement

Chapter 4: In Medias Res II => AI beyond 3GPP Cellular

  • AI in Open RAN
  • AI-based non-linearity compensator for UE PA
  • 6G: Neuronal Receivers for an AI-native Radio Interface