AI, ML & Deep Learning in the Evolution of 5G & 6G
[duration: 1 full day or 2 x ½ day, Euro 1.450.- (net) per participant]
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
v1.0