AI in 5G Networks: Deployment Aspects, Risks and Telecom LLM

AI adoption in 5G networks is already a reality!

This is not another surface-level “AI for telecom” overview.

I give you 5.5 hours of well-structured video presentations in simple words when I will help you to gain a competitive knowledge to be ahead of everybody in AI adoption.

The only course where 5G engineers, CTOs, and telecom researchers get the complete picture — standards, deployment realities, LLM economics, and the roadmap to 6G AI-native architecture. No hype. No marketing.

By the end of this course, you'll understand:

• Basic AI/ML concepts related to telecom networks, including Gen AI, Large Language Models (LLMs), and Federated Learning.

• The potential of LLMs in telecom areas, such as on-demand LLM and 5G Multi-Edge Computing (MEC).

• The truth about on‑device LLM inference, semantic communication, and the coming 10x uplink explosion driven by AI+AR devices (or not?).

• 5G infrastructure challenges and KPIs related to AI features and implementation.

• How AI‑driven beam management, CSI feedback, and UE positioning are being standardized in 3GPP - and what you already can implement right now.

• Why AI‑native air interfaces and deep neural receivers will soon replace conventional RF blocks - and how to prepare?

  
  

But we also confront the uncomfortable truths:

• Why most AI “solutions” will never reach production - and how to spot them.

• The hidden TCO of AI‑infrastructure, model generalization gaps, and control‑layer risks.

• How AI traffic will break current QoS models and force to re‑engineer our telecom networks.

• The ethical, privacy, and workforce upheavals that come with true AI adoption.

You will have a possibility to check your knowledge after each paragraph.

Let's rock telecom together!