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Telecom Transformation using AI and ML

A comprehensive course on AI and ML for telecom use cases using modern network architecture design and development. This course is highly suited for professionals in Telecom industry for upskiling

Book a Class, for FREE


Jayesh Suryawanshi

Volkswagen (I) Pvt. Ltd.,

RMD Sinhgad

Mithin SanthaKumar

Timetooth Technology

Cochin University of Science and Technology University in Kochi, Kerala

Manthan Waghaye

Altigreen Propulsion Labs Pvt Ltd

Shri Sant Gajanan Maharaj College Of Engineering

Hemant Sagar

KN Associates

PM polytechnic, Delhi ncr , sonipat

Jangaiah Chikonda



sai dinesh


usharama college of engineering and technology

Vadapalli A S Krishna Maruthi Srinivas

advance technologies

Jawaharlal Nehru Technological University, Kakinada

Manas Metar

Sphinix World Biz Limited

University of Wolverhampton


This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects

Course Overview

  • The students will have a thorough knowledge of Telecommunication Network transformation.
  • They can specialize in the domain and gain complete in-depth knowledge of it.
  • The students are exposed to the modern trends & the standard practices followed in the industry right now.
  • After completing this course, the students will gain a better understanding of the concepts of Machine Learning and Artificial Intelligence for the Telecommunication Network Transformation application.
  • The software used in this course such as NS3, MobileInsight, PyTorch, sklearn, Keras, TensorFlow, and Kubernetes will help the students to gain a better understanding of what is being taught to them and make them industry-ready.

Course Syllabus

On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.

Week 1 - Introduction to 4G LTE and 5G NR

  • Evolution from 4G LTE/LTE-A to 5G NR: IMT-2020 requirements for 5G
  • 5G use-cases
    • (a) Enhanced mobile broadband (eMBB)
    • (b) Ultra-reliable low latency communication (uRLLC)
    • (c) Machine-type Communication or massive IoT (mIoT, MTC)
  • Gaps in LTE/LTE-A – to be bridged en route to 5G

Week 2 - Key design principles of 4G and 5G NR

  • Carrier aggregation
  • Transmit diversity and spatial multiplexing
  • Small cell deployment and network densification
  • Cellular network deployment in unlicensed bands
  • Resource allocation and quality of service
  • Differences between LTE protocol stack and NR protocol stack

Week 3 - PHY layer enhancements in 5G NR

  • 5G NR frequency bands: sub-6 GHz and mmwave frequency bands
  • 5G NR frame structure – Time Domain, the notion of TTIs, flexible TTIs, slots, flexible slots, aggregated slots, mini-slots, UL/DL multiplexing, and time-division duplexing (TDD/FDD), mmwave.
  • 5G NR frame structure – Frequency Domain, the notion of sub-carrier spacing numerology and bandwidth parts.
  • Massive MIMO, Analog, digital and hybrid beamforming, frequency localization in 5G NR waveform, and channel coding schemes.
  • Cell-based association in LTE vs beam-based association in 5G NR.

Week 4 - MAC layer and higher RAN layer enhancements in 5G NR

  • Resource allocation (Physical Resource Blocks and Bandwidth part allocation), frequency/time multiplexing
  • HARQ process, retransmissions, and Asynchronous HARQ
  • Grant-free resource allocation for uplink.
  • Carrier Aggregation: Primary cell and secondary cells.
  • Dual connectivity architecture, Non-stand-alone, and stand-alone 5G architectures
  • The notion of bearers and flows, QoS Class Index (QCI), QoS Flow Index (QFI)
  • RLC windowing, RLC ARQ retransmission impact on latency.
  • PDCP duplications and Service Data Adaptation Protocol.

Week 5 - 5G Core Network and application design

  • Enhanced EPC core and CUPS architecture
  • 5G Next Generation (NG) Core network architecture and components.
  • Network Slicing.
  • 5G Core Interfaces and protocols
  • 5G Core basic procedures.
  • Cross-layer inter-dependencies: Impact of RAN on TCP/IP and application performances
  • 360 video and Virtual Reality, Field-of-View based VR, drones/UAVs.
  • Augmented Reality (AR) and AR gaming over a cellular network
  • Wearables, IoT apps.
  • Mobile edge cloud architecture

Week 6 - Advanced topics and course summary

  • Disaggregated RAN architecture
  • Open-RAN and virtual RAN design principles, Software-Defined Networking and Network Function Virtualization in RAN.
  • RAN Intelligent Controller: Real-time and non-real-time.
  • Machine Learning micro-services

Week 7 - Introduction to ML and analytics

  • Fundamentals of machine learning.
  • Challenges and potentials of machine learning in networks
  • Application examples in networks
  • Introduction to 5G and 4G performance counters.
  • Measurement object classes
  • Introduction to 5G and 4G Performance Measurements
  • Introduction to 5G and 4G Key Performance Indicators

Week 8 - 5G Performance Measurements – L1/L2 (DU)

  • RLC PDU delay measurements
  • Radio Resource utilization
  • UE throughput measurements
  • MCS and Transport Block related transmission and retransmission measurements
  • F1-U PDU drop measurements
  • L1 RSRP and SINR measurements
  • CQI and MCS distributions
  • RACH preambles
  • Slices and sessions

Week 9 - 5G Performance Measurements – L2/L3 (CU)

  • RRC connected users
  • DRB Session time and QoS flow time
  • Handovers and types of handovers: Inter-system and intra-system handovers
  • Radio Link failures and other causes
  • PDCP Packet delay measurements
  • PDCP bit-rate measurements
  • PDCP data volume measurements
  • Slices and session-specific measurements

Week 10 - 5G and 4G Performance Measurements – KPIs

  • Accessibility KPI
  • Integrity KPI
  • Utilization KPI
  • Retainability KPI
  • Mobility KPI
  • Energy Efficiency KPI
  • RRC connection measurements
  • E-RAB related measurements
  • Handover related measurements
  • Cell-level radio bearer QoS related measurements
  • Radio utilization measurements
  • Carrier Aggregation measurements
  • Power, Energy, Environment measurements
  • Accessibility, Integrity, Utilization, Retainability KPIs

Week 11 - Channel models, path loss and propagation loss models

  • Channel models for 0.5 – 100 GHz
  • Antenna Modeling
  • Pathloss and penetration modeling
  • Slow fading and Fast fading model
  • Channel models for link-level evaluations and calibration

Week 12 - NWDAF and MDAF analytics

  • UE Mobility prediction
  • Network Performance
  • QoS, Experience, and sustainability
  • User Data Congestion
  • NF Load
  • M-plane analytics

Week 13 - Introduction to ML for RAN

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning
  • Neural Networks
  • Deep learning
  • Applicability for cellular RAN predictions

Week 14 - KPI Prediction techniques using regression/classification

  • Throughput prediction
  • Latency prediction
  • Packet loss prediction
  • Basics of supervised and unsupervised learning
  • Random Forest regression/classification for KPI prediction
  • Support Vector Regression for KPI prediction

Week 15 - RNN and Deep Learning and introduction to RL

  • ARIMA time series for KPI prediction
  • RNN LSTM and auto-encoders for KPI prediction
  • Deep Learning for KPI prediction
  • Markov Decision process
  • Model-free vs Model-based RL

Week 16 - Reinforcement Learning

  • Off-policy vs On-policy RL
  • Offline vs Online RL
  • Q learning: Vanilla Q-learning and DQN
  • Variants of Q learning
  • Differences between Q learning, SARSA, PPO
  • RL decision trees
  • RL actions

Week 17 - Bayesian Optimization and other ML techniques

  • Introduction to Bayesian optimization and acquisition functions
  • Black box configuration in RAN
  • Optimization of configuration functions
  • Power and beam tilt optimization

Week 18 - Review and Recap

  • Supervised ML
  • Unsupervised ML
  • Reinforcement Learning
  • Recurrent Neural Networks
  • Regression, Classification and Decision trees
  • Bayesian Optimization

Week 19 - Building embedded ML intelligence in a cellular network

  • O-RAN architecture
  • Service Management and Orchestration framework for M-plane cellular network configuration management
  • Non-RT RIC and offline ML training
  • Near-RT RIC and reinforcement learning/online ML
  • Full ML life cycle
  • Virtualization and SDN

Week 20 - AI/ML use-case in cellular networks

  • Traffic Steering
  • Quality of Service/Quality of Experience
  • Network slicing
  • MU-MIMO Beamforming
  • SoN optimization
  • V2X, UAV, and connected vehicles
  • Load optimization and UPF selection
  • Slice-specific Schedulers
  • Root-cause analysis

Week 21 - Case studies (Part A): Traffic Steering, QoS and slicing

  • Non-RT RIC for offline ML models
  • KPI target and declarative guidance
  • Near-RT RIC for RL/online models and inference host
  • SMO components as inference hosts
  • SMF as inference host for UPF selection
  • UPF as inference host for traffic delivery

Week 22 - Case studies (Part B): Root-cause diagnostics and anomaly detection

  • PM and KPI Data-driven analysis
  • Root-cause diagnostics
  • What-if analysis
  • SoN optimization and Radio link failure minimization
  • Channel State Information Predictions from imperfect channel feedback

Week 23 - Case studies (Part C): RAN-assisted IP analytics

  • RAN PMs and KPIs for throughput
  • Using network/edge cloud APIs to communicate RAN KPIs to core/application network – using NWDAF and Application Function
  • Video bit-rate adaptation at application server using ML and deep learning techniques.
  • QoE metrics: Freezing, Stalling, Session Rebuffering, etc.
  • RAN PMs and KPIs for latency
  • Using network/edge cloud APIs to communicate RAN KPIs to core/application network – using NWDAF and Application Function
  • IP packet size adaptation using ML/RL techniques.
  • QoE metrics: Screen freezes/blanks, etc.

Week 24 - Cellular Network deployments

  • RAN PMs and KPIs for latency
  • Using network/edge cloud APIs to communicate with the RAN KPIs to core/application network – using NWDAF and Application Function
  • IP packet size adaptation using ML/RL techniques.
  • QoE metrics: Screen freezes/blanks, etc.

Our courses have been designed by industry experts to help students achieve their dream careers

Industry Projects

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 5 Projects that are available in this program.

5G Cellular Network design using NS3 Simulation

In this project, the students will simulate the key functionalities associated with the RAN and/or core network functions across the layers of the protocol stack for an LTE or 5G or LTE-5G dual-connected cellular mobile network.

RAN Data analytics using MobileInsight or QXDM

RAN Data analytics using MobileInsight or QXDM

Transport layer performance of Facebook 360 using Wireshark PCAP logs

Transport layer performance of Facebook 360 using Wireshark PCAP logs

Cloud-native microservices for real-time prediction and optimization

Cloud-native microservices for real-time prediction and optimization

ML Models for Network KPI prediction

ML Models for Network KPI prediction

Our courses have been designed by industry experts to help students achieve their dream careers

Ratings & Reviews by Learners

Skill-Lync has received honest feedback from our learners around the globe.

Google Rating

Flexible Pricing

Talk to our career counsellors to get flexible payment options.


INR 2,00,000

Inclusive of all charges

Become job ready with our comprehensive industry focused curriculum for freshers & early career professionals

  • 1 Year Accessto Skill-Lync’s Learning Management System (LMS)

  • Personalized Pageto showcase Projects & Certifications

  • Live Individual & Group Sessionsto resolve queries, Discuss Progress and Study Plans.

  • Personalized & Hands-OnSupport over Mail, Telephone for Query Resolution & Overall Learner Progress.

  • Job-Oriented Industry Relevant Curriculumavailable at your fingertips curated by Global Industry Experts along with Live Sessions.

Instructors profiles

Our courses are designed by leading academicians and experienced industry professionals.


1 industry expert

Our instructors are industry experts along with a passion to teach.


10 years in the experience range

Instructors with 10 years extensive industry experience.


Areas of expertise

  • Wireless Networking & Communication

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