All classes MIMO-OFDM Channel Estimation Deep Learning Syllabus · Episode 5 of 6

Class Estimation of MIMO-OFDM Channels Using Deep Learning #5: Training and the Impact of Pilot Numbers and SNR

0 views

Tutorial on the impact of pilot numbers and SNR during training on MIMO-OFDM channel estimation using CNN with Python,. Episode 5 of the massive MIMO-OFDM channel estimation deep learning series.

Software: Python (NumPy, SciPy, PyTorch, Matplotlib)
Level: Advanced
Duration: ± 8 min
Series total: 57 min · 6 videos
Published: October 25, 2026

Episode 5 of 6 · view syllabus

Subscribe ▶ Watch the video lesson0 plays

Checking access...

Want to go deeper?

Join one of our courses or ask us directly on WhatsApp.

View the full 6-episode syllabus View MIMO-OFDM Channel Estimation Courses Ask on WhatsApp

Recommended classes for you

Picked from the software, topic, and level of “Class Estimation of MIMO-OFDM Channels Using Deep Learning #5: Training and the Impact of Pilot Numbers and SNR”.

18 episodesFree preview

Python · Beginner–Advanced

Machine Learning Python

18 video lessons with PDF guides, 3 h 41 min in total. Starts with: Introduction to Machine Learning & First Model.

3 h 41 min 61 0

View class

Deep Learning CNN-LSTM Pneumonia Detection

6 episodesFree preview

Python · Intermediate–Advanced

Deep Learning CNN-LSTM Pneumonia Detection

6 video lessons with PDF guides, 57 min in total. Starts with: Chest X-ray Dataset: Loading, Exploration & Data Splitting.

57 min 22 0

View class

EKG Arrhythmia Classification Wavelet Deep Learning

6 episodesFree preview

Python · Intermediate–Advanced

EKG Arrhythmia Classification Wavelet Deep Learning

6 video lessons with PDF guides, 56 min in total. Starts with: EKG & EEG Signals: Characteristics and Public Datasets (MIT-BIH).

56 min 2 0

View class

50 episodesFree preview

Agentic AI · Beginner–Intermediate

Agentic AI Without Coding

50 video lessons with PDF guides, 14 h 29 min in total. Starts with: Chatbots, Automation, and AI Agents: What's the Difference?.

14 h 29 min 66 0

View class

100 episodesFree preview

Claude · Beginner–Advanced

Claude from Zero to Expert

100 video lessons with PDF guides, 15 h 1 min in total. Starts with: What Claude Is and How It Differs from ChatGPT and Gemini.

15 h 1 min 62 0

View class

6 episodesFree preview

Python · Intermediate–Advanced

Transfer Learning for Leaf Disease

6 video lessons with PDF guides, 1 h 5 min in total. Starts with: PlantVillage Dataset: Folder Structure, Class Distribution & Data Split.

1 h 5 min 9 0

View class

6 episodesFree preview

Python · Intermediate–Advanced

5G Deep RL Resource Allocation

6 video lessons with PDF guides, 1 h 1 min in total. Starts with: Concept of 5G/6G Resource Allocation: Resource Block, Power, User.

1 h 1 min 0 0

View class

Reinforcement Learning for Robot Manipulator

6 episodesFree preview

Python · Intermediate–Advanced

Reinforcement Learning for Robot Manipulator

6 video lessons with PDF guides, 49 min in total. Starts with: Forward and Inverse Kinematics of a 2-DOF Robotic Arm.

49 min 1 0

View class

Need more than a video?

Other ways we can help with Python.

Learn with a mentor

Scheduled AI & Machine Learning courses with live guidance and a certificate.

Course

Multi-Objective Optimization Course: NSGA-II, MOPSO & Hybrid Algorithms (Python)

Course

ECG Arrhythmia Classification Course with Wavelet & Deep Learning (PyTorch)

Course

ANFIS + PSO/GA Course for Non-Linear Time Series Forecasting (Python)

See all in AI & Machine Learning →

Have it built for you

No time to learn Python? Our team can do the project for you.

Service

Python Machine Learning — Prediction Models, Customer Segmentation, Fraud Detection & AI Apps

Service

AI Image Creation with Stable Diffusion — Unlimited Custom Visual Production

★ 4.9

Service

AI Image & Video Creation with Leonardo AI — All-in-One Visuals for Business

★ 4.9

See all in AI & Machine Learning →

Get notified about new Python classes

At most one email a week. Unfollow anytime with one click.

Transcript

The full narration of this video. The current sentence is highlighted while the video plays; click any sentence to jump there.

Show transcript

Introduction

Here is the final result, two important experiments for the research. With half the pilots, CNN at low SNR matches full-pilot LMMSI. And a model trained only at high SNR actually fails when the signal is weak. Let's start. Hello, welcome to episode five of the Massive MIMO FDM Channel Estimation series with Deep Neural Networks. Each sub-career pilot is a sub-career that does not carry data, so reducing pilots means increasing speed. On the other hand, a deep learning model is only as good as its training data. Today we are investigating both.

Step 1: Two experiment models

Create a file named 'impact underscore pilot dot pay'. We load the CNN pilot 4 from the previous episode, then train two new models. The first model uses pilots every 8 sub-careers, only 8 pilots or 12,5% overhead, half of the previous one. The second model uses 4 pilots, but has only ever seen SNR from 15 to 20dB. Then we test the five estimators at SNR from minus 10 to 20dB and draw two comparison graphs.

Step 2: Training & Comparing

Run. Training the two models takes about 2 minutes. With half the pilots, LMMSI drops drastically, stuck around minus 10dB because the pilots are too sparse for channel delay up to 8 tabs. CNN pilot 8 remains better by 2dB than LMMSI pilot 8, and at 0dB, minus 5,03, even better than full-pilot LMMSI, minus 4,58. The model trained only at high SNR is slightly better at 20dB, minus 21,66, but at 0dB it's only minus 4,91, far below the mixed SNR CNN, minus 7,07. Let's take a look at the graph. The left graph shows the distance between the 4-pilot and 8-pilot curves, and CNN narrows that distance. The right graph shows the high SNR model performing slightly better in the gray area where it was trained, but left behind outside that area. The lesson, train the model on the entire range of conditions it will encounter. Done

na Done!

We have already examined the trade-off between the number of pilots and the importance of the training SNR range. CNN allows for fewer pilots while maintaining competitive accuracy. In the final episode, we evaluate all estimators from the most important perspective for users, Bit Error Rate. Working on a thesis, thesis, Or research in the field of telecommunications and artificial intelligence? Cerdas Inovasi Digital helps implement communication system research, from OFDM and multipath channels, massive MIMO simulation, estimator list squares and LMMSI, until deep learning-based channel estimation with PyTorch, as well as opening a Python programming course for electrical engineering and telecommunications. Want to master the entire series, From basic OFDM to NMSI and BER evaluation? All 6 episodes, complete with a step-by-step PDF guide and source code, Available for premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or pay once Rp999.000 for lifetime access. Want just one class? Buy single class with lifetime access. Subscribe now on the premium page at cerdas inovasi digital.com. Contact our WhatsApp, or visit cerdasinovasidigital.com. Thank you for watching, see you in the next episode.

Ask the class material

Type a question or keyword; we find the part of this episode that explains it and jump the video there.

My notes

Notes are saved with the current video time and are only visible to you.

Questions & answers

Stuck on a step? Ask here; the instructor and other learners can answer.

No questions yet.

Your instructor

Cerdas Inovasi Digital Team

Software developers, BIM & 3D engineers, and AI practitioners

53 video classes1,296 episodes

We build software, BIM models, games and AI automations for clients every day, and turn that hands-on work into step-by-step classes: every step is shown on screen and comes with a PDF guide you can keep.

See our clients →

Memuat

Recommended classes for you

Need more than a video?

Transcript

0:03Introduction

0:44Step 1: Two experiment models

5:21Step 2: Training & Comparing

6:26na Done!

Ask the class material

My notes

Questions & answers

Your instructor

Recommended classes for you

Need more than a video?

Transcript

0:03Introduction

0:44Step 1: Two experiment models

5:21Step 2: Training & Comparing

6:26na Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Two experiment models

Step 2: Training & Comparing

na Done!

Introduction

Step 1: Two experiment models

Step 2: Training & Comparing

na Done!