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5G NR Channel Estimation using Machine Learning

Target year: BE Sem 7-8 (Major Project Phase-I/II) AICTE: 6 credits · ~150 hrs Bloom: Create / Evaluate MU CBCS: ETC801 / ETDLO8021 BE Project

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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About this project

Implement a 5G NR (New Radio) downlink channel estimation pipeline using both classical least-squares (LS) and an ML-based (deep neural network) estimator. Train on simulated multipath channels (3GPP TDL-A, TDL-B, TDL-C), compare bit-error-rate vs SNR. Show that ML beats classical at low SNR.

Course Learning Outcomes (CLOs):
CLO1: Apply OFDM + 5G NR frame fundamentals to a concrete simulator.
CLO2: Implement classical LS channel estimator.
CLO3: Design + train a DNN-based channel estimator.
CLO4: Evaluate communication systems using BER-vs-SNR plots.
CLO5: Document research-grade results in IEEE-format paper.

Industry/societal relevance: India just rolled out 5G nationwide; Reliance Jio, Airtel, Nokia, Ericsson R&D India hiring 100s of 5G physical-layer engineers.

Milestones

1. 5G NR Simulator Setup

15 marks 14d

Use MATLAB 5G Toolbox: generate downlink reference signals for TDL-A 30ns channel. Document frame structure.

2. LS Estimator Baseline

20 marks 18d

Implement classical least-squares channel estimator. Plot BER vs SNR for QPSK + 16QAM.

3. DNN Architecture Design

25 marks 21d

Design DNN: input = pilot symbols + noisy estimates, output = improved estimate. Train on 100k channel realizations.

4. Comparison + Robustness Tests

20 marks 18d

Test DNN on unseen TDL-B + TDL-C channels (out-of-distribution). Compare BER curves vs LS.

5. Final Defense + IEEE Paper

20 marks 21d

8-page IEEE paper, oral defense panel. Submit code + trained models on GitHub.

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Skills you'll learn

Digital CommunicationsOFDM5G NR frame structureMATLAB 5G ToolboxPyTorch / TensorFlowDeep Neural NetworksPerformance evaluation (BER curves)

Tools used

MATLAB R2024 + 5G ToolboxPython 3.11PyTorch 2.xNumPymatplotlibGitHubIEEE-format LaTeX

Prerequisites

Digital Communications; Wireless Communication; MATLAB intermediate; introductory ML

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