Exercises

Use an LLM to generate a Python function for AWGN channel simulation. Verify against theoretical BPSK BER.

Ask an LLM to summarize 5 paper abstracts on MIMO detection. Verify the summaries are accurate.

Use an LLM to translate a simple MATLAB OFDM script to Python NumPy.

Generate unit tests for a channel estimation function using an LLM.

Build a prompt that generates a MIMO Rayleigh channel simulator with Kronecker correlation. Verify spatial correlation statistics.

Use RAG over 20 paper PDFs to answer specific technical questions about channel modeling.

Implement a simple deep JSCC encoder-decoder for transmitting MNIST digits over AWGN.

Use an LLM to review your simulation code and list potential bugs. Verify each finding.

Build a comparison table of 10 channel estimation methods using LLM-assisted paper analysis.

Fine-tune a small LLM on 3GPP specification text and evaluate its ability to answer telecom questions.

Implement deep JSCC for image transmission over Rayleigh fading channels and compare with separate source-channel coding.

Build an agent that can search for papers, generate simulation code, run it, and report results.

Build a wireless foundation model: pre-train on synthetic CSI data from multiple environments and fine-tune for channel prediction.

Create a benchmark suite that tests LLM code generation accuracy on 50 wireless simulation tasks of varying difficulty.

Implement semantic communication for text over a fading channel using sentence embeddings as the semantic representation.