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MV-PATP-MIMO Code Supplement

This directory is the public code supplement for the manuscript:

Robust MIMO Channel Identification under Matrix-Variate Heavy-Tailed Noise via Sign-Preserving Fractional Polynomial Maximization

It is intentionally separated from the manuscript sources. It contains the open implementation code, deterministic result snapshots, R reproduction scripts, rendered figures, and a compact Lean 4 formal supplement.

Contents

  • python/ - reference Python package, experiment drivers, configs, data download helpers, and tests. Includes the Middleton Class A DGP (src/mv_patp/data_gen/middleton.py), the complex-baseband demo (experiments/mv_patp_mimo/run_complex_demo.py), and the symbolic Recovery Theorem check (theory/recovery_symbolic_check.py).
  • data/results/ - CSV and manifest snapshots for the result directories used by the manuscript.
  • R/ - lightweight R scripts that summarize the CSV snapshots and render independent PDF checks.
  • figures/ - manuscript PDF figures copied from the release build.
  • Lean/, lakefile.lean, lean-toolchain - Lean 4 project with symbolic definitions and structural checks for the oPMM score and recovery anchor.
  • REPRODUCIBILITY.md - exact commands for Python, R, and Lean checks.

Python Quick Start

cd code_supplement/python
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,real_data]"
pytest -q -m "not slow and not gate"

Regenerate manuscript figures from the bundled result snapshots:

python -m experiments.mv_patp_mimo.plot \
  --synthetic-dir ../data/results/synthetic_full_v1 \
  --deepmimo-dir ../data/results/deepmimo_v2 \
  --output-dir ../figures \
  --format pdf

R Quick Start

cd code_supplement
Rscript R/run_all.R

The R scripts regenerate summary CSVs under R/results/ and independent PDF checks under figures/.

Lean Quick Start

cd code_supplement
lake build

The Lean files are not a full mechanization of the statistical proof. They are a compact formal supplement for symbolic definitions, admissible alpha ranges, the trace-normalization invariant, and the alpha = 3 PMM3 recovery anchor.

About

Code supplement for "Robust MIMO Channel Identification under Matrix-Variate Heavy-Tailed Noise via Sign-Preserving Fractional Polynomial Maximization" (oPMM_α): Python/R/Lean verification code, data, and figures.

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