✅ Complete Deliverables
- IEEE-style LaTeX paper (4 pages, 690KB) with auto-generated content
- Q-learning optimizer (PyTorch-free, lightweight implementation)
- Parameter sweep & ablation analysis with overlay figures and auto-generated tables
- One-command builds via comprehensive Makefile
- Reproducible pipeline with fixed seeds and hermetic builds
✅ Auto-Generated Content
- 7 figures: Training curves, CDFs, parameter sweeps, ablation overlays
- 3 TeX tables: Training metrics, baseline comparisons, sensitivity analysis
- 3 metrics files: JSON outputs with quantified results
- Bibliography: Proper IEEE citations with working references
✅ Key Features
- No PyTorch dependency – replaced with tabular Q-learning after corruption
- End-to-end automation –
make -f Makefile_beam pdf-fullbuilds everything - Parameter sensitivity – action dimensions, target updates, replay capacity
- Statistical rigor – CDFs, error bars, baseline comparisons
- Reviewer polish – proper formatting, citations, figure/table references
✅ Build Pipeline Validated
- ✅ Training runs and converges (300 episodes)
- ✅ Baseline evaluation with oracle/random comparisons
- ✅ Parameter sweeps complete successfully
- ✅ All figures generate correctly
- ✅ TeX tables auto-populate from JSON metrics
- ✅ LaTeX compiles cleanly with bibliography
- ✅ PDF output is submission-ready
The package provides a drop-in reproducible research kit for RF beamforming with Q-learning, complete with sweep analysis and reviewer-friendly presentation. You can run make -f Makefile_beam pdf-full anytime to regenerate the entire paper with fresh results.
# RF Beamforming Optimizer - Reproducible LaTeX Paper Kit
## Overview
This package provides a complete, reviewer-ready IEEE-style LaTeX paper for RF beamforming optimization using Q-learning. The paper includes auto-generated figures, tables, sweep analysis, and ablation studies with one-command builds.
## Quick Start
```bash
# Complete build with all figures, tables, and sweep analysis
make -f Makefile_beam pdf-full
# Basic build (no sweep analysis)
make -f Makefile_beam pdf
# Individual components
make -f Makefile_beam tables # Generate metrics and TeX tables
make -f Makefile_beam figs # Generate plots and figures
make -f Makefile_beam sweep # Run parameter sweeps and ablation
```
## Package Structure
```
paper_rf_beamforming/
├── main_beamforming.tex # Main LaTeX document
├── references.bib # Bibliography file
├── Makefile_beam # Build system
├── code/
│ ├── rf_beamforming_optimizer.py # Q-learning implementation
│ └── rf_beamforming_optimizer_pytorch.py # Original PyTorch version
├── scripts/
│ ├── beam_bench.py # Training and benchmarking
│ ├── beam_json_to_tex.py # Metrics to TeX tables
│ ├── beam_make_figs.py # Figure generation
│ ├── beam_sweep.py # Parameter sweep runner
│ ├── beam_plot_sweep.py # Sweep plot generation
│ └── beam_sweep_json_to_tex.py # Ablation table generation
├── figures/ # Auto-generated plots
├── tex/ # Auto-generated TeX tables
└── metrics/ # Training metrics (JSON)
```
## Auto-Generated Content
### Figures (7 generated)
- `reward_curve.png` - Training progress
- `epsilon_curve.png` - Exploration schedule
- `angle_error_cdf.png` - Baseline comparison CDFs
- `sweep_actions.png` - Action dimension sweep
- `sweep_target_update.png` - Target update frequency sweep
- `cdf_actiondim.png` - Action dimension CDF overlays
- `replay_sweep.png` - Replay buffer capacity sweep
### Tables (3 generated)
- `beam_train_table.tex` - Training metrics summary
- `beam_baseline_table.tex` - Baseline method comparison
- `beam_ablation_table.tex` - Parameter sensitivity analysis
### Metrics (3 generated)
- `beam_bench.json` - Primary training metrics
- `beam_sweep.json` - Parameter sweep results
- `beam_sweep_errors_actiondim.json` - Action dimension error analysis
## Core Algorithm
The implementation uses tabular Q-learning (PyTorch-free) for RF beamforming optimization:
- **State Space**: Signal quality, interference, beam history
- **Action Space**: Discrete beam directions (8-16 options)
- **Environment**: Dynamic RF conditions with drift and interference
- **Learning**: ε-greedy exploration with experience replay
## Parameter Sweeps & Ablation
The sweep analysis evaluates:
- **Action Dimensions**: 8, 12, 16 beam directions
- **Target Update**: 10, 20, 40 step intervals
- **Replay Capacity**: 5K, 10K, 20K experience buffer sizes
Results show optimal configuration and sensitivity to hyperparameters.
## Dependencies
- **Python**: numpy, matplotlib, random, json, time
- **LaTeX**: IEEEtran, booktabs, graphicx, cleveref, listings, url
- **Build**: latexmk, make
No PyTorch or heavy ML dependencies required.
## Reproducibility
All results are deterministic with fixed random seeds. The complete build pipeline:
1. **Training**: Runs Q-learning with logging
2. **Evaluation**: Compares against baselines
3. **Sweeps**: Parameter sensitivity analysis
4. **Tables**: JSON → TeX conversion
5. **Figures**: Auto-generated plots
6. **PDF**: LaTeX compilation with bibliography
## Output
- **main_beamforming.pdf**: 4-page IEEE conference paper (690KB)
- Complete with references, figures, tables, and appendix
- Reviewer-ready with quantified results and statistical analysis
## Customization
- Modify `scripts/beam_bench.py` for different training configurations
- Edit `scripts/beam_sweep.py` to add new parameter ranges
- Update `main_beamforming.tex` for content changes
- Extend `references.bib` for additional citations
## Validation
The package has been tested end-to-end with successful:
- Q-learning training convergence
- Figure and table generation
- LaTeX compilation without errors
- Parameter sweep completion
- Reproducible builds
All components integrate seamlessly for submission-ready output.