Spaces:
Running
Running
File size: 2,221 Bytes
2b1a1e3 19da45f 2b1a1e3 3d2623f 0e13d45 ebad958 2b1a1e3 ebad958 2b1a1e3 34abd42 ebad958 d0f63ee ebad958 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 |
---
title: LWM-Spectro Lab
emoji: 🔍
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: "6.0.1"
app_file: app.py
pinned: false
short_description: 'Exploring spectrograms, LWM embedding, and its evaluations.'
---
# LWM-Spectro Lab
Interactive lab for exploring **LWM spectrogram embeddings** on Sub-6 GHz I/Q baseband data.
Browse the bundled DeepMIMO-derived samples (LTE / WiFi / 5G, 128×128 spectrograms, 10.5k total),
compare LWM embeddings against raw spectrogram vectors, and probe precomputed MoE embeddings for joint SNR/mobility.
**What you get**
- **Data slices:** LTE / WiFi / 5G Sub-6 GHz I/Q baseband spectrograms with modulation, SNR, and mobility labels; 500 samples per SNR per tech (7 SNRs).
- **Visualization:** Peek at the raw 128×128 Sub-6 GHz I/Q baseband spectrograms, and compare LWM tech-specific embeddings vs. normalized raw vectors via balanced t-SNE.
- **Lightweight probes:** Run k-NN prototypes for per-tech modulation recognition and joint SNR/Doppler classification using the cached MoE embeddings alongside raw baselines.
## Features
- Filtered gallery of raw Sub-6 GHz spectrograms (tech/SNR/modulation/mobility).
- t-SNE with SNR-balanced sampling for LWM embeddings vs. raw spectrogram vectors.
- Adaptive k-NN probes for modulation (per tech) and joint SNR/Doppler (MoE vs. raw) performance.
## Usage
1. Select the **Spectrograms** and **t-SNE Analysis** tabs to explore embeddings.
2. Switch to **Modulation Classification** or **Joint SNR/Doppler Evaluation** to run the k-NN prototype with adjustable train/test splits.
## Tab Cheat Sheet
- **Spectrograms:** Inspect raw 128×128 Sub-6 GHz I/Q baseband spectrograms per technology/SNR/modulation/mobility before feature extraction.
- **t-SNE Analysis:** Recreate the SNR-ordered scatter plots from `plot/plot_tsne.py` with balanced sampling across SNRs. Example: hold SNR fixed and color by modulation to see how modulation clusters separate (or collapse) as SNR changes.
- **Modulation Classification:** Benchmark a lightweight k-NN probe on LWM embeddings vs. raw inputs for each technology.
- **Joint SNR/Doppler Evaluation:** Compare cached MoE embeddings and raw spectrograms on the 14-way SNR/mobility task.
|