| # 📡 Large Spectrum Models (LSM) – Tokenized RF Dataset |
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| ## Overview |
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| This repository provides the **tokenized RF spectrum dataset** introduced in the paper: |
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| **“Large Spectrum Models (LSMs): Decoder-Only Transformer-Powered Spectrum Activity Forecasting via Tokenized RF Data”** |
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| The dataset is designed to bridge **wireless signal processing** and **large language models (LLMs)** by converting raw spectrum measurements into **discrete token sequences**, enabling direct use with transformer-based architectures. |
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| ## 🚀 Key Highlights |
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| * 📊 **Real-world large-scale dataset** |
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| * ~**21 TB raw RF data** |
| * **33 sub-GHz frequency bands** (54 MHz – 990 MHz) |
| * ~**8.4 billion tokens** |
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| * 🧠 **LLM-ready format** |
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| * Fully tokenized sequences |
| * Compatible with GPT-style models, LLaMA, Mistral, etc. |
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| * 🔬 **Rich metadata integration** |
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| * Gain, frequency, time, and spectrum structure embedded as tokens |
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| * ⚡ **Efficient representation** |
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| * Vocabulary size: **128 tokens** |
| * Stored as **1-byte integers** → fast I/O and low memory footprint |
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| --- |
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| ## 📁 Dataset Structure |
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| Each sample is a **fixed-length token sequence**: |
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| ``` |
| [ METADATA TOKENS (36) | SPECTRUM TOKENS (256) ] |
| ``` |
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| * **Total sequence length:** 292 tokens |
| * **Input / Target split:** |
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| * Input: first 128 spectrum tokens |
| * Target: next 128 tokens (forecasting task) |
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| --- |
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| ## 🔑 Tokenization Scheme |
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| ### 1. Spectrum (PSD) |
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| * Range: **-118 dBm to -18 dBm** |
| * Quantized to **101 discrete levels** |
| * Token IDs: `1 – 101` |
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| ### 2. Metadata Tokens |
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| | Feature | Description | |
| | ------------- | -------------------------------------------- | |
| | Frequency | 33 bands | |
| | Gain | 0–79 dB | |
| | Frequency Bin | Encoded using **2 tokens (base-16)** | |
| | Timestamp | Decomposed into: | |
| | | Day, Date, Month, Year, Hour, Minute, Second | |
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| ### 3. Special Tokens |
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| * `0` → Padding |
| * `102–127` → Structural tags (start/end markers for fields) |
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| --- |
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| ## 🧬 Data Generation Pipeline |
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| ``` |
| Raw IQ Data |
| ↓ |
| STFT (Spectrogram) |
| ↓ |
| Max-Pooling (temporal) |
| ↓ |
| Trimmed Mean (robust smoothing) |
| ↓ |
| 256 × 256 Spectrogram |
| ↓ |
| Tokenization |
| ``` |
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| ### Key properties: |
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| * Time resolution: **~3.91 ms** |
| * Frequency resolution: **78.125 kHz** |
| * Designed for **long-sequence modeling** |
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| --- |
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| ## 🎯 Task Definition |
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| **Spectrum Forecasting** |
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| Given: |
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| ``` |
| Past PSD tokens (t = 0 → 127) |
| ``` |
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| Predict: |
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| ``` |
| Future PSD tokens (t = 128 → 255) |
| ``` |
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| This formulation allows: |
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| * Autoregressive modeling (GPT-style) |
| * Sequence-to-sequence learning |
| * Diffusion or masked modeling approaches |
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| --- |
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| ## 📊 Dataset Characteristics |
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| * Highly diverse spectrum environments |
| * Includes **high-variance bands (e.g., 630 MHz, 650 MHz)** for robustness evaluation |
| * Captures: |
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| * Cellular activity |
| * Broadcast signals |
| * Environmental noise patterns |
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| --- |
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| ## 🔧 Usage |
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| ### Load Dataset (Hugging Face) |
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| ```python |
| from datasets import load_dataset |
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| dataset = load_dataset("cpnlab/LSM-Tokenized-Full") |
| sample = dataset["train"][0] |
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| tokens = sample["input_ids"] |
| ``` |
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| ### Typical Training Setup |
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| * Vocabulary size: `128` |
| * Input length: `164` (metadata + partial sequence) |
| * Output length: `128` |
| * Loss: |
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| * Cross-Entropy |
| * Optional RMSE hybrid (for high-variance bands) |
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| --- |
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| ## 🧠 Compatible Models |
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| This dataset is designed for: |
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| * Transformer-based models: |
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| * GPT-style (decoder-only) |
| * LLaMA / Mistral / Gemma / Phi variants |
| * Sequence models: |
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| * LSTM / RNN baselines |
| * Generative approaches: |
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| * Diffusion models |
| * Masked modeling |
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| --- |
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| ## 📦 Related Resources |
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| * 💻 Code: [https://github.com/UNL-CPN-Lab/LSM](https://github.com/UNL-CPN-Lab/LSM) |
| * 🌐 Project Page: [https://lsm.unl.edu](https://lsm.unl.edu) |
| * 📄 Paper: DySPAN 2026 (see above) |
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| --- |
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| ## ⚠️ Notes |
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| * This dataset is **tokenized** — raw IQ data is not included here |
| * Designed for **research in AI-driven spectrum intelligence** |
| * Suitable for: |
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| * Dynamic Spectrum Access (DSA) |
| * 6G wireless systems |
| * RF foundation models |
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| --- |
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| ## 📜 Citation |
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| If you use this dataset, please cite: |
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| ```bibtex |
| @inproceedings{lunar2026lsm, |
| title={Large Spectrum Models (LSMs): Decoder-Only Transformer-Powered Spectrum Activity Forecasting via Tokenized RF Data}, |
| author={Lunar, Mohammad Mosiur and Vuran, Mehmet C.}, |
| booktitle={IEEE DySPAN}, |
| year={2026} |
| } |
| ``` |
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| --- |
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| ## 🤝 Acknowledgment |
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| Collected using the **NEXTT city-scale wireless testbed** and supported by NSF grants. |
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| --- |
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| ## 🔥 Why This Dataset Matters |
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| This is one of the **first datasets that:** |
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| * Treats RF signals as **language tokens** |
| * Enables **foundation models for wireless spectrum** |
| * Scales to **billions of tokens in real-world environments** |
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