# 📡 Large Spectrum Models (LSM) – Tokenized RF Dataset

## Overview

This repository provides the **tokenized RF spectrum dataset** introduced in the paper:

**“Large Spectrum Models (LSMs): Decoder-Only Transformer-Powered Spectrum Activity Forecasting via Tokenized RF Data”** 

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.


## 🚀 Key Highlights

* 📊 **Real-world large-scale dataset**

  * ~**21 TB raw RF data**
  * **33 sub-GHz frequency bands** (54 MHz – 990 MHz)
  * ~**8.4 billion tokens**

* 🧠 **LLM-ready format**

  * Fully tokenized sequences
  * Compatible with GPT-style models, LLaMA, Mistral, etc.

* 🔬 **Rich metadata integration**

  * Gain, frequency, time, and spectrum structure embedded as tokens

* ⚡ **Efficient representation**

  * Vocabulary size: **128 tokens**
  * Stored as **1-byte integers** → fast I/O and low memory footprint

---

## 📁 Dataset Structure

Each sample is a **fixed-length token sequence**:

```
[ METADATA TOKENS (36) | SPECTRUM TOKENS (256) ]
```

* **Total sequence length:** 292 tokens
* **Input / Target split:**

  * Input: first 128 spectrum tokens
  * Target: next 128 tokens (forecasting task)

---

## 🔑 Tokenization Scheme

### 1. Spectrum (PSD)

* Range: **-118 dBm to -18 dBm**
* Quantized to **101 discrete levels**
* Token IDs: `1 – 101`

### 2. Metadata Tokens

| 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 |

### 3. Special Tokens

* `0` → Padding
* `102–127` → Structural tags (start/end markers for fields)

---

## 🧬 Data Generation Pipeline

```
Raw IQ Data
   ↓
STFT (Spectrogram)
   ↓
Max-Pooling (temporal)
   ↓
Trimmed Mean (robust smoothing)
   ↓
256 × 256 Spectrogram
   ↓
Tokenization
```

### Key properties:

* Time resolution: **~3.91 ms**
* Frequency resolution: **78.125 kHz**
* Designed for **long-sequence modeling**

---

## 🎯 Task Definition

**Spectrum Forecasting**

Given:

```
Past PSD tokens (t = 0 → 127)
```

Predict:

```
Future PSD tokens (t = 128 → 255)
```

This formulation allows:

* Autoregressive modeling (GPT-style)
* Sequence-to-sequence learning
* Diffusion or masked modeling approaches

---

## 📊 Dataset Characteristics

* Highly diverse spectrum environments
* Includes **high-variance bands (e.g., 630 MHz, 650 MHz)** for robustness evaluation
* Captures:

  * Cellular activity
  * Broadcast signals
  * Environmental noise patterns

---

## 🔧 Usage

### Load Dataset (Hugging Face)

```python
from datasets import load_dataset

dataset = load_dataset("cpnlab/LSM-Tokenized-Full")
sample = dataset["train"][0]

tokens = sample["input_ids"]
```

### Typical Training Setup

* Vocabulary size: `128`
* Input length: `164` (metadata + partial sequence)
* Output length: `128`
* Loss:

  * Cross-Entropy
  * Optional RMSE hybrid (for high-variance bands)

---

## 🧠 Compatible Models

This dataset is designed for:

* Transformer-based models:

  * GPT-style (decoder-only)
  * LLaMA / Mistral / Gemma / Phi variants
* Sequence models:

  * LSTM / RNN baselines
* Generative approaches:

  * Diffusion models
  * Masked modeling

---

## 📦 Related Resources

* 💻 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)

---

## ⚠️ Notes

* This dataset is **tokenized** — raw IQ data is not included here
* Designed for **research in AI-driven spectrum intelligence**
* Suitable for:

  * Dynamic Spectrum Access (DSA)
  * 6G wireless systems
  * RF foundation models

---

## 📜 Citation

If you use this dataset, please cite:

```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}
}
```

---

## 🤝 Acknowledgment

Collected using the **NEXTT city-scale wireless testbed** and supported by NSF grants.

---

## 🔥 Why This Dataset Matters

This is one of the **first datasets that:**

* Treats RF signals as **language tokens**
* Enables **foundation models for wireless spectrum**
* Scales to **billions of tokens in real-world environments**