cpnlab/LSM-Tokenized-Full

📡 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

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📁 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)

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🔑 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)

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🧬 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

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🎯 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

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📊 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

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🔧 Usage

Load Dataset (Hugging Face)

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)

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🧠 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

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📦 Related Resources

• 💻 Code: https://github.com/UNL-CPN-Lab/LSM
• 🌐 Project Page: https://lsm.unl.edu
• 📄 Paper: DySPAN 2026 (see above)

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⚠️ 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

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📜 Citation

If you use this dataset, please cite:

@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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🤝 Acknowledgment

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

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🔥 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