Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors (1707.08908v2)

Abstract: This paper looks into the technology classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for Automatic Modulation Classification (AMC) based on long short term memory (LSTM) is proposed. The model learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments. Analyses show that the proposed model yields an average classification accuracy of close to 90% at varying SNR conditions ranging from 0dB to 20dB. Further, we explore the utility of this LSTM model for a variable symbol rate scenario. We show that a LSTM based model can learn good representations of variable length time domain sequences, which is useful in classifying modulation signals with different symbol rates. The achieved accuracy of 75% on an input sample length of 64 for which it was not trained, substantiates the representation power of the model. To reduce the data communication overhead from distributed sensors, the feasibility of classification using averaged magnitude spectrum data, or online classification on the low cost sensors is studied. Furthermore, quantized realizations of the proposed models are analyzed for deployment on sensors with low processing power.

• The paper demonstrates that LSTM-based AMC achieves nearly 90% accuracy across varied SNRs and symbol rates.
• It eliminates traditional expert feature extraction by leveraging time-domain amplitude and phase information.
• The study highlights efficient model quantization for practical deployment in decentralized, low-resource spectrum sensing networks.

Deep Learning Models for Wireless Signal Classification

The paper "Deep Learning Models for Wireless Signal Classification with Distributed Low-Cost Spectrum Sensors" investigates the automatic modulation classification (AMC) problem within wireless spectrum sensing networks using advanced deep learning techniques. By employing Long Short-Term Memory (LSTM) models, the authors provide a fresh approach to AMC that eschews conventional expert feature extraction, focusing instead on time domain amplitude and phase information.

Model and Performance

The proposed LSTM model demonstrates notable performance across varied Signal-to-Noise Ratios (SNRs), achieving almost 90% accuracy. This performance is sustained in scenarios with varying symbol rates, displaying strong adaptability and efficient representation of time-domain signals even for input samples not encountered during training. The authors contrast these results with existing state-of-the-art methods and highlight that the LSTM approach provides substantial improvements without requiring complex architectures such as hybrid CNN-LSTM models often used for similar tasks.

Features and Data Management

Reducing the communication overhead inherent in decentralized networks is critical for real-world implementation. The paper evaluates the LSTM model on averaged magnitude spectrum data, providing evidence that classification can still be accurate despite reduced data fidelity. The paper also discusses the quantization of the LSTM model to facilitate deployment in low-resource environments, such as the sensor nodes within Electrosense—a crowd-sourced spectrum monitoring initiative. Quantizing models can significantly reduce the computational loads, making real-time inference on embedded systems more feasible.

Implications and Future Directions

The work has substantial practical implications in spectrum monitoring, suggesting paths for deploying deep learning strategies directly on the sensor level, thus conserving bandwidth and storage resources. From a theoretical standpoint, it underscores the LSTM model's ability to handle varying input lengths, showcasing its versatility in real-world applications. The approach also opens avenues for exploration in semi-supervised learning methods that could alleviate the labor-intensive process of acquiring labeled data.

Given the inherent challenges noted in feature extraction and robustness in fluctuating channel conditions, future directions could involve incorporating more robust preprocessing mechanisms such as blind denoising. Another area of interest could be the design of models capable of deriving features akin to cyclostationary ones, potentially enhancing performance across a broader array of conditions.

This paper paves the way for further investigation into network efficiency, focusing on minimizing resource consumption while maintaining high fidelity in signal classification. In summary, these innovations present a compelling direction for the evolution of wireless sensing technology, moving towards more autonomous, efficient, and scalable solutions.