Over the years, engineers have gradually moved wireless communication systems upward into higher frequency bands, a trend driven by the need for wider bandwidth and larger coverage areas; radar, too, has long operated at these higher frequencies. Therefore, the spectrum of these two systems may overlap. In this research paper we designed a smart spectrum sensing system for radar and wireless communication system using deep convolutional neural network. We designed neural architecture for this purpose and labeling the data. Signal classification is equally important, because the growing number of modulation techniques makes it hard to tell at a glance whether a given burst is a wireless communication signal(smartphone call) or a whether radar ping. To tackle that challenge, the same neural architecture was adapted to recognize the waveform and modulation format of incoming signals, again relying on labeled synthetic data. MATLAB Simulink provided an accessible framework for generating, labeling, and pre-processing the training sets before they were fed into the network. For the spectrum-sensing task, traditional radar and wireless signals were synthesized in blocks, captured as time-series records, and then passed through the joint filter-classifier network. For waveform classification wigner ville distribution and convolutional neural network is used. Finally to the demonstrate and figure out the classification performance we used a confusion matrix, a confusion matrix compare true class values with predicted class values and gives the accurate classification values diagonally.
Introduction
Overview:
This study presents a deep learning-based approach to:
Sense unused spectrum bands (spectrum sensing)
Classify different radio waveforms and modulation types
The system processes signals from radar and commercial wireless communication systems (e.g., LTE, 5G), using deep convolutional neural networks (CNNs) within a MATLAB Simulink environment.
Research Objectives:
Identify and differentiate radar and wireless communication signals.
Classify waveform and modulation types using deep learning.
Improve detection of primary users (PUs) in specific frequency bands.
Ensure robustness against noise/interference.
Minimize misclassification and missed detections.
Literature Insights:
Prior studies used machine learning (e.g., SVMs, Gaussian models) for spectrum sensing but faced limitations (e.g., noise uncertainty, complex implementation).
Deep learning overcomes these by learning high-level features from large datasets without manual feature engineering.
Application of DL in wireless communication is still emerging.
Methodology:
A. Spectrum Sensing:
Environment: Simulated airport radar at 2.8 GHz and nearby LTE/5G signals with 30 scatterers.
Data Preparation:
Used spectrogram images labeled as Radar, LTE, 5G, and Noise.
Split data: 80% training, 20% validation.
Network Architecture:
Used ResNet50 as base for deeplabv3+ architecture.
Trained with SGDM algorithm.
Testing & Evaluation:
Used semanticseg and confusion matrix to assess performance.
Achieved effective separation and identification of signal types.
B. Waveform Classification:
1. Radar Waveforms:
Generated 3 modulation types:
Rectangular Pulse
Linear Frequency Modulation (LFM)
Barker Code
Used Wigner-Ville Distribution (WVD) to extract time-frequency features.
Stored features as 227×227 RGB images.
2. Wireless Waveforms:
Generated 5 modulation types:
G-FSK, CP-FSK, B-FM, SSB-AM, DSB-AM
WVD used for feature extraction.
Data split: training, validation, testing.
3. Network Setup and Training:
Used SqueezeNet for image classification.
Converted WVD images for input.
Trained CNN using trainNetwork in MATLAB.
Training achieved ~96% accuracy, with 1280 iterations over 144 minutes.
Results:
Spectrum Sensing: Accurate identification of radar, LTE, and 5G signals with clear signal labeling.
Waveform Classification: High accuracy in detecting modulation types of both radar and communication signals.
Confusion matrices confirm reliable classification and minimal misclassification.
Conclusion
Spectrum sensing and waveform classification are essential elements of contemporary wireless communication systems, facilitating effective spectrum use and reliable signal recognition. Utilizing deep learning methods, we have shown considerable progress in these fields.
Deep learning architectures, like convolutional neural networks (CNNs), have demonstrated exceptional abilities in identifying intricate patterns within raw signal data, surpassing conventional techniques in accuracy and flexibility.
Incorporating deep learning into spectrum sensing enhances the accuracy of detecting spectrum gaps and primary user signals, even in environments with low signal-to-noise ratio (SNR). Likewise, in waveform classification, deep learning models are proficient at recognizing and differentiating various modulation schemes.
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