Deep Learning Techniques for Cooperative Spectrum Sensing Under Generalized Fading Channels

Deep Learning (DL) for Cooperative Spectrum Sensing (CSS)

Authors: Pradeep Balaji Muthukumar; Samudhyatha B.; Sanjeev Gurugopinath

DOI: 10.1109/WiSPNET54241.2022.9767160

Publisher: IEEE

Full details of the research and to learn more about the methodology, results, and conclusions. Refer the Full Paper.

Problem Statement:

The problem of cooperative spectrum sensing was posed as a binary classification problem, and deep learning-based architectures were employed to find the activity of a primary user, and resource allocation to optimize the sensing performance across multiple nodes in the network.

Introduction:

Cooperative spectrum sensing is a technique used to improve the detection of primary users in a cognitive radio network, where secondary users (or cognitive radios) share the spectrum with primary users while ensuring that they do not interfere with primary user communication. In cooperative spectrum sensing, multiple cognitive radios collaborate to detect the presence or absence of primary users.

The paper proposes a deep learning-based approach to cooperative spectrum sensing that can handle generalized fading channels. Fading channels are channels where the signal experiences attenuation and phase shifts due to multipath propagation, which can cause errors in spectrum sensing. Generalized fading channels are a more general class of fading channels that include Rayleigh and Rician fading channels as special cases.

Overall, the paper demonstrates the potential of deep learning techniques for cooperative spectrum sensing under generalized fading channels, highlighting the importance of machine learning in the development of efficient and effective wireless communication systems.

Who will benefit from that work?

• Telecommunication service providers: Cooperative spectrum sensing can help telecommunication service providers to efficiently utilize the available spectrum resources and improve the overall quality of service for their customers. The proposed deep learning-based approach can improve the accuracy and efficiency of spectrum sensing, leading to better spectrum utilization.

• Researchers: The proposed approach provides a novel technique for cooperative spectrum sensing under generalized fading channels. The research community can benefit from this work by using it as a basis for further research and development in this area.

• Regulatory authorities: The proposed approach can help regulatory authorities to better manage and allocate spectrum resources. By improving the accuracy and efficiency of spectrum sensing, the proposed approach can assist regulatory authorities in identifying and resolving interference issues.

• End-users: Ultimately, the end-users can benefit from the proposed approach by experiencing better wireless communication services with fewer disruptions and improved quality of service.

Applications of the Work:

• Dynamic Spectrum Access: Cooperative spectrum sensing can be used in real-time applications such as dynamic spectrum access, where cognitive radios can opportunistically use underutilized spectrum bands. Cooperative sensing improves the detection performance of the cognitive radio network, enabling it to effectively access unused spectrum.

• Disaster Response Communications: In disaster response scenarios, communication networks can be severely affected, leading to limited spectrum resources. By using cooperative sensing, cognitive radio networks can effectively and efficiently detect unused spectrum and utilize it for emergency communication.

• Military Communications: In military operations, communication is critical and requires reliable and secure channels. Cooperative spectrum sensing can help cognitive radios identify the best available spectrum bands for secure communication.

• Wireless Sensor Networks: Wireless sensor networks require efficient use of spectrum to communicate sensor data in real-time. By using cooperative sensing, these networks can better utilize the available spectrum resources and improve the reliability of data transmission.

• Internet of Things (IoT): The IoT ecosystem consists of numerous connected devices that communicate with each other wirelessly. Cooperative sensing can help cognitive radios identify underutilized spectrum bands for IoT device communication, improving network reliability and reducing interference.

Data Scientists Role:

• Developing Accurate Models: Data scientists can develop accurate deep learning models for cooperative spectrum sensing by using large datasets to train models that can accurately detect the presence or absence of signals in noisy and fading channels. Deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can be used to develop models that can effectively distinguish between different signal types and modulation schemes.

• Improving Sensing Efficiency: Data scientists can also improve the efficiency of spectrum sensing by developing models that can reduce the sensing time required to detect signals. By using deep learning techniques such as transfer learning and online learning, models can be developed that can quickly adapt to changing signal conditions and optimize the use of resources.

• Enhancing Network Security: Data scientists can contribute to enhancing network security by developing models that can detect and mitigate interference and jamming attacks. By using deep learning techniques such as autoencoders and generative adversarial networks (GANs), models can be developed that can effectively detect and classify different types of interference signals.

• Developing New Applications: Data scientists can develop new applications for cooperative spectrum sensing using deep learning techniques by leveraging the capabilities of cognitive radio networks to dynamically adapt to changing signal conditions. For example, deep learning techniques can be used to develop models that can enable cognitive radios to identify the best available spectrum for a particular application or to optimize the allocation of spectrum resources for specific services.

Overall, data scientists play a critical role in the development and application of cooperative spectrum sensing using deep learning techniques

Softwares Used:

• Python: For developing and implementing the deep learning models used for cooperative spectrum sensing.

• TensorFlow: TensorFlow is an open-source software library used for developing and training deep learning models. For developing and implementing the deep learning models used for cooperative spectrum sensing.

• Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow.

• Scikit-learn: To preprocess the dataset and Encode the labels

• MATLAB: For analyzing and simulating Datasets.

• Google Colab: Tool to execute Deep Learning models with cloud storage capabilities

Deep Learning Models used:

• CNN
• DNN
• LSTM
• CNN-LSTM
• GRU

Contact:

If you have any questions or comments about this research project, please feel free to contact us at mpradeepbalaji5@gmail.com