Application of Deep Learning in Deep Space Wireless Signal Identification for Intelligent Channel Sensing

The growing need for connectivity is a significant challenge to fulfill as the spectrum is a limited resource. Underutilized spectrum such as TV can be used to address this challenge. By employing an opportunistic technology where unlicensed users can make use of the channel in the absence of the licensed user, can make an enormous difference. Cognitive Radio (CR) has been invented from this idea of sharing the channel. CR Network (CRN) can make it possible to utilize the spectrum to t’s full potential.Along with over the air communication, deep space communication also looking into the CRN technology to maximize the channel utilization. NASA had deployed a testbed known as SCaN testbed attached to the International Space Station, to accommodate researches on deep space communications. In deep communication lower scheme modulations are used because of the low SNR value to maintain the signal quality. Reconfigurable radio technology like CRN can be used to increase the throughput by changing the modulation scheme w.r.t better SNR value. Whenever, the SNR value for the channel improves, the radio can change the modulation to improve the channel throughput. However, CRN comes with all the traditional wireless security threats and some additional security challenges such as PUEA and SSDF that exploits the vulnerabilities of Spectrum Sensing and cognitive capability of the CRN. To ensure a robust technology to use in the space communication, these challenges need to be addressed and mitigation techniques need to implemented. PUEA is such an attack where an adversary emulates the primary signal and deprives other SUs from discovering the channel. In a CRN, it is important to identify the signal for threat assessment which can help the ICS to address the PUEA attack by taking security measures. In our work, we are exploring the signal identification process by providing studies on modulation classification.As a part of funded project, we got access to captured data from NASA SCaN testbed Ka-Band channel. This dataset contains two types of modulation scheme, which we have processed to classify with Deep learning methods like Feed Forward fully connected MLP Network and Convolutional Neural Network (CNN). To extract different features from the raw complex datset we have transformed it to three other datasets and trained them with two different model. We compared the results to find which feature works better with which deep learning model. MLP resulted in an F-1 score of 0.96 and 0.95 for IQ and FFT representation of the complex dataset, where CNN resulted in 0.97 and 0.98, respectively.