In wireless communication systems, a received signal is corrupted by various means, such as noise, multi-path fading, and defects in hardware. To properly demodulate the signal and recover information, complex systems are used. This typically consists of a series of filtering, corrections, timing recovery, and finally demodulation. Furthermore, the approaches for each stage are application specific. Deep learning (DL) can be applied to create an automatic demodulator, independent of modulation type, with no preprocessing, replacing the complex traditional system. However, these systems can only handle scenarios that are incorporated at the initial training stage. If new modulation types are encountered, the system must be re-trained to adapt. Traditional DL systems require the entire original dataset to retain old information, which increases storage requirements and training time. To increase adaptability, we incorporate incremental learning (IL) into a DL demodulator. Incremental learning attempts to overcome these issues, allowing a system to train on only new information. We apply IL to learn to demodulate new modulation types, not initially introduced to this system. We demonstrate this system in the field through the use of software defined radio. The system is subjected to unknown modulation types, and shown to adapt in real-time and over-the-air in an unsupervised environment.
Jamming, whether intentional or not, threatens stable wireless communications by impeding a transmitted signal. New jamming technologies are regularly developed and deployed for use, which behave differently from their predecessors in order to bypass defense mechanisms. This makes existing jammer classifiers difficult to implement in fast changing dynamic environments, since human intervention is needed every time a new jamming technology is introduced. Improper maintenance will result in misclassification of the technology or allow jammers to pass through defenses. These scenarios will greatly reduce the performance of wireless networks and increase the response time for recovering from these attacks. As 5G continues to become more widespread, and other faster networks are released, wireless data rates will continue to grow. This furthers the need for a faster and more reliable jammer classifier, as shorter interruptions in service will cause even more data loss to occur. Incremental learning (IL) is a technique in machine learning that allows the introduction of new information to a previously trained network. Using IL, it is possible to create classifiers that can grow in number of classes without the need to retrain a new network from nothing. This allows remote devices to learn to adapt in dynamic environments with far lower memory cost. In this paper, we developed an IL-based jammer classifier using software defined radio (SDR) to detect when a jammer is present and classify the type and learn to classify new technologies when the type has not been encountered before.
Conference Committee Involvement (7)
Radar Sensor Technology XXVII
1 May 2023 | Orlando, Florida, United States
Radar Sensor Technology XXVI
4 April 2022 | Orlando, Florida, United States
Radar Sensor Technology XXV
12 April 2021 | Online Only, Florida, United States
Radar Sensor Technology XXIV
27 April 2020 | Online Only, California, United States
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