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Technical Perspective: Physical Layer Resilience through Deep Learning in Software Radios

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Resilience is the new holy grail in wireless communication systems. Complex radio environments and malicious attacks using intelligent jamming contribute to unreliable communication systems. Early approaches to deal with such problems were based on frequency hopping, scrambling, chirping, and cognitive radio-based concepts, among others. Physical-layer security was increased using known codes and pseudorandom number sequences. However, these approaches are not up to modern standards; they do not improve resilience and are rather easy to attack by means of intelligent jamming.

Conceptually, dynamic changing waveforms and physical layer parameters would help overcoming many of these issues. However, almost all modern radio technologies are rather inflexible when it comes to changing physical layer parameters on the fly. For example, Bluetooth is limited to frequency hopping, and Wi-Fi to switching channels and modulation/encoding schemes based on active scanning. What is needed is a system that continuously changes physical layer configurations, that is, carrier frequency, FFT size, symbol modulation, and even the position of header and pilots. This way, the overall resilience of the wireless system would be improved significantly.


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