Cognitive Radio:

Signal Detection and Interference Avoidance

Abstract:

Software defined radio is an existing radio communication system where functionality that normally requires large amounts of hardware can be accomplished with solely software instead. Software defined radio is more flexible than traditional radios, allowing for multiple systems under a single reconfigurable platform. It can be made more efficient with the implementation of machine learning. Machine learning provides the system with the ability to learn and improve, to adapt to network and geographic operating conditions. Currently, software defined radio is most appropriate for the military/defense sector where adaptability in an uncertain battlefield is necessary. SDRs are capable of identifying, interfering, and avoiding wireless signals. The users will be able to see what signals exist in their geographical area giving them a tactical advantage in electronic warfare. Implementing machine learning gives the radio the capability to learn its function without being programmed. This will be done by integrating TensorFlow, a machine learning open source library, with GNU Radio, an open source library for SDRs.

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