Toward Data-Driven Self-Adaptive Spectrum-Aware Wireless Systems

The massive scale and strict performance requirements of next-generation wireless networks will require embedded devices to perform real-time fine-grained optimization of their spectrum usage. Yet, today's networking protocols and architectures are deeply rooted in inflexible designs, and utilize optimization models and strategies that are either too complex or too oversimplified to be fully effective in today's crowded spectrum environment. In this talk, we are going to introduce and discuss our recent research toward the design of data-driven self-adaptive spectrum-aware wireless systems, where transmitters and receivers use real-time deep learning to infer and optimize their networking parameters based on ongoing spectrum conditions. We will conclude the talk by discussing existing technical challenges and possible research directions.


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