Wireless Communications Systems provide key enabling technologies for much of business, industry, and society. Emerging use cases for mobile networks place increases in demand for data transfer rates and reliability as well as security and privacy at the network layer. Innovations in signal processing techniques enable a response to such challenges, yet there are limits to conventional methods under the extremes of new use cases. Deep learning demonstrates the ability to learn from complex signals which may support flexible designs of new wireless transceivers. The proposed research enables a class of learning algorithms combining offline learning and online adaptation when faced with changes during operation. Developing adaptive wireless communication systems for reliable communication under variable environments and unforeseen use cases.
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