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Innovation in the physical layer of communication systems has traditionallybeen achieved by breaking down the transceivers into sets of processing blocks,each optimized independently based on mathematical models. Conversely, deeplearning (DL)-based systems are able to handle increasingly complex tasks forwhich no tractable models are available. This thesis aims at comparingdifferent approaches to unlock the full potential of DL in the physical layer.  First, we describe a neural network (NN)-based block strategy, where an NN isoptimized to replace a block in a communication system. We apply this strategyto introduce a multi-user multiple-input multiple-output (MU-MIMO) detectorthat builds on top of an existing DL-based architecture. Second, we detail anend-to-end strategy, in which the transmitter and receiver are modeled as anautoencoder. This approach is illustrated with the design of waveforms thatachieve high throughputs while satisfying peak-to-average power ratio (PAPR)and adjacent channel leakage ratio (ACLR) constraints. Lastly, we propose ahybrid strategy, where multiple DL components are inserted into a traditionalarchitecture but are trained to optimize the end-to-end performance. Todemonstrate its benefits, we propose a DL-enhanced MU-MIMO receiver that bothenable lower bit error rates (BERs) compared to a conventional receiver andremains scalable to any number of users.  Each approach has its own strengths and shortcomings. While the first one isthe easiest to implement, its individual block optimization does not ensure theoverall system optimality. On the other hand, systems designed with the secondapproach are computationally complex but allow for new opportunities such aspilotless transmissions. Finally, the combined flexibility and end-to-endperformance gains of the third approach motivate its use for short-termpractical implementations.

Applications of Deep Learning to the Design of Enhanced Wireless Communication Systems