Communication systems using neural network-based transceivers with autoencoder-driven end-to-end learning

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The fields of machine learning and, in particular, deep learning have seen very rapid growth during the past few years; their applications now extend into almost every industry and research domain. Although researchers have tried to address communications-related problems with machine learning in the past, it still has had no fundamental impact on the way we design and implement communications systems today. At first glance, machine learning techniques do not appear to be a good match to communications on the physical layer, with 50 years of tremendous progress based on "classic" signal processing, communication and information theory, approaching close-to-optimal Shannon limit performance on many channels. However, several open problems remain, e.g. pertaining adaptivity and complexity of joint processing, where first results using machine learning-based approaches are promising. This proposal seeks to examine learnable end-to-end communications based on the "autoencoder" concept and deep learning techniques. It promises a communications system that can learn to communicate over any type of channel without the need for detailed prior mathematical abstraction of the channel model, breaking up restrictions commonplace in conventional block-based signal processing by moving away from handcrafted, carefully optimized sub-blocks towards adaptive and flexible (artificial) neural networks, leading to many attractive research questions. To obtain a more comprehensive understanding of the potential of machine learning techniques for communications, we start off from classic signal processing as a reference; then we study neural networks using conventional block-based learning (replacing, e.g. classic modulation, detection, or equalization blocks, ...), until finally arriving at multi-block neural networks based on autoencoder-driven end-to-end learning. We also plan to validate the explored concepts by over-the-air measurements, giving rise to many effects on the communication channel that cannot be found in many classical models, and, thus, need to be learned implicitly. The benefits of machine learning approaches may include more flexible hardware, highly adaptive systems and less overall complexity. We thus pose the seemingly naive, yet, in fact, rather complicated and attractive research question: "Can we learn to communicate?"Note that this proposal targets physical layer transmission without any further respect to the semantics of the message itself (i.e. no “understanding” of the message or its content is trained).