Design
and analysis of communication systems have traditionally relied on
mathematical and statistical channel models that describe how a signal
is corrupted during transmission. In particular, communication
techniques such as modulation, coding and detection that mitigate
performance degradation due to channel impairments are based on such
channel models and, in some cases, instantaneous channel state
information about the model. However, there are propagation environments
where this approach does not work well because the underlying physical
channel is too complicated, poorly understood, or rapidly time-varying.
In these scenarios we propose a completely new approach to
communication system design based on machine learning (ML). In this
approach, the design of a particular component of the communication
system (e.g. the coding strategy or the detection algorithm) utilizes
tools from ML to learn and refine the design directly from training
data. The training data that is used in this ML approach can be
generated through models, simulations, or field measurements. We present
results for three communication design problems where the ML approach
results in better performance than current state-of-the-art techniques:
signal detection without accurate channel state information, signal
detection without a mathematical channel model, and joint source-channel
coding of text.
Broader application of ML to communication system design in general
and to millimeter wave and molecular communication systems in particular
is also discussed.
Honorary Degree
Andrea Goldmsith received the honorary degree of Doctor of Science from the University on Wednesday 4th July.
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