Some find the (relatively) new tools a liberation from the time-consuming task of acquiring domain knowledge and advanced analytical skills. For this camp, machine learning shifts the engineering problem from the traditional model-based optimization of individual functionalities to an overall data-driven end-to-end design.
Others hold the reduction of communication engineering to the choice of objective functions and hyperparameters of general-purpose machine learning algorithms to be disturbing and demeaning. This second camp views machine learning as an excuse to cop out on the engineering responsibilities of ensuring reliable and verifiable performance guarantees.
And yet, to continue with the initial paraphrase: Where is the research group that has not attempted to apply machine learning to well-studied communication problems? Where is the communication researcher who has not tweaked hyperparameters so as to reproduce known engineering solutions?
Some perspective seems needed in order to find a beneficial synthesis between the two extreme viewpoints that may open new opportunities rather than transform communication engineering into a branch of computer science. To this end, I would propose that the use of machine learning in communications is justified when:
1) a good mathematical model exists for the problem at hand, and:
- an objective function is hard to define or to optimize mathematically (even approximately);
- it is easy to draw data samples from the model;
- it is easy to evaluate desired pairs of well-specified input and output pairs (supervised learning), or to provide feedback on sequentially selected outputs (reinforcement learning);
- the task does not required detailed explanations or performance guarantees;
or 2) a good mathematical model does not exist, and:
- it is easy to collect real-world data;
- the phenomenon under study does not change rapidly over time;
- it is easy to evaluate desired pairs of well-specified input and output pairs (supervised learning), to assess the quality of the generated output (unsupervised learning), or to provide feedback on sequentially selected outputs (reinforcement learning);
- the task does not required detailed explanations or performance guarantees.
Even when the use of machine learning methods is justified, the adoption of a data-driven approach does not entail the complete rejection of domain knowledge and engineering principles. As a case in point, the decoding of a dense channel code via efficient message passing schemes is known to fail in general. Nevertheless, the structure of a message passing algorithm can still be leveraged to define a parametrized scheme that can be tuned based on input-output examples.
More broadly, engineering a modern communication system is likely to call for a macro-classification of subtasks for which either model- or data-driven approaches are most suitable, followed by a micro-design of individual subproblems and by a possible end-to-end fine-tuning of parameters via learning methods.
Computer scientists and communication engineers of all countries, unite?