Learning to Learn to Demodulate with Uncertainty Quantification via
Bayesian Meta-Learning
- URL: http://arxiv.org/abs/2108.00785v1
- Date: Mon, 2 Aug 2021 11:07:46 GMT
- Title: Learning to Learn to Demodulate with Uncertainty Quantification via
Bayesian Meta-Learning
- Authors: Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai (Shitz)
- Abstract summary: We introduce the use of Bayesian meta-learning via variational inference for the purpose of obtaining well-calibrated few-pilot demodulators.
The resulting Bayesian ensembles offer better calibrated soft decisions, at the computational cost of running multiple instances of the neural network for demodulation.
- Score: 59.014197664747165
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Meta-learning, or learning to learn, offers a principled framework for
few-shot learning. It leverages data from multiple related learning tasks to
infer an inductive bias that enables fast adaptation on a new task. The
application of meta-learning was recently proposed for learning how to
demodulate from few pilots. The idea is to use pilots received and stored for
offline use from multiple devices in order to meta-learn an adaptation
procedure with the aim of speeding up online training on new devices. Standard
frequentist learning, which can yield relatively accurate "hard" classification
decisions, is known to be poorly calibrated, particularly in the small-data
regime. Poor calibration implies that the soft scores output by the demodulator
are inaccurate estimates of the true probability of correct demodulation. In
this work, we introduce the use of Bayesian meta-learning via variational
inference for the purpose of obtaining well-calibrated few-pilot demodulators.
In a Bayesian framework, each neural network weight is represented by a
distribution, capturing epistemic uncertainty. Bayesian meta-learning optimizes
over the prior distribution of the weights. The resulting Bayesian ensembles
offer better calibrated soft decisions, at the computational cost of running
multiple instances of the neural network for demodulation. Numerical results
for single-input single-output Rayleigh fading channels with transmitter's
non-linearities are provided that compare symbol error rate and expected
calibration error for both frequentist and Bayesian meta-learning, illustrating
how the latter is both more accurate and better-calibrated.
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