Neural Estimation and Optimization of Directed Information over Continuous Spaces [article]

Dor Tsur, Ziv Aharoni, Ziv Goldfeld, Haim Permuter
2022 arXiv   pre-print
This work develops a new method for estimating and optimizing the directed information rate between two jointly stationary and ergodic stochastic processes. Building upon recent advances in machine learning, we propose a recurrent neural network (RNN)-based estimator which is optimized via gradient ascent over the RNN parameters. The estimator does not require prior knowledge of the underlying joint and marginal distributions. The estimator is also readily optimized over continuous input
more » ... es realized by a deep generative model. We prove consistency of the proposed estimation and optimization methods and combine them to obtain end-to-end performance guarantees. Applications for channel capacity estimation of continuous channels with memory are explored, and empirical results demonstrating the scalability and accuracy of our method are provided. When the channel is memoryless, we investigate the mapping learned by the optimized input generator.
arXiv:2203.14743v1 fatcat:irj45fufdjdhxnarqzkoluxz3a