# A Deep Learning Approach To Estimation Using Measurements Received Over a Network

arXiv: https://arxiv.org/abs/2201.08020 · HTML: https://mlsanity.com/paper/2201.08020/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Memory-based Deep Reinforcement Learning for POMDPs](https://mlsanity.com/paper/2102.12344/) (2021)
- [Learning Dynamical Systems from Partial Observations](https://mlsanity.com/paper/1902.11136/) (2019)
- [Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems](https://mlsanity.com/paper/1809.05149/) (2018)
- [A Speculative Study on 6G](https://mlsanity.com/paper/1902.06700/) (2019)
- [Wireless Network Design for Control Systems: A Survey](https://mlsanity.com/paper/1708.07353/) (2017)
- [Analysing the Data-Driven Approach of Dynamically Estimating Positioning Accuracy](https://mlsanity.com/paper/2011.10478/) (2020)
- [Networked Control of Nonlinear Systems under Partial Observation Using Continuous Deep Q-Learning](https://mlsanity.com/paper/1908.10722/) (2019)

## Abstract (arXiv)

We propose a novel deep neural network (DNN) based approximation architecture to learn estimates of measurements. We detail an algorithm that enables training of the DNN. The DNN estimator only uses measurements, if and when they are received over a communication network. The measurements are communicated over a network as packets, at a rate unknown to the estimator. Packets may suffer drops and need retransmission. They may suffer waiting delays as they traverse a network path. Works on estimation often assume knowledge of the dynamic model of the measured system, which may not be available in practice. The DNN estimator doesn't assume knowledge of the dynamic system model or the communication network. It doesn't require a history of measurements, often used by other works. The DNN estimator results in significantly smaller average estimation error than the commonly used Time-varying Kalman Filter and the Unscented Kalman Filter, in simulations of linear and nonlinear dynamic systems. The DNN need not be trained separately for different communications network settings. It is robust to errors in estimation of network delays that occur due to imperfect time synchronization between the measurement source and the estimator. Last but not the least, our simulations shed light on the rate of updates that result in low estimation error.