ABSTRACT

The project is a part of R&D program that is aimed to investigate the applicability of RNN as a hardware unit in impulse radio receivers. It uses the timeseries that is a simulation of impulse propagation and receiving as learning-validation dataset to study the applicability. The time series can be generated by another open source project "Maxwell" that takes into account all electrodynamic aspects of radiation, propagation, reflection, and adsorbing of impulse signals. It also allows to simulate a noise of signal of different types with arbitrary parameters.

ADVANTAGES OF THE APPROACH

It is supposed that this technology allows to open new applications of impulse radio due to better recognition of EMP sparks then is provided by existing hardware. Also, we are going to investigate symbiotic usage of RNNs and existing receiving electronics.

Generally, it is supposed that impulse signal classification by RNN may allow:

1. solving of the multipath problem in impulse radio;
2. solving of the multiuser problem in impulse radio;
3. obtaining the AWGN channel capacity gain in impulse radio;
4. obtaining the accuracy gain in radar problem;
5. cost optimization for receiving hardware units in impulse radio;
6. ability to update receiving hardware units in impulse radio for free;
7. building of high-radix communication protocol of channel level.

It is suggested that following advantages is opening new opportunists like:

1. fast and secure and low energy impulsed near field communication (NFC);
2. more accuracy and less cost of short range radar;
3. combined short range radar and remote sensing system;
4. new generation of wireless USB devises with high-radix communication protocol.

DATASET DESCRIPTION

The dataset is a time line of a received signal magnitudes. The signal is a sequential arrange of EMPs. Multipath and multiuser network scheme can be simulated by combination of datasets. Every point of the dataset contains not only the magnitude but also an information about location of point of observation and shape of impulse excitation. The dataset format is JSON file in ASCII encoding.

THEORETICAL JUSTIFICATION

Impulse shape depends from direction of the observation. So, the matching of the initial form of time dependency of excitation to the shape of EMP is a classification problem. Transient respond (TR) of the signal and a Duhamel's integral allow to build learning dataset for an arbitrary shape of the excitation for solving the classification problem by a supervised machine learning.

ENVIRONMENT INSTALATION GUIDE FOR UBUNTU 16.04 x64

~ $ 

USER INSTRUCTIONS

~ $