# Deep Delay Loop Reservoir Computing for Specific Emitter Identification

arXiv: https://arxiv.org/abs/2010.06649 · HTML: https://mlsanity.com/paper/2010.06649/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Reservoir-Based Distributed Machine Learning for Edge Operation](https://mlsanity.com/paper/2104.00751/) (2021)
- [Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors](https://mlsanity.com/paper/2106.16087/) (2021)

## Abstract (arXiv)

Current AI systems at the tactical edge lack the computational resources to support in-situ training and inference for situational awareness, and it is not always practical to leverage backhaul resources due to security, bandwidth, and mission latency requirements. We propose a solution through Deep delay Loop Reservoir Computing (DLR), a processing architecture supporting general machine learning algorithms on compact mobile devices by leveraging delay-loop (DL) reservoir computing in combination with innovative photonic hardware exploiting the inherent speed, and spatial, temporal and wavelength-based processing diversity of signals in the optical domain. DLR delivers reductions in form factor, hardware complexity, power consumption and latency, compared to State-of-the-Art . DLR can be implemented with a single photonic DL and a few electro-optical components. In certain cases multiple DL layers increase learning capacity of the DLR with no added latency. We demonstrate the advantages of DLR on the application of RF Specific Emitter Identification.