# Decentralized Covert Routing in Heterogeneous Networks Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2402.10087 · HTML: https://mlsanity.com/paper/2402.10087/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 3 of 3)

- [Secrecy and Covert Communications against UAV Surveillance via Multi-Hop Networks](https://mlsanity.com/paper/1910.09197/) (2019)
- [Multi-Hop Routing in Covert Wireless Networks](https://mlsanity.com/paper/1803.04100/) (2018)
- [Covert Communication in Hybrid Microwave/mmWave A2G Systems with Transmission Mode Selection](https://mlsanity.com/paper/2302.00301/) (2023)

## Built on by (most cited first, 1 of 1)

- [MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETs](https://mlsanity.com/paper/2609.40170/) (2026)

## Abstract (arXiv)

This letter investigates covert routing communications in a heterogeneous network where a source transmits confidential data to a destination with the aid of relaying nodes where each transmitter judiciously chooses one modality among multiple communication modalities. We develop a novel reinforcement learning-based covert routing algorithm that finds a route from the source to the destination where each node identifies its next hop and modality only based on the local feedback information received from its neighboring nodes. We show based on numerical simulations that the proposed covert routing strategy has only negligible performance loss compared to the optimal centralized routing scheme.