mlsanityData to 14 Oct 2026

Decentralized Covert Routing in Heterogeneous Networks Using Reinforcement Learning

arXiv 2402.10087 ↗First posted 31 Jan 2024cs.NI cs.LG eess.SPcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

Key facts. Cited by 1 paper 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.

Builds on

3 of 20 references.

References · 3

  1. 2019Secrecy and Covert Communications against UAV Surveillance via Multi-Hop Networkscited by 3
  2. 2018Multi-Hop Routing in Covert Wireless Networkscited by 2
  3. 2023Covert Communication in Hybrid Microwave/mmWave A2G Systems with Transmission Mode Selectioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETscited by 0

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