# Deep Reinforcement Learning Based Cross-Layer Design in Terahertz Mesh Backhaul Networks

arXiv: https://arxiv.org/abs/2310.05034 · HTML: https://mlsanity.com/paper/2310.05034/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 8 Oct 2023.

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

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee](https://mlsanity.com/paper/2011.05869/) (2020)
- [Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning](https://mlsanity.com/paper/2012.01991/) (2020)
- [TeraHertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade](https://mlsanity.com/paper/2112.13187/) (2021)
- [Deep Reinforcement Learning Based Spectrum Allocation in Integrated Access and Backhaul Networks](https://mlsanity.com/paper/2004.13133/) (2020)
- [Constrained Deep Reinforcement Based Functional Split Optimization in Virtualized RANs](https://mlsanity.com/paper/2106.00011/) (2021)
- [Terahertz Technologies to Deliver Optical Network Quality of Experience in Wireless Systems Beyond 5G](https://mlsanity.com/paper/1803.09060/) (2018)

## Built on by (most cited first, 2 of 2)

- [Tera-SpaceCom: GNN-based Deep Reinforcement Learning for Joint Resource Allocation and Task Offloading in TeraHertz Band Space Networks](https://mlsanity.com/paper/2409.07911/) (2024)
- [Graph Neural Network Aided Deep Reinforcement Learning for Resource Allocation in Dynamic Terahertz UAV Networks](https://mlsanity.com/paper/2505.04981/) (2025)

## Abstract (arXiv)

Supporting ultra-high data rates and flexible reconfigurability, Terahertz (THz) mesh networks are attractive for next-generation wireless backhaul systems that empower the integrated access and backhaul (IAB). In THz mesh backhaul networks, the efficient cross-layer routing and long-term resource allocation is yet an open problem due to dynamic traffic demands as well as possible link failures caused by the high directivity and high non-line-of-sight (NLoS) path loss of THz spectrum. In addition, unpredictable data traffic and the mixed integer programming property with the NP-hard nature further challenge the effective routing and long-term resource allocation design. In this paper, a deep reinforcement learning (DRL) based cross-layer design in THz mesh backhaul networks (DEFLECT) is proposed, by considering dynamic traffic demands and possible sudden link failures. In DEFLECT, a heuristic routing metric is first devised to facilitate resource efficiency (RE) enhancement regarding energy and sub-array usages. Furthermore, a DRL based resource allocation algorithm is developed to realize long-term RE maximization and fast recovery from broken links. Specifically in the DRL method, the exploited multi-task structure cooperatively benefits joint power and sub-array allocation. Additionally, the leveraged hierarchical architecture realizes tailored resource allocation for each base station and learned knowledge transfer for fast recovery. Simulation results show that DEFLECT routing consumes less resource, compared to the minimal hop-count metric. Moreover, unlike conventional DRL methods causing packet loss and second-level latency, DEFLECT DRL realizes the long-term RE maximization with no packet loss and millisecond-level latency, and recovers resource-efficient backhaul from broken links within 1s.