# Robust Communication and Computation using Deep Learning via Joint Uncertainty Injection

arXiv: https://arxiv.org/abs/2406.03548 · HTML: https://mlsanity.com/paper/2406.03548/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021)
- [Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design](https://mlsanity.com/paper/2003.00199/) (2020)
- [Uncertainty Injection: A Deep Learning Method for Robust Optimization](https://mlsanity.com/paper/2302.12304/) (2023)
- [Joint Offloading and Resource Allocation for Hybrid Cloud and Edge Computing in SAGINs: A Decision Assisted Hybrid Action Space Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2401.01140/) (2024)
- [Extended Reality via Cooperative NOMA in Hybrid Cloud/Mobile-Edge Computing Networks](https://mlsanity.com/paper/2310.06874/) (2023)
- [Resource Allocation Under Channel Uncertainties for Relay-Aided Device-to-Device Communication Underlaying LTE-A Cellular Networks](https://mlsanity.com/paper/1401.6683/) (2014)
- [Latency-Aware Radio Resource Optimization in Learning-Based Cloud-Aided Small Cell Wireless Networks](https://mlsanity.com/paper/2309.11892/) (2023)

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

- [Deep Unfolded Fractional Optimization for Maximizing Robust Throughput in 6G Networks](https://mlsanity.com/paper/2602.06062/) (2026)
- [Agentic AI-Based Joint Computing and Networking via Mixture of Experts and Large Language Models](https://mlsanity.com/paper/2605.02911/) (2026)

## Abstract (arXiv)

The convergence of communication and computation, along with the integration of machine learning and artificial intelligence, stand as key empowering pillars for the sixth-generation of communication systems (6G). This paper considers a network of one base station serving a number of devices simultaneously using spatial multiplexing. The paper then presents an innovative deep learning-based approach to simultaneously manage the transmit and computing powers, alongside computation allocation, amidst uncertainties in both channel and computing states information. More specifically, the paper aims at proposing a robust solution that minimizes the worst-case delay across the served devices subject to computation and power constraints. The paper uses a deep neural network (DNN)-based solution that maps estimated channels and computation requirements to optimized resource allocations. During training, uncertainty samples are injected after the DNN output to jointly account for both communication and computation estimation errors. The DNN is then trained via backpropagation using the robust utility, thus implicitly learning the uncertainty distributions. Our results validate the enhanced robust delay performance of the joint uncertainty injection versus the classical DNN approach, especially in high channel and computational uncertainty regimes.