# Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks

arXiv: https://arxiv.org/abs/2412.16565 · HTML: https://mlsanity.com/paper/2412.16565/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 21 Dec 2024.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [The Road Towards 6G: A Comprehensive Survey](https://mlsanity.com/paper/2102.01420/) (2021)
- [Edge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions](https://mlsanity.com/paper/2012.06992/) (2020)
- [Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching](https://mlsanity.com/paper/2110.10349/) (2021)
- [User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions](https://mlsanity.com/paper/2104.14589/) (2021)
- [Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels](https://mlsanity.com/paper/2010.04061/) (2020)
- [Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service](https://mlsanity.com/paper/2104.01036/) (2021)
- [Subcarrier Assignment Schemes Based on Q-Learning in Wideband Cognitive Radio Networks](https://mlsanity.com/paper/1911.07149/) (2019)
- [Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks](https://mlsanity.com/paper/2205.10791/) (2022)
- [Intelligent Reflecting Surface-Aided Joint Processing Coordinated Multipoint Transmission](https://mlsanity.com/paper/2003.13909/) (2020)
- [Joint User Scheduling and Beamforming Design for Multiuser MISO Downlink Systems](https://mlsanity.com/paper/2112.01738/) (2021)
- [A Joint Solution for Scheduling and Precoding in Multiuser MISO Downlink Channels](https://mlsanity.com/paper/1902.04415/) (2019)
- [Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation,Phase Shifts, and Hybrid Beamforming Optimization](https://mlsanity.com/paper/2007.05873/) (2020)
- [Robust Energy-Efficient Resource Management, SIC Ordering, and Beamforming Design for MC MISO-NOMA Enabled 6G](https://mlsanity.com/paper/2101.06799/) (2021)
- [CoMP Enhanced Subcarrier and Power Allocation for Multi-Numerology based 5G-NR Networks](https://mlsanity.com/paper/2112.04070/) (2021)

## Abstract (arXiv)

Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate. However, the technical bottlenecks of traditional methods pose significant challenges to cross-layer optimization. In this paper, joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning to maximize the weighted sum rate. Specifically, we convert the underlying problem into a joint multi-task optimization problem and then propose a centralized multi-task self-supervised learning algorithm to solve the problem so as to avoid costly manual labeling. Therein, two novel and general loss functions, i.e., negative fraction linear loss and exponential linear loss whose advantages in robustness and target domain have been proved and discussed, are designed to enable self-supervised learning. Moreover, we further design a MEC-enabled distributed multi-task self-supervised learning (DMTSSL) algorithm, with low complexity and high scalability to address the challenge of dimensional disaster. Finally, we develop the distance-aware transfer learning algorithm based on the DMTSSL algorithm to handle the dynamic scenario with negligible computation cost. Simulation results under 3rd generation partnership project 38.901 urban-macrocell scenario demonstrate the superiority of the proposed algorithms over the baseline algorithms.