Summary of Learning For Cross-layer Resource Allocation in Mec-aided Cell-free Networks, by Chong Zheng et al.
Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks
by Chong Zheng, Shiwen He, Yongming Huang, Tony Q. S. Quek
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach for joint subcarrier allocation and beamforming optimization in mobile edge computing (MEC)-aided cell-free networks using deep learning techniques to maximize weighted sum rate. The authors convert the underlying problem into a joint multi-task optimization problem and design a centralized multi-task self-supervised learning algorithm to solve it, avoiding costly manual labeling. Two novel loss functions, negative fraction linear loss and exponential linear loss, are designed for self-supervised learning, and a MEC-enabled distributed multi-task self-supervised learning (DMTSSL) algorithm is proposed to address dimensional disaster. The authors also develop a distance-aware transfer learning algorithm based on DMTSSL to handle dynamic scenarios with negligible computation cost. Simulation results under 3rd generation partnership project 38.901 urban-macrocell scenario demonstrate the superiority of the proposed algorithms over baseline algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers explore ways to improve data rates in mobile edge computing networks by optimizing how resources are shared between different devices and parts of the network. They use a new type of machine learning called self-supervised learning to find the best way to share these resources, which is more efficient than previous methods. The authors test their approach using a simulated urban environment and show that it works better than existing methods. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Multi task » Optimization » Self supervised » Transfer learning
