Machine Learning for Large-Scale Optimization in 6G Wireless Networks
- URL: http://arxiv.org/abs/2301.03377v1
- Date: Tue, 3 Jan 2023 13:56:50 GMT
- Title: Machine Learning for Large-Scale Optimization in 6G Wireless Networks
- Authors: Yandong Shi, Lixiang Lian, Yuanming Shi, Zixin Wang, Yong Zhou, Liqun
Fu, Lin Bai, Jun Zhang and Wei Zhang
- Abstract summary: 6G wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence"
Machine learning (ML) stands out as a promising and viable methodology for many complex large-scale optimization problems in 6G.
In this paper, we systematically review the most representative "learning to optimize" techniques in diverse domains of 6G wireless networks.
- Score: 27.947500165976486
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The sixth generation (6G) wireless systems are envisioned to enable the
paradigm shift from "connected things" to "connected intelligence", featured by
ultra high density, large-scale, dynamic heterogeneity, diversified functional
requirements and machine learning capabilities, which leads to a growing need
for highly efficient intelligent algorithms. The classic optimization-based
algorithms usually require highly precise mathematical model of data links and
suffer from poor performance with high computational cost in realistic 6G
applications. Based on domain knowledge (e.g., optimization models and
theoretical tools), machine learning (ML) stands out as a promising and viable
methodology for many complex large-scale optimization problems in 6G, due to
its superior performance, generalizability, computational efficiency and
robustness. In this paper, we systematically review the most representative
"learning to optimize" techniques in diverse domains of 6G wireless networks by
identifying the inherent feature of the underlying optimization problem and
investigating the specifically designed ML frameworks from the perspective of
optimization. In particular, we will cover algorithm unrolling, learning to
branch-and-bound, graph neural network for structured optimization, deep
reinforcement learning for stochastic optimization, end-to-end learning for
semantic optimization, as well as federated learning for distributed
optimization, for solving challenging large-scale optimization problems arising
from various important wireless applications. Through the in-depth discussion,
we shed light on the excellent performance of ML-based optimization algorithms
with respect to the classical methods, and provide insightful guidance to
develop advanced ML techniques in 6G networks.
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