Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks

• URL: http://arxiv.org/abs/2107.02520v1
• Date: Tue, 6 Jul 2021 10:27:43 GMT
• Title: Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks
• Authors: Daesung Yu, Hoon Lee, Seok-Hwan Park, Seung-Eun Hong
• Abstract summary: Cooperative beamforming across points (APs) and fronthaulization strategies are essential for cloud radio network (C-RAN) systems. Non-dimensional quantity problem is stemmed from per-AP power and fronthaul capacity constraints. We investigate a deep learning optimization module is where well-trained deep neural network (DNN) Numerical results validate the advantages of the proposed learning solution.
• Score: 12.838832724944615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cooperative beamforming across access points (APs) and fronthaul quantization strategies are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP power and fronthaul capacity constraints, requires high computational complexity for executing iterative algorithms. To resolve this issue, we investigate a deep learning approach where the optimization module is replaced with a well-trained deep neural network (DNN). An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies. Numerical results validate the advantages of the proposed learning solution.

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