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

TL;DR

This paper introduces a deep learning approach to optimize beamforming and fronthaul quantization in C-RAN systems, replacing complex iterative algorithms with a neural network for faster, efficient solutions.

Contribution

It proposes a novel deep neural network model that efficiently approximates optimal beamforming and quantization strategies in C-RAN, reducing computational complexity.

Findings

The DNN effectively approximates optimal strategies.

Numerical results show improved computational efficiency.

The approach outperforms traditional iterative algorithms.

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.