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

@article{Yu2021DeepLM,
  title={Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks},
  author={Daesung Yu and Hoon Lee and Seok-Hwan Park and Seung-Eun Hong},
  journal={IEEE Wireless Communications Letters},
  year={2021},
  volume={10},
  pages={2180-2184}
}
  • Daesung Yu, Hoon Lee, +1 author Seung-Eun Hong
  • Published 2021
  • Computer Science, Engineering, Mathematics
  • IEEE Wireless Communications Letters
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… Expand

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