To accommodate diverse Quality-of-Service (QoS) requirements in the 5th
generation cellular networks, base stations need real-time optimization of
radio resources in time-varying network conditions. This brings high computing
overheads and long processing delays. In this work, we develop a deep learning
framework to approximate the optimal resource allocation policy that minimizes
the total power consumption of a base station by optimizing bandwidth and
transmit power allocation. We find that a fully-connected neural network (NN)
cannot fully guarantee the QoS requirements due to the approximation errors and
quantization errors of the numbers of subcarriers. To tackle this problem, we
propose a cascaded structure of NNs, where the first NN approximates the
optimal bandwidth allocation, and the second NN outputs the transmit power
required to satisfy the QoS requirement with given bandwidth allocation.
Considering that the distribution of wireless channels and the types of
services in the wireless networks are non-stationary, we apply deep transfer
learning to update NNs in non-stationary wireless networks. Simulation results
validate that the cascaded NNs outperform the fully connected NN in terms of
QoS guarantee. In addition, deep transfer learning can reduce the number of
training samples required to train the NNs remarkably.

本文提出了一种基于深度学习的资源分配策略，通过优化带宽和传输功率来最小化基站的总功耗，并使用级联结构的神经网络和深度迁移学习来应对无线网络中的非平稳性。实验结果表明，相对于全连接神经网络，在 QoS 保证方面，级联神经网络的性能更好。