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.

中文翻译：

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5G 中具有不同服务质量要求的无线电资源分配的深度学习

为了适应第 5 代蜂窝网络中不同的服务质量 (QoS) 要求，基站需要在时变网络条件下实时优化无线电资源。这带来了高计算开销和长处理延迟。在这项工作中，我们开发了一个深度学习框架来近似最优资源分配策略，通过优化带宽和发射功率分配来最小化基站的总功耗。我们发现，由于子载波数量的近似误差和量化误差，全连接神经网络 (NN) 不能完全保证 QoS 要求。为了解决这个问题，我们提出了 NN 的级联结构，其中第一个 NN 接近最佳带宽分配，第二神经网络输出在给定带宽分配下满足QoS要求所需的发射功率。考虑到无线网络中无线信道的分布和服务类型是非平稳的，我们应用深度迁移学习来更新非平稳无线网络中的神经网络。仿真结果验证了级联神经网络在 QoS 保证方面优于全连接神经网络。此外，深度迁移学习可以显着减少训练 NN 所需的训练样本数量。仿真结果验证了级联神经网络在 QoS 保证方面优于全连接神经网络。此外，深度迁移学习可以显着减少训练 NN 所需的训练样本数量。仿真结果验证了级联神经网络在 QoS 保证方面优于全连接神经网络。此外，深度迁移学习可以显着减少训练 NN 所需的训练样本数量。