Wireless community networks (WCNs) are large, heterogeneous, dynamic, and decentralized networks. Such complex characteristics raise different challenges, such as the effect of wireless communications on the performance of networks and routing protocols. The prediction approaches of link quality (LQ) can improve the performance of routing algorithms of WCNs while avoiding weak links. The prediction of LQ in WCNs can be a complex task because of the fluctuated nature of LQ measurements due to the dynamic wireless environment. In this paper, a deep learning based approach is proposed to accurately predict LQ in WCNs. Specifically, we propose the use of two variants of deep recurrent neural network (RNN): long short-term memory recurrent neural networks (LSTM-RNN) and gated recurrent unit (GRU). The positive feature of the proposed variants is that they can handle the fluctuating nature of LQ due to their ability to learn and exploit the context in LQ time-series. The experimental results on data collected from a real-world WCN show that the proposed LSTM-RNN and GRU models accurately predict LQ in WCNs compared to related methods. The proposed approach could be a helpful tool for accurately predicting LQ, thereby improving the performance of routing protocols of WCNs.

无线社区网络（WCN）是大型，异构，动态和分散的网络。这种复杂的特性带来了不同的挑战，例如无线通信对网络和路由协议性能的影响。链路质量（LQ）的预测方法可以提高WCN路由算法的性能，同时避免弱链路。由于动态无线环境导致LQ测量的波动性，WCN中LQ的预测可能是一项复杂的任务。在本文中，提出了一种基于深度学习的方法来准确预测WCN中的LQ。具体来说，我们建议使用深度递归神经网络（RNN）的两种变体：长短期记忆递归神经网络（LSTM-RNN）和门控递归单元（GRU）。所提出的变体的积极特征是，由于它们学习和利用LQ时间序列中的上下文的能力，它们可以处理LQ的波动性质。从真实世界的WCN收集的数据的实验结果表明，与相关方法相比，所提出的LSTM-RNN和GRU模型可以准确预测WCN中的LQ。所提出的方法可能是准确预测LQ的有用工具，从而改善WCN路由协议的性能。