A Deep Learning Model for Wireless Channel Quality Prediction

Accurately modeling and predicting wireless channel quality variations is essential for a number of networking applications such as scheduling and improved video streaming over 4G LTE networks and bit rate adaptation for improved performance in WiFi networks. In this paper, we propose an encoder-decoder based sequence-to-sequence deep learning model that is capable of predicting future wireless signal strength variations based on past signal strength data. We consider two different versions of the deep learning model; the first and second versions use LSTM and GRU as their basic cell structure, respectively. In contrast to prior work that is primarily focused on designing models for particular network settings, the deep learning model is highly adaptable and can predict future channel conditions for different networks, sampling rates, mobility patterns, and communication standards. We compare the performance (i.e., the root mean squared error of future predictions) of our model with respect to two baselines-i) auto-regression(1), and ii) linear regression for multiple networks and communication standards. In particular, we consider 4G LTE, WiFi, an industrial network operating in the 5.8 GHz range, Zigbee, and WiMAX networks operating under varying levels of user mobility and observe that the deep learning model provides significantly superior performance. Finally, we provide detailed discussion on key design decisions including insights into hyper-parameter tuning of the model.