Wireless Channel Scenario Identification Using Convolutional Neural Networks
Govind R. Gopal, Jie Chen, William J. Hillery, Junkai Tan, Serdar Özen, Qiping Zhu · 2023
Wireless channel scenario identification carries importance as it assists in network design aspects such as receiver modeling and channel prediction. In this work, we consider the identification of indoor and outdoor scenarios using convolutional neural networks (CNNs) with channel measurements as the input data. First, we consider the channel measurements obtained as independent realizations (the snapshot dataset) and utilize a 2-D CNN to classify between indoor and outdoor channels. Then, the channel measurements taken for successive time samples (the time series dataset) are classified using 3-D CNNs. Finally, comparison of the CNN-based approaches is performed with a traditional machine learning-based k-nearest neighbors (k-NN) algorithm, which takes skewness, kurtosis, and delay spread of the channel data as the input features. The CNN-based method for the snapshot data yields around 87% accuracy and performs better than the baseline k-NN method yielding around 76%. For the time series dataset, the CNN-based method with up to a 85% accuracy shows a marked improvement over the 60% accuracy achieved by the k-NN algorithm.