Accurate identification of the signal type in shared-spectrum networks is critical for efficient resource allocation and fair coexistence. It can be used for scheduling transmission opportunities to avoid collisions and improve system throughput, especially when the environment changes rapidly. In this paper, we develop deep neural networks (DNNs) to detect coexisting signal types based on In-phase/Quadrature (I/Q) samples without decoding them. By using segments of the samples of the received signal as input, a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) are combined and trained using categorical cross-entropy (CE) optimization. Classification results for coexisting Wi-Fi, LTE LAA, and 5G NR-U signals in the 5-6 GHz unlicensed band show high accuracy of the proposed design. We then exploit spectrum analysis of the I/Q sequences to further improve the classification accuracy. By applying Short-time Fourier Transform (STFT), additional information in the frequency domain can be presented as a spectrogram. Accordingly, we enlarge the input size of the DNN. To verify the effectiveness of the proposed detection framework, we conduct over-the-air (OTA) experiments using USRP radios. The proposed approach can achieve accurate classification in both simulations and hardware experiments.

Mobile edge computing (MEC) is a key enabler of delay-sensitive vehicle-to-everything (V2X) applications. Determining where to execute a task necessitates accurate estimation of the offloading latency. In this paper, we propose a latency prediction framework that integrates machine learning and statistical approaches. Aided by extensive latency measurements collected during driving, we first preprocess the data and divide it into two components: one that follows a trackable trend over time and the other that behaves like random noise. We then develop a Long Short-Term Memory (LSTM) network to predict the first component. This LSTM network captures the trend in latency over time. We further enhance the prediction accuracy of this technique by employing a k-medoids classification method. For the second component, we propose a statistical approach using a combination of Epanechnikov Kernel and moving average functions. Experimental results show that the proposed prediction approach reduces the prediction error to half of a standard deviation (STD) of the raw data.