This letter proposes a deep learning-based pilot assignment scheme (DL-PAS) for a massive multiple-input multipleoutput (massive MIMO) system that utilizes a large number of antennas for multiple users. The proposed DL-PAS improves the performance in cellular networks with severe pilot contamination by learning the relationship between pilot assignment and the users’ location pattern. In this work, we design a novel supervised learning method where input features and output labels are users’ locations in all cells and pilot assignments, respectively. Specifically, pretrained optimal pilot assignments with given users’ locations are provided through an exhaustive search method as the training data. Then, the proposed DL-PAS provides a nearoptimal pilot assignment from the produced inferred function by analyzing the training data. We implement the proposed scheme using a commercial deep multilayer perceptron system. Simulation-based experiments show that the proposed scheme achieves almost 99.38% theoretical upper-bound performance with low complexity, requiring only 0.92 ms computational time.

This study confirmed the importance of AI education for fostering students’ AI literacy and derived the necessity of constructivist-oriented datasets that provide contextual relevance to students’ lives and real-world problem-solving experiences. By reconstructing the machine learning dataset development cycle through prior research, we developed datasets following each procedural step, then evaluated and refined them through expert panel interviews focusing on dataset quality metrics and characteristics of authentic activities. The datasets were deployed through educational programming platforms commonly used in AI education and designed for sustainable maintenance. To verify effectiveness, we analyzed usage metrics of the developed datasets and conducted comparative analysis of their impact on AI literacy through educational implementations. The research outcomes include development of four AI education datasets demonstrating potential to replace conventional materials like the Iris dataset. Implementation on major Korean AI education platforms confirmed high accessibility and utility, establishing these as crucial educational resources meeting classroom needs. Through application and effectiveness analysis, we verified that AI education datasets developed based on constructivism can: connect students’ prior knowledge with real-world experiences, deepen understanding of AI model learning processes, and provide authentic data-driven computing experiences - collectively contributing to comprehensive AI literacy enhancement.

This paper proposes a deep recurrent neural network (RNN)-based traffic classification scheme (deep RNN-TCS) for classifying applications from traffic patterns in a hybrid edge computing and cloud computing architecture.We can also classify traffic from a cloud server, but there will be a time delay when packets transfer to the server. Therefore, the traffic classification is possible almost in realtime when it performed on edge computing nodes. However, training takes a lot of time and needs a lot of computing resources to learn traffic patterns. Therefore, it is efficient to perform training on cloud server and to perform serving on edge computing node. Here, a cloud server collects and stores output labels corresponding to the application packets. Then, it trains those data and generates inferred functions. An edge computation node receives the inferred functions and executes classification. Compared to deep packet inspection (DPI), which requires the periodic verification of existing signatures and updated application information (e.g., versions adding new features), the proposed scheme can classify the applications in an automated manner. Also, deep learning can automatically make classifiers for traffic classification when there is enough data. Specifically, input features and output labels are defined for classification as traffic packets and target applications, respectively, which are created as two-dimensional images. As our training data, traffic packets measured at Universitat Politecnica de Catalunya Barcelonatech were utilized. Accordingly, the proposed deep RNN-TCS is implemented using a deep long short-term memory system. Through extensive simulation-based experiments, it is verified that the proposed deep RNN-TCS achieves almost 5% improvement in accuracy (96% accuracy) while operating 500 times faster (elapsed time) compared to the conventional scheme.