Abstract
Semi-supervised learning self-training (SSST) is a promising approach to solving classification problems with unlabeled data. Our paper proposes a multi-objective cache content strategy that aims at maximizing small base stations’ cache hit rates in mobile edge networks (MENs). The cache placement algorithm is formulated as a classification problem. The logistic regression (LR) learning technique is used to create a classifier based on a limited amount of labeled data. The classifier then uses a self-training technique to classify unlabeled samples. To expand the number of samples in the dataset, the cases with the highest prediction probability are added to the training set. The proposed technique was developed and tested on samples with a variety of input characteristics, including file popularity, user location, user to SBS contact probability, communication range, contact duration, and others. Experiments on real user request sequences reveal that the proposed method increases prediction accuracy considerably. It also saves a lot of time when it comes to identifying the unlabeled data, making it an efficient solution to the cache location problem.
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This work is funded in part by a research grant from the National Science and Engineering Research Council of Canada.
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Mohammed, L.B., Anpalagan, A., Khwaja, A.S., Jaseemuddin, M. (2022). Semi-supervised Learning with Self-training Classifier for Cache Placement in Mobile Edge Networks. In: Nguyen, H., Le, L., Yahampath, P., Mohamed, E.B. (eds) 30th Biennial Symposium on Communications 2021. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-06947-5_15
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