Abstract
Efficient computational resource management in 5G Cloud Radio Access Network (C-RAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. The assumption of a fixed computational capacity at the baseband unit (BBU) pools may result in underutilized or oversubscribed resources, thus affecting the overall Quality of Service (QoS). As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). In this paper, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). To further improve, two new strategies are proposed and tested in a realistic scenario: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-AC-ES), reducing the average of unsatisfied resources by 98 % and 99.9 % compared to the DRM-AC, respectively.
Highlights
The rise of mobile data subscriptions and emerging technologies as the Internet of Things (IoT) and autonomous vehicles are about to generate a massive growth in data traffic
2) After a literature review on traffic prediction and testing some of the most used techniques on a realistic scenario, we propose and intensively compare support vector machine (SVM), timedelay neural network (TDNN), and long short-term memory (LSTM) machine learning strategies; concluding that LSTM is more suitable because its performance does not depend on the number of previous time-steps used in the prediction
The maximum error of SVM and time-delay neural network (TDNN) approaches are around 35 Giga operations per second (GOPS), and the root-mean-square error (RMSE) is close to 7.5 GOPS, which represents a deviation of 4.5 % of the mean value of the overall dataset
Summary
The rise of mobile data subscriptions and emerging technologies as the Internet of Things (IoT) and autonomous vehicles are about to generate a massive growth in data traffic. Multipoint (CoMP) and beamforming could be improved by the coordination among BBUs [2], [3] For this reasons, the 3rd Generation Partnership Project (3GPP) includes the C-RAN architecture in the standardization of the 5G RAN, as well as different split options to reduce the enormous bandwidth and latency requirements of the fronthaul [4]. OF THE USED ML TECHNIQUES After consulting the literature on traffic forecasting and testing the most common techniques, the best results were obtained using SVM, TDNN, and Deep Learning with LSTM For this reason, these ML techniques are considered in the proposal. The original idea focuses on element classification This strategy was extended to address regression tasks in [16].
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