Machine learning adaptive computational capacity prediction for dynamic resource management in C-RAN

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 thebaseband unit (BBU) pools may result in underutilized or oversubscribed resources, thus affecting the overallQuality of Service (QoS). As resources are virtualized at the BBU pools, they could be dynamically instan-tiated according to the required computational capacity (RCC). In this paper, a new strategy for DynamicResource 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: supportvector 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 higherthan the predicted computational capacity (PCC). To further improve, two new strategies are proposed andtested 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, respectivelyThis work was supported in part by the Spanish ministry of science through the project CRIN-5G (RTI2018-099880-B-C32) withERDF (European Regional Development Fund) and in part by the UPC through COST CA15104 IRACON EU Project and theFPI-UPC-2018 Grant.Peer ReviewedPostprint (published version

On the feasibility of remote driving application over dense 5G roadside networks

Remote driving (RD) is a critical backup option for an autonomous vehicle when it faces an unexpected situation on the road. Fifth generation (5G) mobile communication is a key enabler of RD providing link between the remote operator and the vehicle. There are stringent requirements in terms of coverage, data rate and latency for RD application. In this paper, a feasibility study at the radio access network level for the radio coverage conditions that are required for RD application is carried out. The study considers RD over three carriers including 2.6 GHz, 5 GHz and 28 GHz. A ray tracing software is used for the channel pathloss computation. The RD provision in terms of coverage and rate statistics is analysed for these frequency carriers. The role of interference is also investigated. The performance statistics are aggregated using a large number of realistic vehicular routes created using Google Directions APIs. It is shown that the provision of RD can not be guaranteed over a wide network area especially in high-load/high-interference conditions and it is more feasible to define the RD application for specific roads.Peer reviewe