Random forests for resource allocation in 5G cloud radio access networks based on position information

Abstract

Next generation 5G cellular networks are envisioned to accommodate an unprecedented massive amount of Internet of things (IoT) and user devices while providing high aggregate multi-user sum rates and low latencies. To this end, cloud radio access networks (CRAN), which operate at short radio frames and coordinate dense sets of spatially distributed radio heads, have been proposed. However, coordination of spatially and temporally denser resources for larger sets of user population implies considerable resource allocation complexity and significant system signalling overhead when associated with channel state information (CSI)-based resource allocation (RA) schemes. In this paper, we propose a novel solution that utilizes random forests as supervised machine learning approach to determine the resource allocation in multi-antenna CRAN systems based primarily on the position information of user terminals. Our simulation studies show that the proposed learning based RA scheme performs comparably to a CSI-based scheme in terms of spectral efficiency and is a promising approach to master the complexity in future cellular networks. When taking the system overhead into account, the proposed learning-based RA scheme, which utilizes position information, outperforms legacy CSI-based scheme by up to 100%. The most important factor influencing the performance of the proposed learning-based RA scheme is antenna orientation randomness and position inaccuracies. While the proposed random forests scheme is robust against position inaccuracies and changes in the propagation scenario, we complement our scheme with three approaches that restore most of the original performance when facing random antenna orientations of the user terminal.

A Developed Multi-Level Deep Learning Model for Network Slicing Classification in 5G Network

“…RF is a technique for supervised learning. The primary RF working processes are as follows: picking random samples from a dataset, for each sample a DT were built; and receiving a prediction result from each DT, holding a vote for each predicted result, and as a final prediction the prediction result with the most votes were chosen [18]. A DT is another example of a supervised learning approach.…”

Transition technologies towards 6G networks

“…A key field for developing the future virtual networking architectures of 6G systems is softwarization solutions that are not based on traditional pure hardware solutions. 6G systems will feature a full range of software-based functionalities powered by agile SDN, NFV, and fog computing capabilities [25][26][27][28][29][30][31][32]. In addition, network softwarization embraces zero-touch service management, automation, security, and trust networking features, to name a few.…”

“…In addition, network softwarization embraces zero-touch service management, automation, security, and trust networking features, to name a few. The cloud RAN [26], where baseband functions are moved deeper into the network in more centralized locations as compared to legacy architecture, has support for multi-tenancy network sharing and slicing. The combination of all these network features should provide ideal flexibility and re-configuration of the system in a way that any sort of service can be fully satisfied.…”

Coordinates-based Resource Allocation Through Supervised Machine Learning

“…This position estimate can be determined, for example, by Kalman filtering of the direction-of-arrival and time-of-arrival of the specifically sent positioning beacons in the uplink [9]. These positioning beacons are in fact narrow-band signals, which pose significantly lesser overhead compared to CSI estimation beacons for high user density scenarios, as mentioned in [15]. Let p p p(t) denote the true position coordinates of the terminal at time t, while p p p(t) denote the estimate of position coordinates of the terminal.…”