Modelling of Computational Resources for 5G RAN

Abstract

The future mobile networks have to be flexible and dynamic to address the exponentially increasing demand with the scarce available radio resources. Hence, 5G systems are going to be virtualised and implemented over cloud data-centres. While elastic computation resource management is a well-studied concept in IT domain, it is a relatively new topic in Telco-cloud environment. Studying the computational complexity of mobile networks is the first step toward enabling elastic and efficient computational resource management in telco environment. This paper presents a brief overview of the latency requirements of Radio Access Networks (RANs) and virtualisation techniques in addition to experimental results for a full virtual physical layer in a container-based virtual environment. The novelty of this paper is presenting a complexity study of virtual RAN through experimental results, in addition to presenting a model for estimating the processing time of each functional block. The measured processing times show that the computational complexity of PHY layer increases as the Modulation and Coding Scheme (MCS) index increases. The processes in uplink such as decoding take almost twice the time comparing to the related functions in the downlink. The proposed model for computational complexity is the missing link for joint radio resource and computational resource management. Using the presented complexity model, one can estimate the computational requirement for provisioning a virtual RAN as well as designing the elastic computational resource management.

Optimal Computational Resource Allocation and Network Slicing Deployment in 5G Hybrid C-RAN

“…The MCS indices and the number of RBs per slice used to compute the VNF computational demand are given in Table I. The values of the other parameters needed for the computational resource model in (1) can be found in [10].…”

“…3GPP options for function split between RRHs and the central cloud [9]. experiments in [10], RB s is the number of resource blocks (RBs) allocated to the slice chain s, i s,DL and i s,UL are the indices of the modulation and coding schemes (MCSs) of VNF chain s in the downlink (DL) and the uplink (UL) as defined in 3GPP TS 38.214 [11]: the higher the index, the higher the MCS spectral efficiency. Moreover, {α n,DL,k , α n,UL,k } n,k are fitting coefficients.…”

“…Moreover, {α n,DL,k , α n,UL,k } n,k are fitting coefficients. The VNFs related to the PHY layer, especially the encoding/decoding, are the most computational demanding functions [2], [10]. Moreover, from (1), the computational requirement of a VNF increases with the spectral efficiency and the number of RBs required by the slice; therefore, services characterized by high data-rate are more computational demanding than low data-rate services.…”

Optimal Resource Allocation in C-RAN through DSP Computational Load Forecasting

“…The main objective of the operator is to minimize the total cost for the virtualization service, which requires to forecast the computational requirements of all the RRHs in order to activate/deactivate VMs accordingly. The process is subject to two main constraints, both related to the Quality of Service perceived by users of the network: 1) In-time decoding: frame decoding for each RRH must be completed by the BBU within stringent time requirements (the typical HARQ loop lasts a few ms in LTE [11], [17]). Any delay in the process due to under provisioning of the virtual resources may lead to the expiration of the corresponding timeouts and triggers of frame retransmissions at the user side, thus decreasing its QoS.…”

A Flexible Network Architecture for 5G Systems

“…The resulting models may facilitate the overall resource management of the cloud infrastructure. In [38], the experimental modelling of physical layer is presented.…”