Optimum Functional Splits for Optimizing Energy Consumption in V-RAN

Abstract

A virtualized radio access network (V-RAN) is considered one of the key research points in the development of 5G and the interception of machine learning algorithms in the Telecom industry. Recent technological advancements in Network Function Virtualization (NFV) and Software Defined Radio (SDR) are the main blocks towards V-RAN that have enabled the virtualization of dual-site processing instead of all BBU processing as in the traditional RAN. As a result, several types of research discussed the trade-off between power and bandwidth consumption in V-RAN. Processing at remote locations instead of BBU reduces mid-haul bandwidth at the expense of power consumption and vice versa. As a result, the integration of NFV and SDR in V-RAN facilitates dynamic power consumption and processing whenever relaxation is needed. This paper studies several functional splits proposed by ETSI in the NFV of the dualsite network. In addition, network performance is analyzed in terms of data rate, power consumption, and energy efficiency (EE) optimization. Furthermore, the combined optimization of power consumption and mid-haul bandwidth are investigated, and optimal operating parameters are recommended for similar network operators. Thus, regulators/operators can adjust their networks with these parameters to achieve the best performance. Additionally, the UEs switching scheme is introduced to sleep some RRHs in low-density traffic to lessen power consumption.

Multiservice Loss Models for Cloud Radio Access Networks

“…Regarding the offered traffic-load, we initially assume that α m,k = 1 erl for all calls in all RRHs (point 1 in the x-axis of Figs. [4][5][6][7][8][9][10]. Table 2 summarizes the traffic parameters used for the simulation and analytical CBP results of this example.…”

“…During the last years, various significant aspects of the 5G C-RAN architecture have been extensively studied, including: a) functional splits and capacity demands of the fronthaul network [7]- [9], b) privacy and security challenges [10], [11] and c) energy and cost saving issues [12]- [15]. On the other hand, only a few papers exist in the literature that study call admission control (CAC) of MUs in a 5G C-RAN and propose efficient formulas, via Markov-based loss/queueing models, for the call blocking probabilities (CBP) computation [16]- [21].…”

“…Procedure A7 represents (9). It accepts as parameters the service-class k calls serviced in the m-th RRH and the amount j of radio RUs that are occupied by those calls.…”

6G: A comprehensive survey on technologies, applications, challenges, and research problems

“…With the fast movement of the virtualization in telecommunication applications, many works introduced a virtualization network named virtualized‐radio access network (V‐RAN) or Fog‐RAN (F‐RAN) on the cloud‐radio access networks (C‐RAN) architecture 22 . The C‐RAN is a centralized processing, collaborative radio, and a real‐time cloud‐based RAN.…”

Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

“…Various important C-RAN architectural aspects have been studied during the last years, including (a) capacity demands and possible functional splits of the fronthaul network [9][10][11], (b) security and/or privacy challenges [12,13], (c) cost and/or energysaving issues [14][15][16][17], and (d) network slicing issues [18][19][20]. At the same time, very few papers exist in the literature (to the best of our knowledge) that investigate call admission control (CAC) and propose efficient formulas for the call blocking probabilities (CBP) computation [21][22][23][24][25][26][27][28].…”