Paper Info
Title
Optimization Of Latency-Aware Flow Allocation In Ngfi Networks
Abstract
The next generation fronthaul interface (NGFI) architecture has been specified recently in the IEEE P1914.1 standard for packet-based fronthaul transport networks. NGFI is a flexible and cost-effective solution developed for meeting stringent latency and throughput requirements of future centralized and virtualized radio access networks (C-RAN/vRAN) in the 5th generation mobile networks (5G). NGFI allows for splitting baseband processing functions of a radio frequency signal between radio unit (RU), distributed unit (DU), and central unit (CU), where each of these elements may be located at a different site of the network. In the NGFI network, the radio data to be processed is transmitted between the RU, DU, and CU in the form of packets and routed by means of packet switches over a common packet-based fronthaul transport network. In this paper, we address a basic optimization problem in an NGFI network that concerns allocation of transmission resources in network links for the RU-DU and DU-CU data flows assuming quality of service (QoS) requirements related to maximal latency of these flows. To formulate the latency-aware flow allocation (LFA) optimization problem, we apply the mixed integer programming (MIP) approach. To generate solutions to larger LFA problem instances, we develop a meta-heuristic algorithm. To account for latency constraints in both MIP and meta-heuristic, we make use of a worst-case latency estimation model. The results of numerical experiments run in three network topologies for different network instances show the effectiveness of the meta-heuristic method, when compared to the MIP approach. These results also allow us to assess the performance of the NGFI network considered.
Year
DOI
Venue
2020
10.1016/j.comcom.2020.07.044
COMPUTER COMMUNICATIONS
Keywords
DocType
Volume
5G, NGFI, C-RAN, Fronthaul network, Packet-switched network, Time sensitive networking, Network optimization, Mixed integer programming, Metaheuristics
Journal
161
ISSN
Citations 
PageRank 
0140-3664
2
0.38
References 
Authors
0
1
Name
Order
Citations
PageRank
Miroslaw Klinkowski121023.76