2015/03/11 by Vinay Suryaprakash, Peter Rost, Gerhard Fettweis · 88 citations
Computer Science · Engineering · #Access network #Advanced MIMO Systems Optimization #Advanced Wireless Network Optimization #Backhaul (telecommunications) #Base station #Capital expenditure #Cellular network #Cloud computing #Computer network #Computer science #Distributed computing #ICT Impact and Policies #Mobile station #Operating expense #Radio access network #Software deployment #cs.NI #msc:94A05
paper · pdf · doi:10.1109/jsac.2015.2435275
published in IEEE Journal on Selected Areas in Communications 33(10), 2239-2251 (Institute of Electrical and Electronics Engineers) · accepted for publication, 2015 IEEE Journal on Selected Areas in Communication
arxiv created 2015/03/11 · openalex publication_date 2015/05/20 · arxiv updated 2016/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Mobile networks of the future are predicted to be much denser than today's networks to cater to increasing user demands. In this context, cloud-based radio access networks have garnered significant interest as a cost-effective solution to the problem of coping with denser networks and providing higher data rates. However, to the best of the authors' knowledge, a quantitative analysis of the cost of such networks is yet to be undertaken. This paper develops a theoretic framework that enables computation of the deployment cost of a network (modeled using various spatial point processes) to answer the question posed by the paper's title. Then, the framework obtained is used along with a complexity model, which enables computing the information processing costs of a network, to compare the deployment cost of a cloud-based network against that of a traditional LTE network, and to analyze why they are more economical. Using this framework and an exemplary budget, this paper shows that cloud-based radio access networks require approximately 10% to 15% less capital expenditure per square kilometer than traditional LTE networks. It also demonstrates that the cost savings depend largely on the costs of base stations and the mix of backhaul technologies used to connect base stations with data centers.