No abstract

The exponential growth of mobile traffic means that operators must upgrade their mobile networks to provide higher capacity to final users. A promising alternative is to deploy heterogeneous networks (HetNets) that combine macro Base Stations (BSs) and SmallCells (SCs), although this increases the complexity and cost of the transport (SCs to Fiber Access Point–FAP). Most of the planning strategies outlined in the literature are aimed at reducing the number of SCs and ignore the impact that the transport segment might have on the total cost of network deployment. In this paper, heuristics are used for the joint planning of radio (i.e., SCs) and transport resources (i.e., point-to-point fiber links). These were compared and examined to determine the advantages and disadvantages of each approach, and in some cases, this led to a 50% reduction in total costs, while still creating a non-scalable network.

Abstract-Heterogeneous Networks (HetNets) have been introduced as an alternative means of improving the overall network capacity. However, HetNets increase the complexity and cost of transport due to the large number of smallcells (SCs) that have to be connected, and hence, it is essential to investigate the best way to plan the joint deployment of radio and transport resources. For this reason, some planning strategies have been put forward in the literature with the aim of reducing both the number of SCs and amount of transport. These systems are generally based on OFDM (Orthogonal Frequency Division Multiplexing) which uses a radio frequency range from 2 to 20 GHz. However, those papers do not evaluate path loss, which is a major component in the analysis or how to design the link budget of a telecommunication system. In this paper, we examine a heuristic for the joint planning of radio (i.e., SCs) and transport resources (i.e., point-to-point fiber links) by using suitable propagation models for next generation networks. Through the proposed heuristics, it is possible to save up to 12% of the total costs of the network deployment incurred by other systems found in the literature. that investigate how to provide backhaul to transport data to/from a gateway node (a node with an existing fiber point, often co-located with a macrocell) in the core network. Radio Network Planning (RNP) is essential for operators to deploy wireless cellular networks in a cost-effective manner; however, in the optimization process, this must take account of both the radio features, and transport. .In particular, it must include its inevitable migration to the Cloud Radio Access Networks (C-RAN), which provide key solutions for ensuring an efficient allocation and management of baseband processing resources, which are essential for forthcoming ultra-dense deployments.C-RAN is the key architecture of the 5G networks and should be able to support faster data rates (up to 10 Gb/s) and more bandwidth than the preexisting cellular technologies. Another important feature of 5G is the dense Base Station (BS) deployment in HetNets. Studies show that 80% of subscribers are concentrated in 20% of the sites of the network. Thus, it is clear that the use of highcapacity technology as backhaul in traditional architecture and fronthaul in the C-RAN architecture, is

The exponential growth of mobile traffic requires mobile operators to update their network infrastructure to provide greater capacity and better connections for end-users. A promising alternative is to deploy heterogeneous networks (HetNets) that combine macrocells and small cells; however, this alternative increases the complexity and cost of transport (connections between the small cells and the operator’s control center). Most planning strategies outlined in the literature are aimed at reducing the number of small cells without considering important aspects involving transport (access backbone). With the advent of centralized architectures, this point becomes essential, since it is necessary to consider the potential impact of the transport segment on the deployment cost of the network (with the advent of the fronthaul). In this sense, this work proposes an optimal multiobjective model of radio and transport allocation based on linear programming to minimize the total cost of the network and two efficient heuristics to obtain a near-optimal solution. Considering a real case study of the literature, we show the cost (financial and computational) of the optimal placement of radio and transport infrastructure and the limitations of the solution. We also compare the proposed function placement heuristic with the optimal solution in terms of cost efficiency and execution time and demonstrate that it can provide a good estimation of the deployment cost in a much shorter time, with an approximation of up to 10% in relation to the optimal model.