Centralized radio access network (C-RAN) has been considered as an architectural solution able to reduce capital and operational expenditure in dense 5G cellular networks, while allowing better network performance. The C-RAN approach decouples baseband units from antenna sites and places them in selected locations, connected by the so called fronthaul links. These links require expensive high capacity connections, thus calling for cost-efficient deployment. This paper presents a hybrid fronthaul solution for C-RAN based on both optical fibers and free space optics (FSO) to enhance fronthaul flexibility and minimize deployment costs. Two design strategies based on integer linear programming are proposed for both greenfield and brownfield deployments. The first strategy is referred to as joint planning (JP) and is based on the joint minimization of the number of deployed remote radio heads (RRHs) and cost of the hybrid fiber/FSO fronthaul. The second strategy is based on a two-step disjoint planning (DP) which first identifies a cost-optimal RRH placement and then finds the corresponding minimum cost deployment for the fronthaul links. Results obtained with JP and DP are compared in dense urban area scenarios (i.e., with characteristics similar to festivals or concerts) highlighting the advantage of the JP approach with respect to DP, both in terms of costs and an enhanced flexibility during the network design process.

The traffic in mobile access networks is increasing at an exponential rate, with the majority of this traffic being generated indoor. To cope with this trend, heterogeneous network (HetNet) architectures based on the centralized radio architecture (CRA) concept have been recently proposed. A CRA network is able to reach high wireless network performance by centralizing the radio physical layer functions of macro and small cells. On the other hand, a CRA network puts strict latency and capacity requirements on the transport segment, which usually comprises a mixture of fiber-and copper-based infrastructure. These strict constraints may translate into high deployment costs if not carefully addressed. This paper proposes an optimized deployment strategy for CRA networks in residential areas. The objective of the proposed strategy is to contain the total deployment cost by minimizing the number of wireless and transport resources required. We demonstrate that our deployment strategy allows for a significant reduction of the required amount of network components and the overall network cost compared to the existing deployment solutions.

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The global connected cars market is growing rapidly. Novel services will be offered to vehicles, many of them requiring low-latency and high-reliability networking solutions. The Cloud Radio Access Network (C-RAN) paradigm, thanks to the centralization and virtualization of baseband functions, offers numerous advantages in terms of costs and mobile radio performance. C-RAN can be deployed in conjunction with a Multi-access Edge Computing (MEC) infrastructure, bringing services close to vehicles supporting time-critical applications. However, a massive deployment of computational resources at the edge may be costly, especially when reliability requirements demand deployment of redundant resources. In this context, cost optimization based on integer linear programming may result in being too complex when the number of involved nodes is more than a few tens. This paper proposes a scalable approach for C-RAN and MEC computational resource deployment with protection against single-edge node failure. A two-step hybrid model is proposed to alleviate the computational complexity of the integer programming model when edge computing resources are located in physical nodes. Results show the effectiveness of the proposed hybrid strategy in finding optimal or near-optimal solutions with different network sizes and with affordable computational effort.

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