Wireless heterogeneous networks (HetNets) are a cost- and energy-efficient alternative to provide high capacity to end users in the future 5G communication systems. However, the transport segment of a RAN poses a big challenge in terms of cost and energy consumption. In fact, if not planned properly, its resulting high cost might limit the benefits of using small cells and impact the revenues of mobile network operators. Therefore, it is essential to be able to properly assess the economic viability of different transport technologies as well as their impact on the cost and profitability of a HetNet deployment (i.e., RAN plus transport). This article first presents a general and comprehensive techno-economic framework able to assess not only the TCO but also the business viability of a HetNet deployment. The framework is then applied to the specific case study of a backhaul-based transport segment. In the evaluation work two technology options for the transport network are considered (i.e., microwave and fiber) assuming both a homogeneous (i.e., macrocells only) and a HetNet deployment. Our results demonstrate the importance of selecting the right technology and deployment strategy in order not to impact the economic benefits of a HetNet deployment. Moreover, the results also reveal that a deployment solution with the lowest TCO does not always lead to the highest profit.

Filterless optical networks based on broadcast-and-select nodes and coherent transceivers are attractive cost-effective and flexible solutions in core networks. In this paper, we explore the suitability of filterless architectures in metropolitan core and aggregation networks.

Filterless optical networks based on broadcast-and-select nodes and coherent transceivers are attractive cost-effective and flexible solutions in core networks. In this paper, we explore the suitability of filterless architectures in metropolitan core and aggregation networks.

Wireless network solutions, a dominant enabling technology for the backhaul segment, are susceptible to weather disturbances that can substantially degrade network throughput and/or delay, compromising the stringent 5G requirements. These effects can be alleviated by centralized rerouting realized by software defined networking architecture. However, careless frequent reconfigurations can lead to inconsistencies in the network states due to asynchrony between different switches, which can create congestion and limit the rerouting gain. The aim of this paper is to minimize the total data loss during rain disturbance by proposing an algorithm that decides on the timing, the sequence, and the paths for rerouting of network flows considering the imposed congestion during reconfiguration. At each time sample, the central controller decides whether to adopt the optimal routes at a switching cost, defined as the imposed congestion, or to keep using existing, sub-optimal routes at a throughput loss. To find optimal solutions with minimal data loss in a static scenario, we formulate a dynamic programming problem that utilizes perfect knowledge of rain attenuation for the whole rain period. For dynamic scenarios with unknown future rain attenuation, we propose an online consistency-aware rerouting algorithm, called consistency-aware rerouting with prediction (CARP), which uses the temporal correlation of rain fading to estimate future rain attenuation. Simulation results on synthetic and real networks validate the efficiency of our CARP algorithm, substantially reducing data loss and increasing network throughput with a fewer number of rerouting actions compared to a greedy and a regular rerouting benchmarking approaches.

We propose a novel approach that exploits a delay-constrained framework to meet stringent delay requirements and achieves energy-savings in Intelligent Transportation Systems (ITS). Analytical results show 40% energy-savings in a network that delivers delay-sensitive safety information over ITS.

We present a proof-of-concept demonstration of an SDN/NFV-based orchestrator for sharing infrastructure resources among different tenants. The designed orchestrator maximizes the profit of an infrastructure provider by using a dynamic slicing approach based on big data analytics.

In this paper, we propose a new restoration algorithm for disaster recovery in Elastic Optical Networks (EONs). The algorithm considers the different requirements on restoration delay and bandwidth degradation while restoring over multiple routes. Furthermore, the algorithm is able to adapt the connection recover process to the post-disaster network conditions and, consequently, increase the number of restored connections. Results indicate that the proposed strategy is able to improve connections restorability and decrease bandwidth blocking probability when compared to restoration approach based on bandwidth degradation and multipath recovery.

We present the flexible RAN concept and evaluate its performance in different radio coordination scenarios considering an optical transport network. Results show the benefits of flexible RAN compared to C-RAN in terms of wavelength usage and transponder cost.

Space Division Multiplexing (SDM) is a promising solution to provide ultra-high capacity optical network infrastructure for rapidly increasing traffic demands. Such network infrastructure can be a target of deliberate attacks that aim at disrupting a large number of vital services. This paper assesses the effects of high-power jamming attacks in SDM optical networks utilizing Multi-Core Fibers (MCFs), where the disruptive effect of the inserted jamming signals may spread among multiple cores due to increased Inter-Core CrossTalk (ICo-XT). We first assess the jamming-induced reduction of the signal reach for different bit rates and modulation formats. The obtained reach limitations are then used to derive the maximal traffic disruption at the network level. Results indicate that connections provisioned satisfying the normal operating conditions are highly vulnerable to these attacks, potentially leading to huge data losses at the network level.