Design of reliable wireless backhaul networks is challenging due to the inherent vulnerability of wireless backhauling to random fluctuations of the wireless channel. Considerable studies deal with modifying and designing the network topology to meet the reliability requirements in a cost-efficient manner. However, these studies ignore the correlation among link failures, particularly those caused by weather disturbances. Consequently, the resulting topology designs may fail to meet the network reliability requirements under correlated failure scenarios. To fill this gap, we study the design of cost-efficient and reliable wireless backhaul networks under correlated failures with a focus on rain disturbances. We first propose a new model to consider the pairwise correlation amongf links along a path. The model is verified on real data, indicating an approximation closer to reality than the existing independent failure model. Second, we model the correlation among different paths by defining a penalty cost. Considering the newly formalized link and path correlation, we formulate the correlation-aware network topology design problem as a quadratic integer program to find the optimal solutions. Two lightweight heuristic algorithms are developed to find near-optimal solutions within reasonable time. Performance evaluation shows that correlation-aware design substantially improves the resiliency under rain disturbances at a slightly increased cost compared to independent failure approaches. 

This chapter introduces a techno-economic framework that provides a complete market analysis of the various business actors for any type of mobile access network deployments. It presents a case study where the proposed business feasibility framework is applied. The chapter presents a comprehensive techno-economic framework for estimating the total cost of ownership (TCO) of a backhaul network segment as well as for analyzing the business viability of a given wireless network deployment. It focuses on two backhaul technologies: microwave and fiber. The chapter addresses the framework proposed specifically only the backhaul segment, but it is general enough to also be applied to the other 5G transport solutions. It also presents the TCO module used in the proposed framework. The module covers both the Capital Expenditure and the Operational Expenditure aspects of the backhaul segment. The backhaul network is responsible for aggregating the users' traffic from the wireless access to the metro/backbone segment of the network. 

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.

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.

Inherent vulnerability of wireless backhauling to random fluctuations of the wireless channel complicates the design of reliable backhaul networks. In the presence of such disturbances, network reliability can be improved by providing redundant paths between given source and destination. Many studies deal with modifying and designing the network topology to meet the reliability requirements in a cost- efficient manner. However, these studies ignore the correlation among link failures, such as those caused by rain. Consequently, the resulting topology design solutions may fail to satisfy the network reliability requirements under correlated failure scenarios. To address this issue, this paper studies the design of reliable wireless backhaul networks under correlated failures with focus on rain fading. We consider green-field topology design and brown-field topology upgrade scenarios with the objective to minimize the total cost of wireless links added to meet the target reliability requirement in the presence of correlated link failures. We propose a new model to formulate the spatial correlation using pairwise joint probability distribution of rain attenuation between different links. This model is applied to consider the link- wise correlation along individual paths, as well as the correlation among the multiple redundant paths from the source to the destination node of a traffic flow. We formulate the problem as a quadratic integer program, which is NP-hard, and develop a heuristic algorithm to find near-optimal solutions. Performance evaluation shows that correlation-aware design improves the resiliency under rain disturbance at a slightly increased cost.

Wireless heterogeneous networks (HetNets) are a cost- and an energy-efficient alternative to provide high capacity to end users in the future 5G communication systems. However, the transport segment of a radio access network (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 techonolgies as well as their impact on the cost and profitability of a HetNets deployment (i.e., RAN + transport).

This paper first presents a general and comprehensive techno-economic framework able to assess not only the total cost of ownership (TCO) but also the business viability of a HetNets deployment. It then applies it 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., macro cells only) and a HetNet deployments. 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.  

The exponentially increasing traffic demand for mobile services requires innovative solutions in both access and backhaul segments of 5th generation (5G) mobile networks. Although, heterogeneous networks (HetNets) are a promising solution for the wireless access, the backhaul segment has received considerably less attention and falls short in meeting the stringent requirements of 5G in terms of capacity and availability.

HetNets together with mobility requirements motivate the use of microwave backhauling that supports fiber-like capacity with millimeter-wave communications. However, higher carrier frequencies are subject to weather disturbances like rain that may substantially degrade the network throughput. To mitigate this effect, we develop a fast and accurate rain detection algorithm that triggers a network-layer strategy, i.e., rerouting. The results show that with small detection error the network throughput increases while posing small overhead on the network.

The rain impact can be alleviated by centralized rerouting under the software defined networking paradigm. However, careless reconfiguration may impose inconsistency that leads to a significant temporary congestion and limits the gain of rerouting. We propose a consistency-aware rerouting framework by considering the cost of reconfiguration. At each time, the centralized controller may either take a rerouting or no-rerouting decision in order to minimize the total data loss. We use a predictive control algorithm to provide such an online sequence of decisions. Compared to the regular rerouting, our proposed approach reduces the throughput loss and substantially decreases the number of reconfigurations.

In the thesis we also study which backhaul option is the best from a techno-economic perspective. We develop a comprehensive framework to calculate the total cost of ownership of the backhaul segment and analyze the profitability in terms of cash flow and net present value. The results highlight the importance of selecting proper backhaul solution to increase profitability.

Although, wireless solutions continue to be a dominant enabling technology in the future backhaul  segment, they are susceptible to weather disturbances that may substantially degrade network throughput, or delay, compromising the 5G requirements.  These  effects  can  be  alleviated  by centralized rerouting realized by software defined networking (SDN) architecture. However, careless frequent reconfigurations may lead to inconsistencies in network states due to asynchrony between different switches, which may create  congestion and limit the gain of frequent rerouting.  In  this  paper, we focus on the rerouting process during rain disturbance considering the minimum total congestion imposed  during  the  update  of  routing  tables as a switching cost. At each time sample, the central controller has the possibility to adopt the optimal routes at a switching cost or to keep using previous routes at the expense of a throughput loss due to route sub- optimality. To find optimal solutions with minimal data loss in a static scenario, we formulate a dynamic programming problem that utilizes perfect knowledge of the rain attenuation for the whole rain period (off-line policy with full knowledge). For dynamic scenarios where the future rain attenuation data cannot be known, we propose an online consistency-aware rerouting algorithm, called optimal control action with prediction (OCAP), which uses the temporal correlation of rain fading to estimate the future rain attenuation. Simulation results on synthetic and real networks validate the efficiency of our OCAP algorithm, substantially reducing congestion and increasing network throughput with a fewer number of rerouting actions compared to benchmarks approaches.

Microwave backhaul networks are a cost-efficient option to support increasing capacity demands of mobile networks. However, inherent vulnerability of wireless backhauling to random fluctuations of the wireless channel complicates the design of reliable backhaul links. Long-lasting channel fluctuations such as rain fading may bring significant network performance degradation, and therefore, need to be carefully treated. This paper proposes a novel rain detection algorithm utilizing both temporal and spatial correlation of link status, aiming at efficiently distinguishing between long-term and short-term channel fading. With this distinction, a central controller decides whether network-wide strategies, such as rerouting, are required to mitigate the effects of rain. The accuracy of the proposed detection method is evaluated by measuring false alarm and misdetection probabilities. Numerical results show high rain detection accuracy of the proposed algorithm. Consequently, the impact of imperfect rain detection on the network throughput performance and on the overhead imposed to the central controller becomes negligible.

Microwave backhaul networks are a cost-efficient option to support increasing capacity demands of mobile networks. However, inherent vulnerability of wireless backhauling to random fluctuations of the wireless channel complicates the design of reliable backhaul links. Long-lasting channel fluctuations such as rain fading may bring significant network performance degradation, and therefore, need to be carefully treated. This paper proposes a novel rain detection algorithm utilizing both temporal and spatial correlation of link status, aiming at efficiently distinguishing between long-term and shortterm channel fading. With this distinction, a central controller decides whether network-wide strategies, such as rerouting, are required to mitigate the effects of rain. The accuracy of the proposed detection method is evaluated by measuring false alarm and misdetection probabilities. Numerical results show high rain detection accuracy of the proposed algorithm. Consequently, the impact of imperfect rain detection on the network throughput performance and on the overhead imposed to the central controller becomes negligible.