By predicting changes in service requirements and the evolution of network states, anticipatory networking aims to improve the performance of decision making and optimization algorithms. In this paper, we propose a reliability-aware scheduling model which exploits information about the upcoming channel states of users to proactively optimize resource allocation. Along with the optimal formulation, proven to be NP-hard, a heuristic is also presented. The evaluation of the proposed optimization model and its corresponding heuristic is carried out considering both perfect and imperfect prediction of the users' channel states. Numerical results show that both models are robust against errors and that the decrease in reliability, when the length of the prediction window is equal to 25 ms, is less than 10%.

Maintaining service guarantees in a dynamic multi- tenant network, while ensuring an economically sustainable sharing platform, is a non-trivial problem. This paper, extending our previous work, develops a dynamic slicing and trading framework that can satisfy a variety of service guarantees. This framework not only determines the size of the network resource slices required for various active services, but it also adapts resource prices in accordance with the microeconomic laws of supply and demand. The proposed framework also ensures service continuity by learning the variations in the traffic mix as well as in the channel conditions, and by adjusting the slice assignments accordingly.

Flexible resource sharing at short time scales in multi-tenant shared radio access networks has proven to be quite a challenge. In this study, we develop a techno-economic model that enables dynamic short-term resource sharing as well as resource pricing, while simultaneously collecting revenue for network expansion. In order to regulate the resource costs and to prevent monopolization of resources, we define a unit cost of resources which can be scaled dynamically. The proposed framework allows operators to meet their individual utility targets while optimizing their expenditures based on their respective budgets. This work demonstrates that dynamic short timescale resource sharing can help network operators achieve their utility targets while minimizing their total expenditure.

The rapid growth in the number and variety of connected devices requires 5G wireless systems to cope with a very heterogeneous traffic mix. As a consequence, the use of a fixed transmission time interval (TTI) during transmission is not necessarily the most efficacious method when heterogeneous traffic types need to be simultaneously serviced. This work analyzes the benefits of scheduling based on exploiting scalable TTI, where the channel assignment and the TTI duration are adapted to the deadlines and requirements of different services. We formulate an optimization problem by taking individual service requirements into consideration. We then prove that the optimization problem is NP-hard and provide a heuristic algorithm, which provides an effective solution to the problem. Numerical results show that our proposed algorithm is capable of finding near-optimal solutions to meet the latency requirements of mission critical communication services, while providing a good throughput performance for mobile broadband services.

Active sharing of wireless infrastructure can be an effective approach to reduce costs and improve network profitability. However, schemes proposed so far neither guarantee network operators the autonomy to compete and differentiate themselves in various market segments, nor give infrastructure providers the economic resources to keep the network updated in terms of technology and capacity. In this work, we propose a techno-economic model that allows network operators to compete and dynamically select the quality target to deliver to their customers, while simultaneously seeking to maximize their profits. In order to understand the willingness of network operators to participate in such a scenario, we develop a non-cooperative game wherein the Nash Equilibria show the propensity of operators to meet the customers' requirements. This work also points out the importance of retaining independent regulatory bodies, within the new business ecosystem, charged with proposing pricing policies capable of incentivizing investments towards infrastructure upgrades.

For the first time since the advent of mobile networks, the idea of advancing their pervasiveness by co-opting them into most aspects of daily life has taken hold and this idea is, henceforth, intended to be a mainstay of future networks (5G and beyond). As a result, a term one frequently encounters in the latest literature pertinent to radio access networks is reliability. It is, however, fairly evident that it is mostly used in a colloquial linguistic sense or that, in some cases, it is used synonymously with availability. This work is, to the best of our knowledge, the first to provide a quantitative definition of reliability which stems from its characterization in the dictionary and is based on quantifiable definitions of resilience, availability, and other parameters important to radio access networks. The utility of this quantitative definition is demonstrated by developing a reliability-aware scheduler which takes predictions of the channel quality into account. The scheduler developed here is also compared with the classical proportional fair scheduler in use today. This comparison not only succeeds in highlighting the practicality of the definition provided, but it also shows that the anticipatory reliability-aware scheduler is able to provide an improvement of about 35 - 50% in reliability when compared to a proportional fair scheduler which is common in contemporary use.

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.

Future mobile networks are visualized as networks that consist of more than one type of base station to cope with rising user demands. Such networks are referred to as heterogeneous networks. There have been various attempts at modeling and optimization of such networks using spatial point processes, some of which are alluded to (later) in this paper. We model a heterogeneous network consisting of two types of base stations by using a particular Poisson cluster process model. The main contributions are two-fold. First, a complete description of the interference in heterogeneous networks is derived in the form of its Laplace functional. Second, using an asymptotic convergence result which was shown in our previous work, we derive the expressions for the mean and variance of the distribution to which the interference converges. The utility of this framework is discussed for both the contributions.

A flexible and effectual back-haul infrastructure is required for managing extremely dense future mobile networks proficiently, and economically. This paper aims to establish a framework which can estimate the cost of a network, and then use the framework to minimize the deployment cost of the network by optimizing the number of back-haul nodes required to help the base stations satisfy user demands. The two main contributions of this work are: the derivation of a framework that gives the cost of deploying a back-haul node in a 3-layer model which consists of users, base stations, and back-haul nodes; and the generalization of the framework to obtain the cost of deploying a node in a particular layer of interest in a k-layer model. The utility of this framework is illustrated for the 3-layer model by showing that there exist a range of back-haul node intensity values for which deployment costs can be minimized while satisfying users' rate requirements.

Future mobile networks are forecast as being increasingly heterogeneous and dense. An aspect crucial to managing such networks is the existence of a flexible and effective backhaul infrastructure. Since backhaul infrastructure is essential, it becomes important to analyze the cost of its implementation. This paper aims to realize a framework to estimate deployment costs in a network which consists of users, two types of base stations, and backhaul nodes that could either be microwave or fiber optic backhaul nodes. The main contribution of this work is the derivation of a framework using spatial point processes that helps estimate the cost of deploying a backhaul node based on the number of users and base stations connected to it. The framework, along with assumptions of typical costs of various network components, is utilized to examine whether there exist an optimal number of backhaul nodes that can minimize the overall deployment cost of a network while catering to a given number of users in the area.

This paper aims to discuss the influence of adjustable base station (BS) power parameters, such as power consumed during active mode and sleep modes, on the overall energy consumption of a network and highlight potential energy savings that can be achieved by the introduction of sleep modes. A BS density that can satisfy the demands of a given user density - defined by a daily traffic profile - can be found using the relationship between spatially averaged rate, user density, and BS density established here. The underlying framework for this relationship assumes users and BSs to be independently marked point processes in ℝ2. A power model that is an affine function of the BS density is used to determine the overall energy consumption of the network at full load, and these values are compared to those that incorporate sleep modes to utilize the minimum number of BSs that satiate the demands of a user density that varies during the course of the day. The relationship established between spatially averaged rate, user density, and BS density forms the main result of this paper, based on which it can be inferred that the introduction of sleep modes results in substantial energy savings when the load is seldom full.

This paper tries to analyze the energy saving capabilities of two common power saving techniques being suggested - introduction of sleep modes and bandwidth (BW) variation. The framework for this analysis assumes users and base stations (BSs) to be independently marked point processes in R2. The relationship between spatially averaged rate, user density, and base station density, which is an extension of findings in [1], is used in an affine power model to estimate the energy that can be saved by the two methods under consideration. The primary contribution of this paper constitutes an analytic relationship between spatially averaged rate, user density, BS density, transmit power, and the noise power. Another key contribution is a proof that shows that power saved by using sleep modes (or turning off BSs) is always greater than the power saved by varying the BW, for a system model implementing an affine power model (described here) when traffic densities below full load are considered.