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Edge computing has been recently introduced as a way to bring computational capabilities closer to end users of modern network-based services, in order to support existent and future delay-sensitive applications by effectively addressing the high propagation delay issue that affects cloud computing. However, the problem of efficiently and fairly manage the system resources presents particular challenges due to the limited capacity of both edge nodes and wireless access networks, as well as the heterogeneity of resources and services' requirements. To this end, we propose a techno-economic market where service providers act as buyers, securing both radio and computing resources for the execution of their associated end users' jobs, while being constrained by a budget limit. We design an allocation mechanism that employs convex programming in order to find the unique market equilibrium point that maximizes fairness, while making sure that all buyers receive their preferred resource bundle. Additionally, we derive theoretical properties that confirm how the market equilibrium approach strikes a balance between fairness and efficiency. We also propose alternative allocation mechanisms and give a comparison with the market-based mechanism. Finally, we conduct simulations in order to numerically analyze and compare the performance of the mechanisms and confirm the theoretical properties of the market model., Comment: Corrected typos and figure orientation. in IEEE Transactions on Mobile Computing

With the capability to support gigabit data rates, millimetre-wave (mm-Wave) communication is unanimously considered a key technology of future cellular networks. However, the harsh propagation at such high frequencies makes these networks quite susceptible to failures due to obstacle blockages. Recently introduced Reconfigurable Intelligent Surfaces (RISs) can enhance the coverage of mm-Wave communications by improving the received signal power and offering an alternative radio path when the direct link is interrupted. While several works have addressed this possibility from a communication standpoint, none of these has yet investigated the impact of RISs on large-scale mm-Wave networks. Aiming to fill this literature gap, we propose a new mathematical formulation of the coverage planning problem that includes RISs. Using well-established planning methods, we have developed a new optimization model where RISs can be installed alongside base stations to assist the communications, creating what we have defined as Smart Radio Connections. Our simulation campaigns show that RISs effectively increase both throughput and coverage of access networks, while further numerical results highlight additional benefits that the simplified scenarios analyzed by previous works could not reveal.

Millimeter wave (mmWave) communications have been introduced in the 5G standardization process due to their attractive potential to provide a huge capacity extension to traditional sub-6 GHz technologies. However, such high-frequency communications are characterized by harsh propagation conditions, thus requiring base stations to be densely deployed. Integrated access and backhaul (IAB) network architecture proposed by 3GPP is gaining momentum as the most promising and cost-effective solution to this need of network densification. IAB networks’ available resources need to be carefully tuned in a complex setting, including directional transmissions, device heterogeneity, and intermittent links with different levels of availability that quickly change over time. It is hard for traditional optimization techniques to provide alone the best performance in these conditions. We believe that Deep Reinforcement Learning (DRL) techniques, especially assisted with Long Short-Term Memory (LSTM), can implicitly capture the regularities of environment dynamics and learn the best resource allocation strategy in networks affected by obstacle blockages. In this article, we propose a DRL based framework based on the Column Generation (CG) that shows remarkable effectiveness in addressing routing and link scheduling in mmWawe 5G IAB networks in realistic scenarios.

5G standardization has envisioned mmWave communications as a promising direction to expand the capacity of current mobile radio networks. However, communications at high frequency are characterized by extremely harsh propagation conditions, thus requiring a high base station deployment density. To solve this issue, from both technical and economic perspective, 3GPP has proposed mmWave access networks based on an Integrated Access and Backhaul (IAB) multi-hop architecture.IAB networks require fine-tuning of the available resources in a complex setting, due to directional transmissions, device heterogeneity, and harsh propagation conditions. The latter, in particular, characterize the operations of such networks, resulting in links with very different levels of availability. For this reason, traditional optimization techniques do not provide the best performance in these conditions. We believe, instead, Reinforcement Learning (RL) techniques can implicitly consider the dynamics of the network links and learn the best resource allocation strategy in networks with intermittent links. In this paper, we propose an RL-based resource allocation approach that shows the advantages of these techniques in dynamic environmental conditions.

One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate Traffic Engineering modules able to optimize network configuration according to traffic. Ideally, the network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc. In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in multiple domains and not only in traffic values. Moreover, to provide more flexibility to the decisions on when to apply a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing even in case of smooth transitions. We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration. Moreover, we present an experimental platform where our solution is implemented in a production-ready SDN controller.

This paper proposes a novel semi-distributed and practical ICIC scheme based on the Almost Blank SubFrame (ABSF) approach specified by 3GPP. We define two mathematical programming problems for the cases of guaranteed and best-effort traffic, and use game theory to study the properties of the derived ICIC distributed schemes, which are compared in detail against unaffordable centralized schemes. Based on the analysis of the proposed models, we define Distributed Multi-traffic Scheduling (DMS), a unified distributed framework for adaptive interference-aware scheduling of base stations in future cellular networks which accounts for both guaranteed and best-effort traffic. DMS follows a two-tier approach, consisting of local ABSF schedulers, which perform the resource distribution between guaranteed and best effort traffic, and a lightweight local supervisor, which coordinates ABSF local decisions. As a result of such a two-tier design, DMS requires very light signaling to drive the local schedulers to globally efficient operating points. As shown by means of numerical results, DMS allows to (i) maximize radio resources reuse, (ii) provide requested quality for guaranteed traffic, (iii) minimize the time dedicated to guaranteed traffic to leave room for best-effort traffic, and (iv) maximize resource utilization efficiency for best-effort traffic., 16 pages, submitted to IEEE/ACM Transactions on Networking

The exploitation of mm-wave bands is one of the key-enabler for 5G mobile radio networks. However, the introduction of mm-wave technologies in cellular networks is not straightforward due to harsh propagation conditions that limit the mm-wave access availability. Mm-wave technologies require high-gain antenna systems to compensate for high path loss and limited power. As a consequence, directional transmissions must be used for cell discovery and synchronization processes: this can lead to a non-negligible access delay caused by the exploration of the cell area with multiple transmissions along different directions. The integration of mm-wave technologies and conventional wireless access networks with the objective of speeding up the cell search process requires new 5G network architectural solutions. Such architectures introduce a functional split between C-plane and U-plane, thereby guaranteeing the availability of a reliable signaling channel through conventional wireless technologies that provides the opportunity to collect useful context information from the network edge. In this article, we leverage the context information related to user positions to improve the directional cell discovery process. We investigate fundamental trade-offs of this process and the effects of the context information accuracy on the overall system performance. We also cope with obstacle obstructions in the cell area and propose an approach based on a geo-located context database where information gathered over time is stored to guide future searches. Analytic models and numerical results are provided to validate proposed strategies., Comment: 14 pages, submitted to IEEE Transaction on Mobile Computing

Mm-wave technologies are a promising solution to provide ultra-high capacity in 5G wireless access networks. However, the potential of several-GHz bandwidths must coexist with a harsh propagation environment. While high attenuations can be compensated by advanced antenna systems, the severe obstacle blockage effect can only be mitigated by more sophisticated network management.One of the most widely adopted techniques to guarantee a reliable service in mm-wave access scenarios is to establish multiple connections from mobile to different base stations. However, the advantage of multiple mm-wave connections can be fully exploited only if uncorrelated channels are available, thus spatial diversity must be ensured. Although smart base-station selections could be made once the network is deployed, much better results are achievable if diversity-aware selection aspects are already included in the network planning phase.In this paper, we propose for the first time an mm-wave access network planning framework which considers both spatial diversity among potential base-station selection candidates and user achievable throughput, according to channel conditions and network congestion. The results show that our approach allows to obtain much better spatial diversity conditions than traditional k-coverage approaches, and this can indeed provide higher robustness in presence of sudden obstacles.

One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate traffic engineering modules able to optimize network configuration according to traffic. Ideally, network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc. In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in optimal routing and not only in traffic values. Moreover, to provide more flexibility to the online decisions on when applying a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing regardless of the transition instant. We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration., 10 pages, 8 figures, submitted to IFIP Networking 2018

Nowadays, the maintenance costs of wireless devices represent one of the main limitations to the deployment of wireless mesh networks (WMNs) as a means to provide Internet access in urban and rural areas. A promising solution to this issue is to let the WMN operator lease its available bandwidth to a subset of customers, forming a wireless mesh community network, in order to increase network coverage and the number of residential users it can serve. In this paper, we propose and analyze an innovative marketplace to allocate the available bandwidth of a WMN operator to those customers who are willing to pay the higher price for the requested bandwidth, which in turn can be subleased to other residential users. We formulate the allocation mechanism as a combinatorial truthful auction considering the key features of wireless multihop networks and further present a greedy algorithm that finds efficient and fair allocations even for large-scale, real scenarios while maintaining the truthfulness property. Numerical results show that the greedy algorithm represents an efficient, fair, and practical alternative to the combinatorial auction mechanism.

This article introduces the ideas investigated in the BCG2 project of the GreenTouch consortium. The basic concept is to separate signaling and data in the wireless access network. Transmitting the signaling information separately maintains coverage even when the whole data network is adapted to the current load situation. Such network-wide adaptation can power down base stations when no data transmission is needed and, thus, promises a tremendous increase in energy efficiency. We highlight the advantages of the separation approach and discuss technical challenges opening new research directions. Moreover, we propose two analytical models to assess the potential energy efficiency improvement of the BCG2 approach.

The exponential growth of mobile traffic is forcing operators to quickly increase the capacity of their networks by means of new technologies and advanced architectures. This capacity expansion not only brings increasing fixed costs for additional network infrastructures, but also inflates operational costs, which are becoming critical, mainly in terms of energy bills. In this perspective, monitoring the energy consumption of network devices and defining their energy profile models are valuable approaches for estimating energy costs and identifying the most efficient configurations. In this article, we propose an energy profiling approach that simplifies the characterization of different base station components and allows the estimation of the network energy efficiency relying only on traffic statistics. We have validated the approach over the extensive dataset of real measurements provided by a probe network for monitoring live energy consumption of Vodafone sites in three different countries.

Cognitive radio networks (CRNs) are composed of frequency-agile radio devices that allow licensed (primary) and unlicensed (secondary) users to coexist, where secondary users opportunistically access channels without interfering with the operation of primary ones. From the perspective of secondary users, spectrum availability is a time-varying network resource over which multihop end-to-end connections must be maintained. In this paper, a theoretical outlook on the problem of routing secondary user flows in a CRN is provided. The investigation aims to characterize optimal sequences of routes over which a secondary flow is maintained. The optimality is defined according to a novel metric that considers the maintenance cost of a route as channels, and/or links must be switched due to the primary user activity. Different from the traditional notion of route stability, the proposed approach considers subsequent path selections, as well. The problem is formulated as an integer programming optimization model. Properties of the problem are also formally introduced and leveraged to design a heuristic algorithm when information on primary user activity is not complete. Numerical results are presented to assess the optimality gap of the heuristic routing algorithm in realistic CRN scenarios.

This paper provides definite results about the access delay of slotted Aloha with exponential back-off, an infinite number of stages, and queues in saturation. The analysis is performed leveraging a method recently introduced, which indicates a maximum throughput equal to 0.370 with binary back-off, and 0.4303 when the exponential back-off base is optimized, and using a delay offset equal to 2. Here we provide the exact cumulative distribution of the access delay, showing the heavy-tail behavior already conjectured by using approximate models. We also prove that the existence of k-th order delay moments depends on explicit conditions on the number of stations involved and other system parameters as well.

The stability and throughput of the Slotted Aloha protocol have been studied at length, yielding results that depend on the environment and channel assumptions, in many cases indicating e−1 as the S-Aloha capacity. When users can detect only their own collisions, and the number of users N goes to infinity, no definite capacity result exists. Approximated models have been introduced to study the exponential back-off mechanism, which seem to indicate an asymptotic capacity of ln(2)/2 when binary back-off is used, and again e−1 when the exponential base is optimized. Here we introduce a more accurate and flexible model that shows that past results miss their mark. In fact, we prove that with binary back-off the capacity is practically 0.370, slightly greater than e−1; furthermore, and more important, we prove that using 1.35 as exponential back-off base, the capacity reaches 0.4303 with an infinite number of users, and up to 0.496 with N = 2 users.

The introduction of millimeter-wave (mm-wave) technologies in the future 5G networks poses a rich set of network access challenges. We need new ways of dealing with legacy network functionalities to fully unleash their great potential, among them the cell discovery procedure is one of the most critical. In this paper, we propose novel cell discovery algorithms enhanced by the context information available through a C-/U-plane-split heterogeneous network architecture. They rely on a geo-located context database to overcome the severe effects of obstacle blockages. Moreover, we investigate the coordination problem of multiple mm-wave base stations that jointly process user access requests. We show that optimizing the resource allocated to the discovery has a great importance in defining perceived latency and supported user request rate. We have performed complete and accurate numerical simulations to provide a clear overview of the main challenging aspects. The results show that the proposed solutions have an outstanding performance with respect to basic discovery approaches and can fully enable mm-wave cell discovery in 5G networks.

The exponential growth of mobile traffic is forcing operators to increase quickly the capacity of their network and extend it with new technologies and improved topologies, such as heterogeneous layouts with small cells. However, since revenues cannot grow at the same rate of traffic, the main challenge is to manage capacity expansion with reduced costs. In addition to fixed costs for the new network infrastructures, operational costs are becoming critical, mainly for energy bill component. Moreover, the carbon footprint of mobile access networks is considered one of the largest of the whole ICT (Information and Communications Technology) sector and its reduction is fundamental for the environmental sustainability of the Internet economy. Due to these reasons, improving the energy efficiency of the access network is crucial for mobile operators. In order to do that, monitoring the energy consumption of the network components and defining models of energy profile are valuable approaches for estimating energy costs and identifying the most efficient configurations. In this paper, we present an energy consumption monitoring system that has been designed and implemented in three different countries using separate sensors for the radio and base-band components of second, third and fourth generation systems. We also propose an energy profiling approach that simplifies the characterization of the different components and allows the estimation of the energy efficiency based on traffic statistics.

Inter-Cell Interference Coordination (ICIC) has been identified for LTE as the main instrument for interference control. With ICIC, quality requirements can be guaranteed while avoiding the complexity of coordinated baseband processing approaches. However, most ICIC schemes proposed so far rely on centralized multi-cell scheduling algorithms that involve very heavy signaling overhead and, as a result, cannot be used for dense cellular layouts. In this paper, we propose H 2 (IC) 2 , a novel ICIC scheme that, in contrast to previous approaches, incurs very low overhead and is practical for dense deployments. H 2 (IC) 2 is based on the Almost Blank SubFrame (ABSF) approach specified by 3GPP, which controls interference by avoiding data transmission in some subframes. Our scheme follows a two-tier approach, consisting of (i) the local schedulers, which perform the scheduling decisions locally and compute ABSF patterns, and (ii) a central coordinator, which supervises ABSF decisions. As a result of such a two-tier design, the scheme requires very light signaling to drive the local schedulers to globally efficient operating points. We analyze the convergence of distributed ABSF/scheduling decisions by using game theoretical tools and show that H 2 (IC) 2 performs fairly close to the benchmark provided by a centralized omniscient scheduler.

This paper provides an analysis of the Dynamic frame Aloha (DFA) protocol with frame Restart. Although a previous work has numerically provided the best restarting strategy when the number of tags N is known, that strategy is represented by a table whose entries are so many to be impractical for high values of N. Here we provide a simpler set of equations that, besides the optimal strategy, lead to a very simple sub-optimal strategy whose performance is practically indistinguishable from the optimal one. Furthermore, we prove that asymptotically the Frame Restart property is useless, being the corresponding efficiency equal to e -1 , the same as the plain DFA. We investigate the strategies and performance when N is unknown, furthermore providing a procedure, called AE 2 , that is able to asymptotically reach the theoretical efficiency e -1 .

Base stations have been identified to be the most power consuming part in current mobile networks. Since their power profile only depends to a small fraction on the actual traffic load, putting some base stations in sleep mode has been identified as a solution to scale the network power consumption with the actual load. This paper presents a new system architecture based on the paradigm of “cell on-demand”, which is currently studied in the Beyond Cellular Green Generation (BCG2) project of the GreenTouch consortium. We first outline the key characteristics of the new system architecture, then, we present a framework for position information processing in order to predict the channel quality between a user and a set of switched-off base stations that can potentially be activated by the system to serve the user. Experimental evaluation on a small scale testbed shows the viability of the proposed framework by achieving estimation errors of 20%.

In recent years, with the evolution of new and content-rich Internet services, mobile network operators face the challenging task to guarantee ubiquitous access to their customers, while minimizing network deployment costs. In order to foster the opportunistic utilization of unexploited Internet connections of residential users, we propose a new marketplace where mobile network operators can rent the unused capacity of residential users' access devices (e.g., wireless access points or femtocells) when the traffic demand of their mobile customers exceeds the operator's network capacity. We formulate the allocation problem as a combinatorial reverse auction, which prevents market manipulation, and we further propose a greedy algorithm that finds efficient allocations in polynomial time, even for large-size network scenarios. Numerical results demonstrate that our proposed schemes well capture the economical and networking essence of the allocation problem, thus representing a promising approach to enhance the performance of next-generation wireless access networks.

We consider surveillance applications through wireless sensor networks (WSNs) where the areas to be monitored are fully accessible and the WSN topology can be planned a priori to maximize application efficiency. We propose an optimization framework for selecting the positions of wireless sensors to detect mobile targets traversing a given area. By leveraging the concept of path exposure as a measure of detection quality, we propose two problem versions: the minimization of the sensors installation cost while guaranteeing a minimum exposure, and the maximization of the exposure of the least-exposed path subject to a budget on the sensors installation cost. We present compact mixed-integer linear programming formulations for these problems that can be solved to optimality for reasonable-sized network instances. Moreover, we develop Tabu Search heuristics that are able to provide near-optimal solutions of the same instances in short computing time and also tackle large size instances. The basic versions are extended to account for constraints on the wireless connectivity as well as heterogeneous devices and nonuniform sensing. Finally, we analyze an enhanced exposure definition based on mobile target detection probability.

Data collected by sensors often have to be remotely delivered through multi-hop wireless paths to data sinks connected to application servers for information processing. The position of these sinks has a huge impact on the quality of the specific Wireless Sensor Network (WSN). Indeed, it may create artificial traffic bottlenecks which affect the energy efficiency and the WSN lifetime. This paper considers a heterogeneous network scenario where wireless sensors deliver data to intermediate gateways geared with a diverse wireless technology and interconnected together and to the sink. An optimization framework based on Integer Linear Programming (ILP) is developed to locate wireless gateways minimizing the overall installation cost and the energy consumption in the WSN, while accounting for multi-hop coverage between sensors and gateways, and connectivity among wireless gateways. A traffic-variable scenario is also considered, where the network can go through high and low traffic operation points, and the topology is optimized accordingly. The proposed ILP formulations are solved to optimality for medium-size instances to analyze the quality of the designed networks, and heuristic algorithms are also proposed to tackle large-scale heterogeneous scenarios.

Cognitive Radio Networks (CRNs) are composed of frequency-agile radio devices that allow licensed (primary) and unlicensed (secondary) users to coexist, where secondary users opportunistically access channels without interfering with the operation of primary ones. From the perspective of secondary users, spectrum availability is a time varying network resource over which multi-hop end-to-end connections must be maintained. In this work, a theoretical outlook on the problem of routing secondary user flows in a CRN is provided. The investigation aims to characterize optimal sequences of routes over which a secondary flow is maintained. The optimality is defined according to a novel metric that considers the maintenance cost of a route as channels and/or links must be switched due to the primary user activity. Different from the traditional notion of route stability, the proposed approach considers subsequent path selections, as well. The problem is formulated as an integer programming optimization model and shown to be of polynomial time complexity in case of full knowledge of primary user activity. Properties of the problem are also formally introduced and leveraged to design a heuristic algorithm to solve the minimum maintenance cost routing problem when information on primary user activity is not complete. Numerical results are presented to assess the optimality gap of the heuristic routing algorithm.

Data collected by sensors often have to be remotely delivered through multi-hop wireless paths to data sinks connected to application servers for information processing. The position of these sinks has a huge impact on the quality of the specific Wireless Sensor Network (WSN). Indeed, it may create artificial traffic bottlenecks which affect the energy efficiency and the WSN lifetime. This paper considers a heterogeneous network scenario where wireless sensors deliver data to intermediate gateways geared with a diverse wireless technology and interconnected together and to the sink. An optimization framework based on Integer Linear Programming (ILP) is developed to locate wireless gateways minimizing the overall installation cost and the energy consumption in the WSN, while accounting for multi-hop coverage between sensors and gateways, and connectivity among wireless gateways. The proposed ILP formulations are solved to optimality for medium-size instances to analyze the quality of the designed networks, and heuristic algorithms are also proposed to tackle large-scale heterogeneous scenarios.

In this paper novel optimization models are proposed for planning Wireless Mesh Networks (WMNs), where the objective is to minimize the network installation cost while providing full coverage to wireless mesh clients. Our mixed integer linear programming models allow to select the number and positions of mesh routers and access points, while accurately taking into account traffic routing, interference, rate adaptation, and channel assignment. We provide the optimal solutions of three problem formulations for a set of realistic-size instances (with up to 60 mesh devices) and discuss the effect of different parameters on the characteristics of the planned networks. Moreover, we propose and evaluate a relaxation-based heuristic for large-sized network instances which jointly solves the topology/coverage planning and channel assignment problems. Finally, the quality of the planned networks is evaluated under different traffic conditions through detailed system level simulations.

Trajectory based forwarding (TBF) is a new approach for routing in ad hoc wireless networks. It assumes that nodes know their position and, similarly to source routing, requires the source node to encode a trajectory into the packet header. However, trajectories are just geometrical lines and the routing process does not require specifying forwarding nodes. As a matter of fact, forwarding nodes are dynamically selected while packets cross the network according to their position with respect to the trajectory. Therefore, this new approach is particularly suitable for application scenarios where network topology is fast varying, due to node mobility (e.g. inter-vehicular networks) or to energy management schemes (e.g. sensor networks), whereas the stability of the trajectories is guaranteed by the physical characteristics of the service area (roads, building aisles etc.). In this paper we propose a new TBF scheme that exploits broadcast transmissions at MAC layer and does not require maintaining a list of active neighbours positions in every node. We consider piecewise lines connecting source node to destination area and we extend the approach to the multicast case defining trajectory-trees.