The telecommunications industry is undergoing a major transformation driven by new technologies alongside economic challenges that are reshaping the entire ecosystem. The increasing digitalization of industries and sectors demands more advanced network infrastructures tailored to diverse market verticals and supporting businesses in their digital transformation. However, the ecosystem’s future remains uncertain, influenced by operators’ strategic choices, business model innovation, national and industrial policies, regulations, and cross-industry investments. This paper extends previous work by not only outlining possible evolutionary scenarios of the telecommunications ecosystem but also analyzing the reciprocal relationship between technological advances and these scenarios. Specifically, it investigates how emerging market, ecosystem, and economic conditions influence the evolution and application of technology, while technology itself drives changes in these domains. Focusing on the European ecosystem, with implications for global contexts, this integrated perspective aims to support market players and policymakers in navigating transformation and making informed decisions. This work responds to the Grand Challenge “Creating a vision of the future evolution of telecommunications” within the RESTART research program, the most important public R&D initiative ever launched in the Italian telecommunications sector, funded by the European Union / Italian Ministry of University and Research, with an investment of 116 million euros. Consistently with the elements outlined above, it focuses on the trajectory of the telecommunications ecosystem, providing a comprehensive and forward looking assessment of the key business, technology and regulatory drivers at both national and European level.

The increasing demand for mobile bandwidth is driving 5G networks toward the use of high-frequency spectrum, particularly the upper-6 GHz and mmWave bands. While these bands offer vast bandwidth potential, their propagation characteristics raise critical deployment challenges. This paper presents the first direct, on-field comparative evaluation of 5G standalone (SA) macro-cell deployments operating in these two bands, conducted in Milan, Italy. We show that the upper-6 GHz band can deliver wide-area urban coverage (up to 600 meters) with stable gigabit-level downlink throughput, even in (NLoS) scenarios. mmWave, traditionally deemed unsuitable for NLoS, exhibits strong performance via urban reflections, achieving up to 1.3 Gbps in downlink and 250 Mbps in uplink. Furthermore, outdoor-to-indoor connectivity at mmWave frequencies proves viable through glass facades, challenging pessimistic assumptions about penetration losses. These findings, derived from synchronized deployments and extensive measurements, provide new insights into the complementary roles of these bands and the practical feasibility of their integration into future 5G networks.

Network virtualization, software-defined infrastructure, and orchestration are pivotal elements in contemporary networks, yielding new vectors for optimization and novel capabilities. In line with these principles, O-RAN presents an avenue to bypass vendor lock-in, circumvent vertical configurations, enable network programmability, and facilitate integrated artificial intelligence (AI) support. Moreover, modern container orchestration frameworks (e.g., Kubernetes, Red Hat OpenShift) simplify the way cellular base stations, as well as the newly introduced RAN Intelligent Controllers (RICs), are deployed, managed, and orchestrated. While this enables cost reduction via infrastructure sharing, it also makes it more challenging to meet O-RAN control latency requirements, especially during peak resource utilization. For instance, the Near-real-time RIC is in charge of executing applications (xApps) that must take control decisions within one second, and we show that container platforms available today fail in guaranteeing such timing constraints. To address this problem, we propose ScalO-RAN, a control framework rooted in optimization and designed as an O-RAN rApp that allocates and scales AI-based O-RAN applications (xApps, rApps, dApps) to: (i) abide by application-specific latency requirements, and (ii) monetize the shared infrastructure while reducing energy consumption. We prototype ScalO-RAN on an OpenShift cluster with base stations, RIC, and a set of AI-based xApps deployed as micro-services. We evaluate ScalO-RAN both numerically and experimentally. Our results show that ScalO-RAN can optimally allocate and distribute O-RAN applications within available computing nodes to accommodate even stringent latency requirements. More importantly, we show that scaling O-RAN applications is primarily a time-constrained problem rather than a resource-constrained one, where scaling policies must account for stringent inference time of AI applications, and not only how many resources they consume.

The advancement of Connected and Autonomous Vehicle (CAV) technology promises to revolutionize transportation systems, but robust and effective communication among CAVs is needed to ensure safety and efficiency. Vehicle-to-everything (V2X) communication, particularly vehicle-to-vehicle (V2V) communication, offers direct vehicular data exchange without burdening network infrastructure. However, the dynamic nature of vehicular scenarios and the strict application requirements pose critical challenges in the radio resource allocation domain. To address these challenges, this paper proposes an Open RAN (O-RAN)-based solution, leveraging O-RAN’s flexibility and programmability. The proposed solution employs standardized interfaces to collect and analyze traffic data, enabling centralized cross-base station resource allocation. Implemented as an O-RAN xApp, the solution demonstrates superior performance in large-scale vehicular simulations compared to existing radio allocation schemes, showcasing effectiveness in managing diverse traffic profiles and minimizing allocation collisions with negligible overhead. Evaluation against Mode 2 demonstrates the solution’s efficacy with respect to the standard. Overall, the study highlights for the first time O-RAN’s potential in managing radio resources for V2V communication.

This paper deals with the challenge of modeling the performance of planned ultrabroadband access networks while maintaining technological neutrality and accuracy in measurable quality. We highlight the importance of such modeling also for addressing public funding policies compared to models mainly based on the maximum nominal speed of the access networks, taking also into account the widespread use of measurement tools like "speed test" that have influenced the perceived quality by end-users. We present a performance modelling approach based on the extension of well-known traffic models that accurately characterizes the performance of broadband access networks. We also show how the presented model has been validated with data from two network operators and has been applied to address the recent Italian public interventions for the development of ultrabroadband access networks in market failure areas.

Millimeter wave (mmWave) communications have the potential to dramatically increase the throughput of 5G-and-beyond wireless networks. However, the challenging propagation conditions typical of higher frequencies require expensive base station densification to guarantee reliable Radio Access Networks (RANs). Integrated Access and Backhaul (IAB), a solution where wireless access and backhaul use the same waveform, spectrum, and protocol stack, has been proposed and standardized as a highly effective means of decreasing these costs. While IAB is considered a key enabler for high-frequency RANs, experimental research in this context is hampered by the lack of accessible testing platforms. In this demonstration, we showcase IABEST, a large-scale end-to-end IAB testbed based on open-source software and compatible with off-the-shelf hardware. We show how to deploy IABEST capabilities at scale on Colosseum, a publicly available massive channel emulator. Finally, we show how IABEST can support researchers in data collection and algorithm testing from the highest levels of network abstraction down to scheduling decisions.

Network Function Virtualization has dramatically increased the flexibility in the deployment of network services, however the execution of virtual functions on compute nodes equipped with general purpose hardware can result in worse performance compared to the middleboxes they aim to replace. The use of programmable network hardware to perform part of the processing at line rate can drastically increase the throughput while retaining the flexibility.This work presents a new framework, called CHIMA, which extends the capabilities of other frameworks proposed in the literature for the deployment of heterogeneous Service Function Chains (SFCs). Heterogeneous SFCs comprise a combination of virtual functions meant to be executed in containers running on general purpose hardware and of functions for programmable switches written using the P4 language. CHIMA exploits programmable data planes to perform real time monitoring of the services through In-band Network Telemetry and uses the collected information to guarantee the requested levels of performance by redeploying and rerouting sections that are affected by adverse conditions, allowing applications with critical requirements to be deployed as SFCs.The solution has been tested by emulating various topologies and services on the FOP4 platform with bmv2 switches. The analysis shows that the system is capable of detecting faults in the order of hundreds of milliseconds, and the overhead it causes in the process of redeployment is negligible compared to the startup time of functions. Measurements also reveal that the current bottleneck for the runtime relocation of heterogeneous functions is the redeployment and reconfiguration of P4 programs.

The introduction of the mm-Wave spectrum into 5G NR promises to bring about unprecedented data throughput to future mobile wireless networks but comes with several challenges. Network densification has been proposed as a viable solution to increase RAN resilience, and the newly introduced Integrated-Access-and-Backhaul (IAB) is considered a key enabling technology with compelling cost-reducing opportunities for such dense deployments. Reconfigurable Intelligent Surfaces (RIS) have recently gained extreme popularity as they can create Smart Radio Environments by EM wave manipulation and behave as inexpensive passive relays. However, it is not yet clear what role this technology can play in a large RAN deployment. With the scope of filling this gap, we study the blockage resilience of realistic mm-Wave RAN deployments that use IAB and RIS. The RAN layouts have been optimised by means of a novel mm-Wave planning tool based on MILP formulation. Numerical results show how adding RISs to IAB deployments can provide high blockage resistance levels while significantly reducing the overall network planning cost.

Mm-Wave 5G NR promises to provide unprecedented access throughput to mobile radio networks but comes with several challenges. Network densification is the only viable solution to increase robustness in front of link outages due to random obstacles. While Integrated-Access-and-Backhauling (IA B) architecture is commonly considered the enabling technology to reduce the cost of such dense deployments, Reconfigurable Intelligent Surfaces (RISs) and Smart Repeaters (SRs) are very recently emerging as promising tools to provide further capabilities to 6G networks in Smart Radio Environments. However, the impact of these devices on next-generation networks is yet to be fully understood. In this paper, we provide a first answer to the questions arising in the deployment of mmWave RAN equipped with SRs and RISs. By means of mathematical programming models for full network planning optimization, we produce optimal network layouts that we leverage to assess in which scenario, in which position, and with which configuration SRs and RISs can better improve the reliability of such networks.

Network slicingmight radically change the relations among different actors of the telecommunications ecosystem, where new players, active in different markets, could benefit of tailored connectivity services based on different business strategies. We argue that for fully exploiting the opportunities offered by network slicing, dynamic sharing of resources is crucial not only for efficiency and cost savings, but also for enabling a resource negotiation that can unleash the potential of new business relations. We develop an automated mechanism that allows tenants to take strategic decisions to optimize the management of their slices based on their instantaneous demands and model their interaction as in marketplace. We integrate our solution, based on game theory, on a 3GPP calibrated system level simulator, where a slice-aware scheduler enforces the tenants’ decisions at the Nash Equilibrium (NE). We compare our proposal with a static baseline, that assigns a fixed share of resources to each slice, and show that, by dynamically trading resources in the market, tenants achieve lower costs, and, therefore, higher profits. We provide an algorithmic implementation that guarantees the convergence to a single NE and test the computational complexity of our algorithm to an increasing number of slices in the system.

Virtualization and network slicing offer an unprecedented opportunity to mobile network operators: open their physical network infrastructure platform to the concurrent deployment of multiple logical self-contained networks, namelynetwork slices. In this paper, we propose and analyzeONETS, an Online NETwork Slicing solution that$\textbf {i}$) builds on the budgeted lock-up multi-armed bandit mathematical model and properties,$\textbf {ii}$) derives its analytical bounds in our proposed extension for network slicing,$\textbf {iii}$) seamlessly integrates into the 3GPP architecture,$\textbf {iv}$) proves its feasibility through a proof-of-concept implementation on commercial hardware considering three network slices and$\textbf {v}$) allows for the design of a low-complexity online network slice brokering solution that maximizes multiplexing gains.

With the unprecedented rise in traffic demand and mobile subscribers, real-time fine-grained optimization frame-works are crucial for the future of cellular networks. Indeed, rigid and inflexible infrastructures are incapable of adapting to the massive amounts of data forecast for 5G networks. Network softwarization, i.e., the approach of controlling “everything” via software, endows the network with unprecedented flexibility, al-lowing it to run optimization and machine learning-based frame-works for flexible adaptation to current network conditions and traffic demand. This work presents QCell, a Deep Q-Network-based optimization framework for softwarized cellular networks. QCell dynamically allocates slicing and scheduling resources to the network base stations adapting to varying interference con-ditions and traffic patterns. QCell is prototyped on Colosseum, the world's largest network emulator, and tested in a variety of network conditions and scenarios. Our experimental results show that using QCell significantly improves user's throughput (up to 37.6%) and the size of transmission queues (up to 11.9%), decreasing service latency.

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.

Abstract Ideally, the network should be dynamically reconfigured as traffic evolves. Yet, even within the software defined network paradigm, network reconfigurations cannot be too frequent due to a number of reasons related to route consistency, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, and so on. In this paper, we focus on the fundamental issue of deciding whether, when, and how to reconfigure the network while traffic evolves. We consider a problem of optimizing semi‐stable routing in the capacitated multicommodity flow network when one may use at most a given maximum number of routing configurations (called routing clusters) and when each routing configuration must be used for at least a given minimum amount of time. We propose an efficient solution approach based on routing cluster generation that provides a tight lower bound on the minimum of a selected objective function (like maximum link delay or a sum of link delays) and suboptimal solutions very close to the calculated bound. The approach scales well with the size of the network.

Data-center topologies interconnect an ever larger number of servers using a high number of alternative paths to provide high bandwidth and a high degree of resiliency. The state-of-the-art routing strategy is based on Equal-cost multipath (ECMP) which employs static hashing mechanism over packet header fields to spread the traffic over multiple paths. Routing the traffic without considering the size of the flows and the utilization of the paths might cause congestion due to the collision of multiple large flows on a same downstream path. We present CEDRO, an in-switch mechanism to detect and reschedule colliding large flows. By exploiting the latest advances in SDN programmable network devices, we offload to the network the detection of both the elephant flows and the path congestion conditions and the rescheduling mechanism. CEDRO is able to promptly cope with path congestion and failures directly from the dataplane, regardless of the availability of the external controller. We implemented CEDRO in an emulated SDN network and tested it against realistic traffic scenarios. Numerical evaluation shows CEDRO is able to improve the average and 95-th percentile of the Flow Completion Time compared to ECMP.

The ability of TCP's congestion control scheme to adapt the rate of traffic flows and fairly use all the available resources is one of the Internet's pillars. So far, however, the elasticity of traffic has been disregarded in traffic engineering (TE) methodologies mainly because, only recently, the increase in access capacity has moved the bottlenecks from the access network to the operator network and hungry cloud-based applications have begun to use all the available bandwidth. We propose a new approach to TE with elastic demands which models the interaction between the network operator and the end-to-end congestion control scheme as a Stackelberg game. Given a set of elastic traffic demands only specified by their origin-destination pairs, the network operator chooses a set of routing paths (leader's problem) which, when coupled with the fair bandwidth allocation that the congestion control scheme would determine for the chosen routing (follower's problem), maximizes a network utility function. We present bilevel programming formulations for the above TE problem with two widely-adopted bandwidth allocation models, namely, max-min fairness and proportional fairness, and derive corresponding exact and approximate single-level mathematical programming reformulations. After discussing some key properties, we report on computational results obtained for different network topologies and instance sizes. Interestingly, even feasible solutions to our bilevel TE problems with large optimality gaps yield substantially higher network utility values than those obtained by solving a standard single-level TE problem and then fairly reallocating the bandwidth a posteriori.

5G introduces a broad range of new applications with diverse and demanding performance targets that require substantial investments on the radio access infrastructure. The network slicing model allows to share resources and to distribute responsibilities for network capacity expansion to infrastructure providers and for identifying resource needs to slice tenants specialized in different application domains. In this paper, we focus on the problem of taking capacity expansion decisions considering the traffic needs and performance requirements of network slices instantiated on the access infrastructure. We build on a techno-economic model that allows to describe the trade of resources between infrastructure provider and tenants. The model is based on a resource market that generates revenues for matching infrastructure recurrent costs, but also extra revenues for capacity expansion and technology upgrade. Being the pricing scheme based on resource scarcity in different geographical regions, we propose to guide capacity expansion decisions through an automatic algorithm based on utility functions and available budget generated by the market. Results show the effectiveness of the approach proposed.

By abstracting network resources and functions, network slicing allows to partition a single physical infrastructure into multiple logical independent networks, called slices, that can be tailored to the diverse needs of a wide range of application domains. In order to be independently managed by different players (tenants), slices need to be logically isolated from each other. As some of the applications envisioned for 5G networks are quite critical from performance point of view, a static slicing of resources, where isolation is not only logical but also physical, may appear a natural solution. However, the price to pay in terms of resource overprovisioning can be high, and therefore dynamic allocation strategies, based on optimized resource sharing, are alternative solutions. In this work, we propose an optimization framework to compare the two resource allocation schemes and define a metric to quantify the slicing gain. We show that the dynamic slicing outperforms the static approach and that the gain is significant even for stringent performance requirements, making dynamic slicing a viable solution also for critical applications.

Dynamically sharing network resources in a sliced multi-tenant network can provide cost efficient solutions that are able to guarantee specific service requirements for 5G networks and beyond. By automatizing the negotiations between tenants and infrastructure providers over the shared resources, it is possible to maximize the flexibility of the network in a very short time frame, thus increase efficiency. However, negotiating resources in a reactive manner can bring risks to the tenants due to traffic variations, and can also limit the gain in terms of spectral efficiency for the infrastructure provider. In this paper, we focus on how to exploit anticipatory strategies relying on predicted information on users' conditions in order to improve the efficiency of the proposed dynamic network slicing and trading framework. In particular, we analyze how to integrate a prediction algorithm into our scheme and analyze the techno-economic impacts of the anticipatory approach. Finally, we introduce a novel filtering algorithm to limit the impacts of prediction errors. Our results prove that using anticipatory strategies in dynamic negotiations and resource allocation increases tenants' utilities, while allows the infrastructure provider to accommodate more requests.

Ideally, the network should be dynamically reconfigured as traffic evolves. Unfortunately, even in SDN paradigm, network 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 consider a problem of optimizing semi-stable routing in the capacitated multicommodity flow network when one may use at most a given maximum number of routing configurations (called clusters) and when each routing configuration must be used for at least a given minimum amount of time. We propose a solution method based on cluster generation that provides a good lower bound on the minimum network delay (i.e., the total of link delays) and scales well with the size of the network.

One of the disruptive innovations introduced by 5G networks is the opportunity for a new group of stakeholders to be actively involved in the management of network slices with the role of tenants. This allows to go beyond the user-centric QoS paradigm of 4G, and to include tools for handling the aggregate performance of multiple services and user groups and to focus on slice resource management, also at the new 5G NR interface. So far, research efforts have privileged a first solution based on the concept of isolation between slices. However, proposed solutions are not particularly efficient due to the loss of pooling gains, and not very reliable due to variable channel conditions that with slice limited resources make performance not easily predictable. We propose a slice management framework where the shared resources are negotiated by tenants in a real-time market based on slice instantaneous demands. Our model, based on game theory, allows tenants to optimize their service strategies acquiring resources when and where it is necessary, according to the level of quality and reliability requested by the specific traffic types they handle. In this paper, we focus on modeling the game theoretical framework and on characterizing its equilibria in a multi-tenant scenario.

Expanding the market of mobile network services and defining solutions that are cost efficient are the key challenges for next generation mobile networks. Network slicing is commonly considered to be the main instrument to exploit the flexibility of the new radio interface and core network functions. It targets splitting resources among services with different requirements and tailoring system parameters according to their needs. Regulation authorities also recognize network slicing as a way of opening the market to new players who can specialize in providing new mobile services acting as “tenants” of the slices. Resources can also be distributed between infrastructure providers and tenants so that they meet the requirements of the services offered. In this paper, we propose a model for dynamic trading of mobile network resources in a market that enables automatic optimization of technical parameters and of economic prices according to high level policies defined by the tenants. We introduce a mathematical formulation for the problems of resource allocation and price definition and show how the proposed approach can cope with quite diverse service scenarios presenting a large set of numerical results.

Network slicing is one of the main novelty of 5G systems and it is expected to radically change the approach of network operators to the support of vertical applications with specific and stringent performance requirements that can be managed by tenants. Static allocation of resources to slices is a straightforward approach to meet Service Level Agreements (SLAs), but it may lead to dramatic inefficiencies and high costs. On the other side, dynamic sharing can greatly improve resource utilization, but it requires a strict control of performance parameters so as to guarantee small congestion probability and smoothed degradation when it occurs. In this paper, we propose a utility-based approach that can properly map the slice customization strategies that tenants can use to address the different traffic requirements and their specific service management policies. We define resource scheduling mechanisms that allow differentiation among slices and service prioritization through slice-specific parameters. We model resource allocation as a mathematical programming problem and present numerical results that show the viability of the proposed approach.

The explosive growth of smartphones and other portable devices, along with new traffic types generated by M2M applications, are creating huge volumes of mobile data traffic and signaling overhead, therefore requiring a radical change to the current mobile network architecture. This has promoted new virtualization paradigms, which combine diverse packet core services, and provide network functions implemented in software, rather than in dedicated hardware appliances, in order to scale capacity and introduce new services in a fast and cost-effective way. In this paper1we study the optimization and resource allocation problems taking into account the deployment of virtualization structures. Our aim is to develop a theoretical framework of resource orchestration for mobile access networks, deriving the fundamental performance limits as well as the tradeoffs among the key system parameters. We therefore study optimal, time-varying placement and chaining of network functions. With respect to existing works, our optimization framework provides a much more precise system modeling, with, among others, a separation between control and data plane functions. We perform an extensive numerical analysis using both real traffic traces provided by a mobile operator (Vodafone UK) and real positions for radio access points for the UK area, and discuss the impact of network parameters on the system performance. Numerical results show that our proposed optimization framework permits to carefully model key aspects of network virtualization and service deployment/chaining in such scenarios, thus representing a very promising framework for the design of efficient and cost-effective mobile networks.

The attractive features of millimeter-wave (mm-wave) technologies in the forthcoming 5G networks entail a rich set of network access challenges. These technologies are characterized by high-gain array antennas to overcome the huge attenuations, this requires to resort to directional transmissions during every network operation. The initial access phase is one of the most critical, because, if not properly managed, it can introduce a non-negligible access delay caused by multiple transmission attempts along several directions. We believe that contextual information about user and network conditions can boost this discovery phase. In this paper, we investigate how differently-rich context information can impact on the duration of the initial cell access. We propose several initial access procedures that can exploit different available information and cope with the presence of obstacles within the service area. Finally, relying on the contextual information on past access attempts, we develop a recommendation system based on machine-learning techniques, which, by processing this information, can derive the best directions to explore to connect incoming users.

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.

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.

We tackle the problem of a network switch enforcing fair bandwidth sharing of the same link among many TCP-like senders. Most of the mechanisms to solve this problem are based on complex scheduling algorithms, whose feasibility becomes very expensive with today's line rate requirements, i.e. 10-100 Gbit/s per port. We propose a new scheme called FDPA in which we do not modify the scheduler, but instead we use an array of rate estimators to dynamically assign traffic flows to an existing strict priority scheduler serving only few queues. FDPA is inspired by recent advances in programmable stateful data planes. We propose a design that uses primitives common in data plane abstractions such as P4 and OpenFlow. We conducted experiments on a physical 10 Gbit/s testbed, we present preliminary results showing that FDPA produces fairness comparable to approaches based on scheduling.

Traffic classification allows network operators to gain important insights to better characterize packet flows, enabling fundamental applications such as traffic engineering, network analytics and Quality of Service (QoS) enforcing. A common approach adopted for flow classification is based on Deep Packet Inspection (DPI): all the traffic is processed by a middlebox whose task is the association of a network flow to the application-level information by inspecting the entire content of the packets. The increased volume of encrypted traffic limits the type of analysis performed by network middleboxes. However, an important amount of information can still be extracted from packets belonging to the very initial phase of a connection which are transmitted in clear (e.g. DNS and TLS handshake). Furthermore, recent research work has shown that it is possible to reduce the burden on the DPI without a significant loss in classification accuracy, by limiting the amount of data processed per flow. In this paper, we propose to exploit the programmability of new stateful SDN data planes to offload down to the network the process of filtering traffic to the DPI. We show that it is jointly possible to reduce the required computing power of the DPI, as well as the network bandwidth between the switches and the DPI. By taking advantage of the flexibility of stateful data planes we also manage to delegate to switches the computation of useful network analytics metrics (such as number of packets, number of bytes and duration) which would otherwise require the DPI to inspect the entire traffic flow.

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.

The rapid evolution of mobile radio network technologies poses severe technical and economical challenges to mobile network operators (MNOs); on the economical side, the continuous roll-out of technology updates is highly expensive, which may lead to the extreme, where offering advanced mobile services becomes no longer affordable for MNOs which thus, are not incentivized to innovate. Mobile infrastructure sharing among MNOs becomes then an important building block to lower the required per-MNO investment cost involved in the technology roll-out and management phases. We focus on a radio access network (RAN) sharing situation where multiple MNOs with a consolidated network infrastructure coexist in a given set of geographical areas; the MNOs have then to decide if it is profitable to upgrade their RAN technology by deploying additional small-cell base stations and whether to share the investment (and the deployed infrastructure) of the new small-cells with other operators. We address such strategic problems by giving a mathematical framework for the RAN infrastructure sharing problem which returns the “best” infrastructure sharing strategies for operators (coalitions and network configuration) when varying techno-economic parameters such as the achievable throughput in different sharing configurations and the pricing models for the service offered to the users. The proposed formulation is then leveraged to analyze the impact of the aforementioned parameters/input in a realistic mobile network environment based on LTE technology.

To accommodate the ever-growing traffic load and bandwidth demand generated by mobile users, mobile network operators (MNOs) need to frequently invest in high spectral efficiency technologies and increase their hold of spectrum resources; MNOs have then to weigh between building individual networks or entering into network and spectrum sharing agreements. We address here the problem of radio access network and spectrum sharing in 4G mobile networks by focusing on a case when multiple MNOs plan to deploy small cell base stations in a geographical area in order to upgrade their existing network infrastructure. We propose two cooperative game models (with and without transferable utility) to address the proposed problem: for given network (user throughput, MNO market, and spectrum shares) and economic (coalition cost and mobile data pricing model) settings, the proposed models output a cost division policy that guarantees coalition (sharing agreement) stability.

Opportunistic traffic offloading has been proposed to tackle overload problems in cellular networks. However, existing proposals only address device-to-device-based offloading techniques with deadline-based data propagation, and neglect content injection procedures. In contrast, we tackle the offloading issue from another perspective: the base station interference coordination problem during content injection. In particular, we focus on dissemination of contents, and aim at the minimization of the total transmission time spent by base stations to inject the contents into the network. We leverage the almost blank sub-frame technique to keep under control the intercell interference in such a process. We formulate an optimization problem, prove that it is NP-hard and NP-complete, and propose a near-optimal heuristic to solve it. Our algorithm substantially outperforms classical intercell interference approaches, as we evaluate through the simulation of LTE-A networks.

This paper presents a hardware implementation of Openstate, an extension of OpenFlow that allows performing stateful control functionalities directly inside the switch, without requiring the intervention of an external controller. The paper shows how, with a minimal reworking of the OpenFlow's basic architecture, and reusing the same building blocks, it is possible to greatly extend the intelligence of an OpenFlow switch allowing the offload of many control task directly in the switch. An FPGA based implementation of an Openstate prototype is here presented, the different architectural design choices are discussed, and the performance and limitations of the developed prototype are examinated. Finally, the paper proposes a discussion on the performance achievable by using an ASIC implementation of the OpenState switch1.

Demand-Side Management (DSM) systems represent an efficient method to improve the performance of Smart Grid infrastructures by controlling users' power loads. In this paper, we focus our analysis on fully distributed DSM systems especially designed to reduce the peak demand of groups of residential users. In our proposed scheme, each appliance decides autonomously its scheduling using only limited information on the energy price fixed by the retailer, thus greatly reducing the system complexity as well as the need of information exchanges. We develop two schedule-selection policies based on the Proportional Imitation Rule, where at each iteration all appliances switch to a new schedule with a probability proportional to the cost difference between the actual and cheapest schedules of the previous iteration. We analyze the proposed learning methods based on realistic instances in several use-case scenarios, and show their effectiveness in terms of cost reductions (both local and system-wide) as well as convergence speed to stable and efficient system equilibria.

This work considers the strategic situation which arises when Mobile Network Operators (MNOs) coexisting in a given geographical area have to decide whether to invest in new radio access technology and whether to share the investment (and the infrastructure) with other operators. We focus on heterogeneous networks (HetNet) where MNOs add a layer of small cells to their existing macro cells. We address such strategic scenario by proposing a Mixed Integer Linear Programming formulation of the infrastructure sharing problem which takes as input techno-economic parameters as the achievable throughput in different sharing configurations, the pricing models for the service offered to the end users and the expectations on the return on investment for the mobile operators, and returns as output the “best” infrastructure/investment sharing options for the MNOs. The proposed formulation is finally leveraged to analyze the dynamics involved in the infrastructure sharing process under different techno-economic conditions in realistic network scenarios.

The problem of scheduling transmission in single hop and multi-hop wireless networks with arbitrary topology under the physical interference model has been extensively studied. The focus has been on optimizing the efficiency of transmission parallelization through a minimum-frame-length schedule that meets a given set of traffic demands using the smallest number of time slots, each of which is associated with a set of compatible (according to the interference model) transmissions. This approach maximizes the resource reuse efficiency, but in general does not correspond to the best performance in terms of end-to-end packet delivery delay for multiple source-destination pairs, due to the inherent restriction of frame periodicity. In this paper, we study the problem of scheduling to minimize the end-to-end delay in wireless networks under the Signal to Interference plus Noise Ratio (SINR) constraints, and propose two schemes. The first scheme extends the minimum-frame-length approach with a phase of time slot ordering to account for the delay metric. The second scheme directly optimizes delay without the constraint of periodic framing. We propose novel mixed integer programming models for the two schemes and study their properties and complexity. Moreover, we present an efficient heuristic method that provides good quality solutions time-efficiently.

With the advent of next-generation mobile devices, wireless networks must be upgraded to fill the gap between huge user data demands and scarce channel capacity. Mm-waves technologies appear as the key-enabler for the future 5G networks design, exhibiting large bandwidth availability and high data rate. As counterpart, the small wave-length incurs in a harsh signal propagation that limits the transmission range. To overcome this limitation, array of antennas with a relatively high number of small elements are used to exploit beamforming techniques that greatly increase antenna directionality both at base station and user terminal. These very narrow beams are used during data transfer and tracking techniques dynamically adapt the direction according to terminal mobility. During cell discovery when initial synchronization must be acquired, however, directionality can delay the process since the best direction to point the beam is unknown. All space must be scanned using the tradeoff between beam width and transmission range. Some support to speed up the cell search process can come from the new architectures for 5G currently being investigated, where conventional wireless network and mm-waves technologies coexist. In these architecture a functional split between C-plane and U-plane allows to guarantee the continuous availability of a signaling channel through conventional wireless technologies with the opportunity to convey context information from users to network. In this paper, we investigate the use of position information provided by user terminals in order to improve the performance of the cell search process. We analyze mm-wave propagation environment and show how it is possible to take into account of position inaccuracy and reflected rays in presence of obstacles.

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 H2(IC)2, a novel ICIC scheme that, in contrast to previous approaches, incurs very low overhead and is practical for dense deployments. H2(IC)2is 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 H2(IC)2performs fairly close to the benchmark provided by a centralized omniscient scheduler.

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.

Information-Centric Networking (ICN) has recently gained momentum as a promising paradigm for the next-generation Internet architecture. The first prototypes for ICN-capable routers have already been developed, and network operators will soon have the opportunity to experience the advantages introduced by this technology. However, to migrate the devices to this novel architecture, non-negligible investments should be made. Therefore, it is of utter importance to provide clear quantitative insights of the expected economic benefits that operators will experience by switching to the ICN paradigm. For these reasons, in this paper we tackle the content-aware network-planning problem, and we formulate a novel optimization model to study the migration to an ICN, in a budget-constrained scenario. Our formulation takes into account 1) traffic routing and 2) content caching. We further complement our contribution by designing a Randomized Rounding heuristic that scales up to realistic topologies composed of hundreds of nodes.

Content Delivery Networks (CDNs) have been identified as one of the relevant use cases where the emerging paradigm of Network Functions Virtualization (NFV) will likely be beneficial. In fact, virtualization fosters flexibility, since on-demand resource allocation of virtual CDN nodes can accommodate sudden traffic demand changes. However, there are cases where physical appliances should still be preferred, therefore we envision a mixed architecture in between these two solutions, capable to exploit the advantages of both of them. Motivated by these reasons, in this paper we formulate a two-stage stochastic planning model that can be used by CDN operators to compute the optimal long-term network planning decision, deploying physical CDN appliances in the network and/or leasing resources for virtual CDN nodes in data centers. Key findings demonstrate that for a large range of pricing options and traffic profiles, NFV can significantly save network costs spent by the operator to provide the content distribution service.

Opportunistic traffic offloading has been proposed to tackle overload problems in cellular networks. However, they only address the problem of deadline-based content propagation in the cellular system, given wireless environment characterization. In contrast, we cope with the traffic offloading issue from another perspective: the base station interference coordination problem. In particular, we aim at the minimization of the total transmission time spent by the base stations in order to inject contents into the network, and we leverage the recently proposed ABSF technique to keep under control intercell interference. We formulate an optimization problem, prove that it is NP-Complete, and propose a near-optimal heuristic. Our proposed algorithm substantially outperforms classical intercell interference approaches proposed in the literature, as we evaluate through the simulation of dense LTE-A network scenarios.

Recent data confirm that the power consumption of the information and communications technologies (ICT) and of the Internet itself can no longer be ignored, considering the increasing pervasiveness and the importance of the sector on productivity and economic growth. Although the traffic load of communication networks varies greatly over time and rarely reaches capacity limits, its energy consumption is almost constant. Based on this observation, energy management strategies are being considered with the goal of minimizing the energy consumption, so that consumption becomes proportional to the traffic load either at the individual-device level or for the whole network. The focus of this paper is to minimize the energy consumption of the network through a management strategy that selectively switches off devices according to the traffic level. We consider a set of traffic scenarios and jointly optimize their energy consumption assuming a per-flow routing. We propose a traffic engineering mathematical programming formulation based on integer linear programming that includes constraints on the changes of the device states and routing paths to limit the impact on quality of service and the signaling overhead. We show a set of numerical results obtained using the energy consumption of real routers and study the impact of the different parameters and constraints on the optimal energy management strategy. We also present heuristic results to compare the optimal operational planning with online energy management operation .

The Content-Centric Networking paradigm aims at improving the Quality of Service of the Internet by providing innovative features to better handle digital content distribution. A major step towards the success of this novel paradigm is to analyze and compare its performance with respect to the most popular ways in which content is disseminated in today's IP Internet. In this paper we give clear answers to this critical issue by proposing a methodology to assess how the innovative design of Content-Centric Networking behaves as opposed to the solution proposed by Content-Distribution Networks. We develop a novel optimization model to study the performance bounds of a Content-Centric Network, by addressing the joint object placement and routing problem. We further introduce three comparative models that well describe 1) a Content-Distribution Network, 2) a traditional IP-based network, and 3) a Content-Centric Network whose caches are pre-populated with given contents. To the best of our knowledge, our proposal is the first that studies the performance bounds of Content-Centric Networks by means of an optimization model. Finally, we discuss the numerical results showing the performance bounds of this revolutionary paradigm. We discover that: 1) a Content-Centric Network with small caches can provide significant performance gains compared to a traditional IP-based network; 2) for large amounts of caching storage, the benefits of using sophisticated cache replacement policies are dramatically reduced and 3) in some scenarios, a Content-Distribution Network with few replica servers can perform better than a Content-Centric Network, even when the total amount of available caching storage is exactly the same.

Information Centric Networking (ICN) is a novel paradigm that aims at improving the performance of today's Internet by supporting universal caching and multicast content delivery features on every network device.

We address a traffic engineering problem where, given a communication network and a set of origin-destination demands, we have to select a single-path routing for each demand and decide which communication interfaces to switch off or run at partial load so as to minimize the total operational costs. We account for the presence of renewable energy plants at some nodes of the network, as well as feed-in-tariffs, rebates and variable energy prices. We also consider the related problem of deciding where renewable energy sources (photovoltaic modules in this case) have to be installed so as to maximize the profit, while respecting a maximum investment budget constraint. We propose mixed integer optimization models for these two problems and we report results for two different network topologies.

Rate adaptation represents a key functionality of the 802.11 MAC protocol for performance enhancement. Several solutions have been proposed for improving the transmission rate of unicast communications using frame receptions/losses, BER (Bit Error Rate) and SNR (Signal to Noise Ratio) measurements. Nevertheless, rate adaptation for multicast transmissions represents a more challenging tasks due to the complexity of estimating the reception correlation of wireless links. This paper presents a novel scheme for selecting the best transmission rate for multicast communications using the packet reception correlation of the links established among the nodes of the multicast group with the access point. The proposed algorithm has been evaluated on a real-life testbed using commercial wireless cards. The results show that our solution accurately estimates the reception correlation of wireless links, thus considerably increasing the performance of multicast transmissions up to 3x and 5x in terms of throughput and delay, respectively.

Intercell coordination and cooperation techniques are some of the most promising approaches to increase the spectral efficiency of future wireless systems as required by the forecasted market needs. Among them, intercell coordinated scheduling (ICCS) arises as a near-term feasible solution due to its lower inter-BS communication requirements when compared to full cooperative approaches. In this paper we present our proposed Reservation Information Aware Intercell Coordinated Scheduling (RIA-ICCS) solution which considers application reservation information when constructing an interference graph for ICCS purposes. Our results shows that i) RIA-ICCS allows to significantly reduce the number of edges in an interference graph for ICCS solutions and its benefit increases as the number of mobile stations grows, i.e., when the system needs it most and ii) the reduced number of edges in the interference graph can be effectively translated to a lower blocking probability using state-of-the-art resource allocation algorithms.

The MESH-WISE project is aiming to address key fundamental issues in wireless mesh networking that span from fundamental performance characterization to prototyping heterogeneous-access mesh networking solutions for emergency response scenarios. In this paper we present the infrastructures that will be leveraged in this project and the latest results that have been acquired from the ones that are deployed. We further discuss the state of the art in academic and industrial resource and network management platforms and discuss how we will use existing know-how towards a centralized solution.

Wireless mesh networks (WMNs) have emerged as a flexible and low-cost network infrastructure, where heterogeneous mesh routers managed by different users collaborate to extend network coverage. This paper proposes a novel routing metric, Expected Forwarded Counter (EFW), and two further variants, to cope with the problem of selfish behavior (i.e., packet dropping) of mesh routers in a WMN. EFW combines, in a cross-layer fashion, routing-layer observations of forwarding behavior with MAC-layer measurements of wireless link quality to select the most reliable and high-performance path. We evaluate the proposed metrics both through simulations and real-life deployments on two different wireless testbeds, performing a comparative analysis with On-Demand Secure Byzantine Resilient Routing (ODSBR) Protocol and Expected Transmission Counter (ETX). The results show that our cross-layer metrics accurately capture the path reliability and considerably increase the WMN performance, even when a high percentage of network nodes misbehave.

In this paper we introduce a joint planning and energy management framework for Wireless Mesh Networks. In order to show that power management should be integrated at the planning steps to produce an effective energy-efficient network, we present a mathematical model considering a trade-off between capital and energy-related operational expenses. Results demonstrating the impact of different coverage strategies on the energy efficiency are also showed.

We propose a multi-period model to minimize the energy consumption of IP networks while guaranteeing the satisfaction of all the traffic demands. Energy savings are achieved by putting into sleep mode cards and chassis. The multi-period optimization is constrained by inter-period limitations necessary to guarantee the stability of the networks. Both exact and heuristic solutions are proposed. Results show that up to 50% of the energy savings can be achieved for realistic test scenarios in networks operated with flow-based routing protocols (i.e. MPLS).

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 introduce a novel approach that jointly optimizes planning and management for cellular networks, aiming at limiting the energy expenses while guaranteeing QoS and minimizing operators capital expenses. Results from LTE test networks confirm that an effective energy-efficient operation depends on the planning decisions and show that power savings of up to 54% can be obtained with modest increases in initial investments.

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.

Wireless mesh networks (WMNs) have emerged as a flexible and low-cost network infrastructure, where heterogeneous mesh routers managed by different users collaborate to extend network coverage. Several routing protocols have been proposed to improve the packet delivery rate based on enhanced metrics that capture the wireless link quality. However, these metrics do not take into account that some participants can exhibit selfish behavior by selectively dropping packets sent by other mesh routers in order to prioritize their own traffic and increase their network utilization. This paper proposes a novel routing metric to cope with the problem of selfish behavior (i.e., packet dropping) of mesh routers in a WMN. Our solution combines, in a cross-layer fashion, routing-layer observations of forwarding behavior with MAC-layer measurements of wireless link quality to select the most reliable and high-performance path. We integrated the proposed metric with a well-known routing protocol for wireless mesh networks, OLSR, and evaluated it using the NS2 simulator. The results show that our cross-layer metric accurately captures the path reliability, even when a high percentage of network nodes misbehave, thus considerably increasing the WMN performance.

This paper proposes a RSSI-aware (rETT) routing metric for WMN. The embedded RSSI information from the mesh nodes are extracted, processed, transformed and incorporated into the routing process. The performance of the rETT metric is then optimised and the results compared with the fundamental hop count metric and the link quality metric by Expected Transmission Count (ETX). The implementation results on OLSR show that rETT improvement on network throughput is more than double (120%) compared to hop count and a 21% improvement compared to ETX. Also, an improvement of 33% was achieved in network delay compared to hop count and 28% better than ETX. This work provides a valuable insight into adapting RSSI information in mesh network routing and the implementation method ensures that the results are realistic.

Internet energy consumption is rapidly becoming an issue due to the exponential traffic growth and the rapid expansion of communication infrastructures worldwide. In this paper we propose an off-line IP traffic engineering approach that allows to adapt the network energy consumption to different daily traffic scenarios (e.g., night, morning), by switching off and on (putting in sleeping mode and waking up) communication interfaces (links) and entire routers. We focus on routing domains where OSPF (Open Shortest Path First) protocol is adopted and we aim at optimizing energy consumption and network congestion by efficiently configuring the OSPF link weights. We present two heuristics, the Greedy Algorithm for Energy Saving (GA-ES) and the Two-stage Algorithm for Energy Saving (TA-ES). The computational results for three real network topologies show that it is possible to switch off up to 80% of the core nodes during low traffic periods (night hours), while moderately increasing the network congestion.

Energy demand management for residential users is a promising research area within the Smart Grid revolution. The whole energy generation and distribution system performance can indeed be improved by optimizing the house energy management while still meeting the energy needs of customers. In this paper we propose Non-Cooperative models for the optimal planning of energy loads of a single house, with the final goal of minimizing the energy bill. A Pre-emptive variant of the model is also presented where some appliances activities can be interrupted in some phases. Finally, Cooperative models are defined for jointly managing the energy consumption of a group of users in order to easily decrease the aggregated maximum power demand, thus reducing the peak absorption. Numerical results, obtained applying models on realistic data, are presented and discussed.

This paper addresses the problem of energy consumption in the Metropolitan Area Networks that employ Carrier Grade Ethernet. The objective is to minimize the energy consumption of nodes and links, managing the traffic through the Multiple Spanning Tree Protocol (MSTP), where the spanning trees are appropriately chosen from a given set and matched with the traffic demands. We present a modelling framework, a resolution approach and comment on the different features that affect energy performance.

Abstract Wireless Mesh Networks (WMNs) have emerged recently as a technology for next‐generation wireless networking. They consist of mesh routers and clients, where mesh routers are almost static and form the backbone of WMNs. WMNs provide network access for both mesh and conventional clients. In this paper, we propose DSA‐Mesh, a fully distributed security architecture that provides access control for mesh routers as well as a key distribution scheme that supports layer‐2 encryption to ensure security and data confidentiality of all communications that occur in the backbone of the WMN. DSA‐Mesh exploits the routing capabilities of mesh routers: after connecting to the access network as generic wireless clients, new mesh routers authenticate to a key management service (consisting of several servers) implemented using threshold cryptography, and obtain a temporary key that is used both to prove their credentials to neighbor nodes and to encrypt all the traffic transmitted on wireless backbone links. A key feature in the design of DSA‐Mesh is its independence from the underlying wireless technology used by network nodes to form the backbone. Furthermore, DSA‐Mesh enables seamless mobility of mesh routers. Since it is completely distributed, DSA‐Mesh permits to deploy automatically and incrementally large WMNs, while increasing, at the same time, the robustness of the system by eliminating the single point of failure typical of centralized architectures. DSA‐Mesh has been implemented in Network Simulator, and extensive simulations have been performed in large‐scale network scenarios, comparing it to a static key approach and to a centralized architecture where a single key server is deployed. Numerical results show that our proposed architecture considerably increases the WMN security and reliability, with a negligible impact on the network performance, thus representing an effective solution for wireless mesh networking. Copyright © 2010 John Wiley & Sons, Ltd.

A fundamental and computationally challenging optimization task in wireless networks is to maximize the number of simultaneous transmissions, subject to signal-to-noise-and-interference ratio (SINR) requirements at the receivers. The conventional approach guaranteeing global optimality is to solve an integer programming model with explicit SINR constraints. These constraints are however numerically very difficult. We develop a new integer programming algorithm based on a much more effective representation of the SINR constraints. Computational experiments demonstrate that the new approach performs significantly better in proving optimality.

In this paper we present a work in progress within the European project AIM for the design of a system based on wireless sensor networks to save energy in home environments.

Abstract This article presents a column generation approach to a resource allocation problem arising in managing Wireless Mesh Networks. The problem consists in routing the given demands over the network and to allocate time resource to pairs of nodes. Half‐duplex constraints are taken into account together with the aggregate interference due to simultaneous transmissions, which affects the signal quality. Different problems are considered, according to the assumptions on the transmission power and rate. The resource allocation problem can be formulated as a Mixed Integer Linear Programming (MILP) problem and dealt with a column generation‐based approach. The pricing problem, due to signal quality constraints, turns out to be computationally demanding. To tackle these difficulties, besides a classical mathematical programming approach, we have applied a hybrid column generation approach where the pricing subproblem is solved using Constraint Programming. Numerical results show that the two methods are comparable. The results of the column generation are then used to solve heuristically the problem. The obtained results provide very small gaps (between lower bounds and Heuristic solutions) for two of the three considered problems and reasonable gaps for the third problem. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010

Abstract One of the most important benefits that WiMAX technology brings, the ability to provide differentiated quality of service (QoS) guarantees, could also prove to be the largest problem for system designers, because scheduling mechanisms able to cope with these demands have not been explicitly defined in the standard. In order to facilitate the understanding of how various scheduling schemes perform in a real system, we present here a detailed performance evaluation of some utility‐based scheduling algorithms, covering aspects like fairness and QoS provisioning. Through a series of extensive simulations, we analyse the ability of the scheduling schemes considered to strike a balance between fairness among users, or more restrictively, user QoS requirement satisfaction, and system efficiency maximization. Further, we show how several simple algorithms could be used as building blocks, constructing a powerful mechanism that allows the system designer to obtain any desired system behaviour, or even to dynamically change from one profile to another, depending on specific network‐related conditions. More specifically, by combining the benefits of proportional fair (PF) scheduling with the highly desirable system capacity maximization, and also taking into account a peak‐to‐average (PTA) channel quality metric, we are able to define a rule that outperforms traditional scheduling schemes, copes with various network conditions and provides graceful service degradation. Our results indicate that, by exploiting the intrinsic properties of orthogonal frequency division multiple access (OFDMA) as well as the mechanisms of the WiMAX system that are not regulated by the standard, one could increase the system efficiency, while fully respecting the QoS guarantees imposed. The use of algorithms that provide graceful performance degradation is highly advisable, in order to be able to employ a non‐conservative call admission control (CAC) mechanism, which further improves the overall spectral efficiency by maintaining the system close to saturation at all times. Copyright © 2009 John Wiley & Sons, Ltd.

Wireless Mesh Networks (WMNs) have recently emerged as a technology for next-generation wireless networking. WMNs partially replace wired backbone networks, and it is therefore reasonable to plan carefully radio resource assignment to provide quality guarantees to traffic flows. Directional transmissions allow to reduce radio interference, thus exploiting spatial reuse. Therefore, as a main contribution, in this paper we study the joint routing and scheduling optimization problem in Wireless Mesh Networks where nodes are equipped with directional antennas. To this aim, we assume a Spatial reuse Time Division Multiple Access (STDMA) scheme, a dynamic power control able to vary the emitted power slot-by-slot, and a rate adaptation mechanism that sets transmission rates according to the Signal- to-Interference-and-Noise Ratio (SINR). We provide column generation-based heuristic approaches for the proposed models in a set of realistic-size instances and discuss the impact of different parameters on the network performance. The results show that our schemes increase considerably the total traffic accepted by the network, providing bounds to the achievable performance.

We consider surveillance applications through wireless sensor networks (WSNs) with fully accessible areas to be monitored. In this context, 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 exposure as a measure of coverage 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 a heuristic that is able to provide near-optimal solutions of the same instances in short computing time and also to tackle large size instances.

In future generation wireless access networks, the users will have the chance of choosing among multiple connectivity opportunities provided by different access networks (network selection problem). Moreover, the network operators themselves will have to implement effective resource allocation strategies taking wise decisions on the used technologies, frequencies, power levels, etc. This paper proposes a game-theoretic framework to model the problems of network selection and resource allocation, capturing the interdependencies of decisions taken by different players (users vs networks). Namely, we cast the problem as a non-cooperative game where users and access networks act selfishly according their specific objectives: maximization of the perceived quality of service for the end users, maximization of the number of customers for the access networks. We characterize the equilibria of the game by resorting to mathematical programming, and we derive numerical results to assess the "quality" of the equilibria.

Next generation wireless systems are expected to provide broadband access service and efficient support to multimedia applications. To overcome the limitation due to harsh propagation environments and the scarce spectrum availability, advanced transmission techniques based on multicarrier schemes and adaptive modulation are commonly considered key elements. However, in order to fully exploit these transmission schemes, scheduling mechanisms able to take advantage of the diverse channel conditions experienced by users and guarantee at the same time quality of service, are of paramount importance. In this paper we first propose a scheduling scheme that increases the achievable throughput by combining multiuser diversity with channel prediction. Then we extend the proposed scheme to incorporate quality of service requirements of multimedia services. The proposed solution has been validated within the framework of the 1ST WINNER project by means of system level simulations.

We tackle the problem of providing minimum data-rate guarantees for different classes-of-service in an OFDMA-based network, while obtaining a high system throughput. Our approach is based on the principles of opportunistic scheduling, leveraging multiuser and frequency diversity. By giving a higher priority for transmission to the users that have the instantaneous channel quality better than their respective average value over a time window, we add an additional degree of liberty for the packet scheduler: time diversity. Using this user-centric approach instead of a system-centric one, simulations have shown that the proposed algorithm always succeeds in fully meeting the guaranteed data- rates for all users, even under heavy loads. The size of the time window over which guarantees are provided allows for a flexible control of both the hardness of the guarantees provided and the level of the delays introduced. We present performance metrics of our low-complexity heuristic in comparison with established algorithms from the literature and provide an upper bound using a mixed-integer programming model.

Service overlay networks (SONs) can provide end-to-end quality of service guarantees in the Internet without requiring significant changes to the underlying network infrastructure. A SON is an application-layer network operated by a third-party Internet service provider (ISP) that owns a set of overlay nodes, residing in the underlying ISP domains, interconnected by overlay links. The deployment of a SON can be a capital-intensive investment, and hence its planning requires careful decisions, including the overlay nodes' placement, the capacity provisioning of overlay links as well as of access links that connect the end-users to the SON infrastructure. In this paper, we propose two novel optimization models for the planning of SONs. The first model minimizes the SON installation cost while providing full coverage to all network's users. The second model maximizes the SON operator's profit by further choosing which users to serve, based on the expected gain, and taking into consideration budget constraints. We also introduce two efficient heuristics to get near-optimal solutions for largescale instances in a reasonable computation time. We provide numerical results of the proposed models and heuristics on a set of realistic-size instances, and discuss the effect of different parameters on the characteristics of the planned networks. We show that in the considered network scenarios the proposed heuristics perform close to the optimum with a short computing time.

The new packet services and applications are radically changing not only traffic demands but also the architecture of transport networks. Adding packet layer support into backbone nodes is considered an important opportunity by telecom carriers that want to integrate packet technologies of the access and metropolitan networks within their networks to improve efficiency and flexibility. In this context, Multi- Protocol Label Switching (MPLS) is at the moment the most promising and popular packet technology. The resulting new network architecture requires new models and methodologies for designing the network in a cost effective way. The classical network design approaches are no longer appropriate since they cannot capture the traffic aggregation effect due to the added packet layer and new multi-layer network design models must be adopted. In a previous work we proposed a mathematical programming model for the design of two-layers networks in the presence of point-to-point traffic demands. In this paper we extend the model to the case of multicast services which are becoming more and more popular due to IPTV applications. A MPLS layer is considered on top of a transport network (SDH or WDM depending on required link speed) and the conflicting effects of the additional cost of MPLS support in the nodes, and the cost saving due to traffic aggregation are taken into account in the models. Traffic aggregation allows to save bandwidth due to the statistical multiplexing and the granularity of capacity values available for links and nodes. We present a MIP (Mixed Integer Programming) model for the problem that aims at optimizing the number and location of MPLS nodes in the network and the link capacity dimensioning. The problem includes the minimum cost Steiner-tree problem as a special case and is, therefore, NP-hard. We propose a local search based heuristic. This algorithm provides reasonable good solutions in short computational time as shown by the computational results obtained for realistic instances.

In this paper we propose SPARE MAC, a TDMA based medium access control (MAC) scheme for data diffusion in wireless sensor networks (WSNs). The rationale behind SPARE MAC is to spare energy through limiting the impact of idle listening and traffic overhearing. To this extent, SPARE MAC implements a distributed scheduling solution which assigns to each sensor specific radio resources (i.e., time slots) for reception, summarized as reception schedules (RS), and spreads the information of the assigned RS to neighboring sensors. A transmitting sensor can consequently become active in correspondence of the RS of its intended receiver only. We analyze the performance of SPARE MAC in terms of throughput, power consumption, and data delivery delay both through analytical models and through detailed simulations. Moreover, we compare the performance of SPARE MAC against SMAC.

Wireless mesh networks (WMNs) have raised great interest in both academy and industry thanks to their flexibility, low cost and easy deployment characteristics. MobiMESH is a WMN implementation that provides a complete framework for analyzing, studying and testing the behavior of a mesh network in a real-life environment. MobiMESH includes seamless mobility support for mesh clients, enhanced multi-radio-aware routing, channel assignment, as well as provisioning, management, monitoring and security platforms.

Wireless MESH networks are a new networking paradigm that allow to extend the coverage of traditional wireless access networks with multi-hop connections through fixed wireless mesh routers. Wireless MESH networks partially replace wired backbone networks, and it is reasonable to carefully plan radio resource assignment in order to provide quality guarantees to traffic flows. Differently from ad hoc networks, energy consumption is usually not a problem with wireless MESH routers, routes are quite stable and bandwidth requirements of traffic flows can be considered almost constants. In this paper we study the scheduling optimization problem in wireless MESH networks assuming a time division multiple access (TDMA) scheme, a dynamic power control able to vary emitted power slot-by-slot, and a rate adaptation mechanism that sets transmission rates according to the signal-to-interference-and-noise ratio (SINR). Traffic quality requirements are expressed in terms of minimum bandwidth and modelled with constraints defining the number of information units (packets) that must be transmitted on each link per frame. We propose an alternative problem formulation where decision variables represent compatible sets of links active in the same slot. Approaches to solve both lower and upper bound for the problem are proposed: since compatible set variables are exponentially many, we use column generation to compute a lower bound for the problem. Heuristic approaches to compute feasible integer solutions are proposed and tested

Wireless local area networks (WLANs) are spreading all over the planet with impressive speed and market penetration. They will replace traditional indoor wired local networks and allow flexible access outdoor, eventually competing with classical cellular systems (GSM, GPRS, UMTS, etc.) in the provision of wireless services. Although the small systems currently installed are planned using rules of thumb, their rapid spread and size increase requires quantitative methods to determine proper access points (AP) positioning. Previously proposed approaches to the coverage planning neglect the effect of the IEEE802.11 access mechanism, which limits system capacity when access points coverage areas overlap. Here we propose a new modelling approach that directly accounts system capacity and show that the resulting optimization problems of WLAN coverage planning can be seen as extensions of the classical set covering or maximum coverage problems. We present and discuss different formulations based on quadratic and hyperbolic objective functions and report some preliminary results on synthetic instances we generated.

Beam forming techniques of adaptive antenna arrays (smart antennas) allow to reduce the mutual interference of simultaneous transmission in wireless access systems exploiting angular separation of user terminals. At the radio resource management layer the information on the arrival direction of signals can be taken into account by the scheduling algorithm so that transmissions of too close user terminals can be scheduled in different time-slots, while transmissions of users with an enough angular separation can be simultaneous. In other words, time diversity is exploited by the scheduling algorithm when spatial diversity is not sufficient to obtain good quality transmissions. In this paper we propose a novel approach to the problem of packet scheduling with smart antennas using mathematical programming. Based on a simplified system model we formulate two combinatorial optimization problems. In the first problem we have to select a subset of users, which can be simultaneously served in a given time slot so as to maximize the number or the total priority of the users served. An arc-circular model is proposed together with an exact polynomial-time algorithm, which searches for a path of maximum total weight in an appropriate graph. In the second problem users must be partitioned into non-interfering subsets so as to minimize the number of time slots needed to transmit all the given packets. For both problems heuristics have been devised in order to obtain approximate solutions in the short time available for packet scheduling in real systems.

Finding a path in the network for each traffic flow able to guarantee some quality parameters such as bandwidth and delay is the task of QoS routing algorithms developed for new IP networks based on label forwarding techniques as Multiprotocol Label Switching (MPLS). In this paper we focus on Dynamic QoS Routing, i.e. the routing of bandwidth guaranteed flows in a dynamic scenario where new connection requests arrive at the network edge nodes. When more than one path satisfying the bandwidth demand exists, the selection of the path aims at minimizing the blocking probability of future requests. We propose two novel mathematical programming models that assume the knowledge of arrival times and durations of connection requests, and provide theoretical bounds to the performance achievable by on-line routing algorithms. We compare to such bounds the performance of the Min-Hop (MH) algorithm, the Minimum Interference Routing Algorithm (MIRA) and the recently proposed Virtual Flow Deviation (VFD) algorithm. We show that the blocking probability of this new algorithm, in most scenarios, is quite close to the bound.

The authors present a method for computing the probability of undetected error for hybrid ARQ over the Gilbert-Elliott channel (Gilbert, E.N., Bell Syst. Tech. J., vol.39, p.1253-65, 1960; Elliott, E.O., Bell Syst. Tech. J., vol.42, p.1977-97, 1963). The method described by Elliott is extended to the case where the code is used for error detection and error correction simultaneously (hybrid ARQ, type-I). This analytical approach to the evaluation of the probability of undetected error allows the throughput to be calculated as a function of the code rate and length, and systems to be designed that maximize the throughput, given the channel parameters and the desired undetected error probability.

The use of enhanced bandwidth estimation procedures within the congestion control scheme of TCP was proposed recently as a way of improving TCP performance over links affected by random loss. This paper first analyzes the problems faced by every bandwidth estimation algorithm implemented at the sender side of a TCP connection. Some proposed estimation algorithms are then reviewed, analyzing and comparing their estimation accuracy and performance. As existing algorithms are poor in bandwidth estimation, and in sharing network resources fairly, we propose TIBET (time intervals based bandwidth estimation technique). This is a new bandwidth estimation scheme that can be implemented within the TCP congestion control procedure, modifying only the sender-side of a connection. The use of TIBET enhances TCP source performance over wireless links. The performance of TIBET is analyzed and compared with other schemes. Moreover, by studying TCP behavior with an ideal bandwidth estimation, we provide an upper bound to the performance of all possible schemes based on different bandwidth estimates.

The problem of planning third generation UMTS networks with a W-CDMA radio interface in investigated. In previous work, we have proposed discrete optimization models and algorithms for supporting the decisions on where to locate base stations in which antenna configuration to select considering quality constraints for the uplink (mobile to base station) direction. The signal-to-interference ratio (SIR) is considered as quality measure and we aim at a trade-off between maximizing coverage and minimizing installation costs. In this paper we present two mathematical programming models for locating directive base stations considering downlink (base station to mobile) direction and assuming a power-based as well or a SIR-based power control mechanism. The downlink direction is expected to be particularly relevant in the presence of asymmetrical traffic deriving, for instance, from data service. A randomized greedy procedure as well as a Tabu search algorithm is adapted to find good approximate solution of the resulting NP-hard downlink BS location problem. Experimental results obtained for realistic instances with voice as well as data traffic are reported and they are compared with those provided by the uplink models and algorithms.

Ad-hoc networks, though an attractive solution for many applications, still presents many unsolved issues, such as the hidden-terminal problem, flexible and prompt access, QoS provisioning, and efficient broadcast service. In this paper, we present a MAC architecture able to solve the above issues in environments with no power consumption limitation, such as networks for inter-vehicle communications. This new architecture is based on a completely distributed access technique, RR-ALOHA, capable to dynamically establish on a slotted/framed structure a reliable single-hop broadcast channel for each active terminal on the network. Though the proposed MAC uses a slotted channel, it can be adapted to operate on the physical layer of different standards, including the UMTS terrestrial radio access TDD, and the IEEE 802.11. The paper presents the mechanisms that compose the new MAC: the basic RR-ALOHA protocol, an efficient broadcast service and the reservation of point-to-point channels that exploit parallel transmissions. Some basic performance figures are discussed to prove the effectiveness of this protocol.

A flexible radio resource utilization in W-CDMA systems, like UMTS, can be achieved using call admission control (CAC) schemes since the number of calls admitted can be adjusted according to interference conditions. Efficient CAC schemes proposed for the uplink direction take the acceptance decision estimating the effect of new call on the signal-to-interference ratio (SIR) of the already active calls. In order to reach this goal, predictive schemes measure the total received power at the base stations and estimate the interference increase due to the new call. In this paper we propose a new predictive scheme, iterative interference-increase estimation (I/sup 3/E), and compare its performance with that of previously proposed schemes based on the estimation of the noise raise. We show that the traffic acceptable with the noise raise scheme is far from the upper capacity bound, while with our new scheme it can be increased remarkably and at the same time the call dropping probability can be kept very low.

Classical coverage models, adopted for second-generation cellular systems, are not suited for planning Universal Mobile Telecommunication System (UMTS) base station (BS) location because they are only based on signal predictions and do not consider the traffic distribution, the signal quality requirements, and the power control (PC) mechanism. We propose discrete optimization models and algorithms aimed at supporting the decisions in the process of planning where to locate new BSs. These models consider the signal-to-interference ratio as quality measure and capture at different levels of detail the signal quality requirements and the specific PC mechanism of the wideband CDMA air interface. Given that these UMTS BS location models are nonpolynomial (NP)-hard, we propose two randomized greedy procedures and a tabu search algorithm for the uplink (mobile to BS) direction which is the most stringent one from the traffic point of view in the presence of balanced connections such as voice calls. The different models, which take into account installation costs, signal quality and traffic coverage, and the corresponding algorithms, are compared on families of small to large-size instances generated by using classical propagation models.

The provision of multimedia services to mobile users is one of the main goals of third generation (3G) systems. The traffic being transferred within 3G mobile networks will be composed by different information flows with various constraints on the required QoS (bit rate, delays, etc...). In order to reach such a goal, 3G standardization bodies have designed highly-flexible radio interfaces, characterized by a great number of physical parameters to be set by the operators. UMTS (Universal Mobile Telecommunication System) offers both circuit switched and packet switched transfer mode, and within each transfer mode, different QoS can be achieved by properly setting physical parameters such as the speed of physical channels, the power control scheme, the rate of the FEC protecting code, etc. We give an evaluation of the performance of W-CDMA UMTS radio access network (UTRA) when providing access to multimedia services. In particular, we analyze through detailed simulations a typical scenario where voice calls and Web-browsing sessions share the same frequency carrier, the former using the dedicated channels (DCH), the latter being transferred on the downlink shared channel (DSCH).

Due to the W-CDMA radio interface, the area covered by a set of UMTS base stations depends on the signal quality requirements, the power control mechanism as well as on the traffic distribution. In previous work we have proposed discrete optimization models and algorithms for locating base stations in UMTS networks. In this paper we address the general problem of optimizing base station locations as well as their configurations, such as antenna height, tilt, and sector orientation. The proposed model, which can also be used to only optimize the base station configurations, accounts for the power control mechanism typical of W-CDMA and considers the signal-to-interference ratio (SIR) as quality measure. To find good approximate solutions of this NP-hard problem, we develop a Tabu Search algorithm which takes into account traffic coverage and installation costs. Experimental results showing the effect of considering base station configurations in the planning process are reported.

The provision of high speed packet data services in an efficient way is probably the most important challenge for UNITS since this can give an advantage with respect to second generation systems. The efficiency mainly depends on the radio interface and since it is characterized by a great flexibility it is of utmost importance to investigate the effect of different configurations on the system performance. In this paper we evaluate the performance of packet data services over downlink dedicated channels (DCH) by means of detailed simulations. Due to the traffic variability, ARQ mechanism and dosed loop power control the system behavior is quite complex and not so easily predictable as with constant rate services such as voice. We study the effect of several parameters (spreading factor, code rate, channel setup delay, etc.) on the system capacity by means of the delay-throughput curves. We show that the setting of parameters may be critical for system capacity and stability. For this reason we propose a flow control mechanism which is able to guarantee stability and to make the system capacity almost independent of channel rates.

Bluetooth is a new low-cost wireless technology that is going to play an important role in communications among small electronic devices and the access to wired networking infrastructure. Bluetooth stations that communicate directly form a piconet. In a piconet one station has the role of master and the others are slaves. The access to the medium is based on a TDD (time division duplexing) scheme controlled by the master. The master sends packets to slaves in even-numbered slots triggering a transmission from slaves in the subsequent slot. Slaves are allowed to send packets only in response to a master packet. The way in which the master schedules packets transmission to slaves or polls them determines system performance. We consider the problem of designing an efficient and simple polling and scheduling scheme for Bluetooth. We propose some practical schemes and compare their performance with some ideal schemes derived from known results for polling systems.

Classical coverage models based on signal predictions, adopted for second generation cellular systems, are not suitable for planning the universal mobile telecommunication system (UMTS) base station location since the area actually covered by each base station depends on the traffic distribution, the power control mechanism as well as the signal quality constraints. In a previous paper Amaldi, Capone and Malucelli (see. Proceedings of IEEE VTC Spring 2001, 2001) presented a discrete optimization model for the UMTS base station location problem. In this paper we propose enhanced models which consider the signal-to-interference ratio (SIR) as quality measure and capture at different levels of detail the specific power control mechanism of the CDMA air interface. Moreover, we propose a tabu search algorithm for the uplink (mobile to base station) direction. The different models and algorithms are compared on realistic instances generated using classical propagation models.

The UMTS W-CDMA radio interface is characterized by great flexibility and a variety of different physical and logical channel types, rates and protections are possible, by choosing suitable parameters, such as spreading factors, code rates and ARQ schemes. In this paper we present the results, obtained by a detailed simulation, about the effect of several parameters and system alternatives on the capacity of the downlink shared channel.

The main advantage of UMTS radio interface is its flexibility in resource management allowed by the W-CDMA (wideband code division multiple access) technique. However, to exploit this flexibility it is necessary to design an efficient call admission control (CAC) scheme able to adapt the number of active calls in each cell according to interference levels and power availability. We compare the performance of two CAC styles for voice services, one based on the number of active calls and one on run-time measures of the power emitted by the base station or of the total received interference. For this last class of CAC algorithms we propose a simple scheme that allows one to perform measures when silence suppression is adopted.

This paper introduces approximate analytical models to evaluate the performance of end-to-end measurement based connection admission control (EMBAC) mechanisms, devised for the setup of real time flows over the Internet. These mechanisms rely on users probing the current congestion status of their required network path using a succession of probing packets. If the probing rate measured at the end receivers is greater than a certain threshold, users are allowed to switch to a phase of data exchange; otherwise they abort the call setup attempts. In conformance with the differentiated services framework, routers are oblivious to individual flows, and only need to give higher priority to data packets than to probing traffic. Despite the approximations introduced to make the analysis tractable, our model appears to be extremely accurate for a scenario of constant rate connections. Much less accurate, but useful as a possible starting point for future work, is the extension of the model to a scenario of variable rate connections. Simulation results are also presented in the paper to gain additional quantitative insights on the effectiveness of EMBAC to provide support for tight QoS requirements.

The increasing demand for a variety of new Internet services with different and possibly stringent QoS requirements (i.e. Internet telephony, videoconferencing etc.) requires the design of mechanisms to support QoS guarantees. The current solutions proposed in IETF, RSVP (Resource reSerVation Protocol) and differentiated services, though suitable for many applications, may result inefficient to support real-time services on a call basis. The RSVP is not really scalable and requires substantial changes in the Internet architecture, while differentiated services provide guarantees mostly on a static and permanent basis. In this paper we assume an Internet architecture supporting multiple priorities as needed in differentiated services, and introduce the PCP (phantom circuit protocol), a mechanism that provides a guaranteed bandwidth transport service for circuit oriented connections. The PCP includes a fully scalable call admission control (CAC) and operates on a per call basis. Simulation of the protocol performance for CBR (constant bit rate) traffic under various network conditions show the adherence of the mechanism to theoretical expectations.

In wireless ATM systems, the introduction of an error control scheme at the air interface is mandatory in order to meet the constraints on the cell loss rate. Error control must be able to cope with the characteristics of the radio channel which is usually a channel with memory characterized by burst of errors, commonly modeled as a Gilbert-Elliott channel. Common approaches to the problem include standard forward error correction (FEC) techniques with bit interleaving implemented at the physical layer, and ARQ (automatic repeat request) techniques implemented at the MAC/DLC layer for non real-time applications only. However, the peculiar characteristics of the Gilbert-Elliott channel, for an extended range of channel parameters, are better exploited by other approaches. In this paper, we consider a wireless ATM access interface based on a TDMA scheme and real-time services with stringent delay constraints. The efficiencies of three error control schemes are compared: the classical FEC plus bit interleaving, a scheme based on block erasure codes, and a real-time ARQ scheme proposed in a previous paper. It turns out that the delay constraints can severely limit the performance of the FEC with respect to the other approaches.

The capacity of mobile cellular systems is mainly limited by cochannel interference. Commonly in multiple carrier-time division multiple access (MC-TDMA) systems, like the global system for mobile (GSM) communications, the interference noise is first reduced by means of channel clustering and slow frequency hopping. The residual interference, characterized by a level varying slot by slot, causes errors in burst which are spread with bit interleaving and fought with correcting codes for random errors. With real-time services like voice, no other error control technique, such as automatic repeat request (ARQ), is usually adopted. An alternative approach is given by capture division packet access (CDPA) where transmissions are spread in time adopting a dynamic slot assignment (DSA), and errors are dealt with a retransmission mechanism. In this paper, we present a comparison of this two approaches, and using a simple theoretic capacity analysis, we justify the better performance of CDPA. Moreover, by the delay performance analysis, we prove that in spite of its retransmission mechanism, CDPA is effective also with delay constrained services.

Different dynamic channel allocation (DCA) strategies based on local signal strength measures are evaluated in terms of the degree of frequency reuse they can afford. Fixed and dynamic channel allocation schemes are compared by using a simulation model, which overcomes the limitations of models traditionally used in literature. Valuable results show the superiority of DCA schemes in various scenarios characterized by different patterns of traffic and propagation conditions.

This paper investigates the ability of the DECT system to provide a cellular transmission service for data traffic composed of short messages. At first, the DECT performance is investigated in a single cell environment. Its access mechanism is analyzed in detail, discussing the overhead of the different procedures and measuring their quantitative effects. Then, the performance in the cellular environment specified by the ETSI is considered. The simulation results show a relatively small degradation in throughput, with respect to the single cell case, due to co-channel interference.

There is a rapidly growing demand for low-cost packet data services in GSM networks. Therefore, the ETSI is considering the possible introduction of a general packet radio service (GPRS) in GSM. We propose an enhancement of a GPRS proposal which improves the utilization of the radio resource. In particular, we consider a random access technique, that is very close to the PRMA, and allows mobile stations to share all data traffic channels available in the cell. In this context, the performance of typical data applications, which require short messages, are evaluated by simulations. The results obtained confirm the validity of this proposal even if more accurate investigations are needed for its complete definition.