Growing congestion in current mobile networks necessitates innovative solutions. This paper contributes a novel network planning approach for mmWave 5G networks in urban settings, focusing on Integrated Access and Backhaul (IAB) and the Smart Radio Environment (SRE). The mmWave traffic will be mainly made of short bursts to transfer large volumes of data and long idle periods where data are processed, necessitating changes in how mobile radio networks are designed. Our proposed optimization models integrate IAB with SRE technologies while leveraging the maximization of achievable peak throughput rather than conventional average throughput metrics. Results highlight the advantages of this approach during the network planning phase, providing insights into better accommodating the demands of mobile traffic without sacrificing the overall network capacity.

The growing demand for high-speed, reliable wireless connectivity in 6 G networks necessitates innovative approaches to overcome the limitations of traditional Radio Access Network (RAN). Reconfigurable Reconfigurable Intelligent Surface (RIS) and Network-Controlled Repeater (NCR) have emerged as promising technologies to address coverage challenges in high-frequency millimeter wave (mmW) bands by enhancing signal reach in environments susceptible to blockage and severe propagation losses. In this paper, we propose an optimized deployment framework aimed at minimizing infrastructure costs while ensuring full area coverage using only RIS and NCR. We formulate a cost-minimization optimization problem that integrates the deployment and configuration of these devices to achieve seamless coverage, particularly in dense urban scenarios. Numerical results confirm that this framework significantly reduces the network planning costs while guaranteeing full coverage, demonstrating RIS and NCR's viability as cost-effective solutions for next-generation network infrastructure.

The widely adopted satellite-based positioning systems have shown limitations in meeting the needs of emerging mobile radio network services, which require consistent, high-quality, real-time positioning data. This study introduces RUN-O-RAN, an innovative network-based ranging system integrated as a micro-service within the 5th generation (5G) Open Radio Access Network (O-RAN). RUN-O-RAN makes opportunistic use of uplink reference signals and is robust against hardware and network impairments. It offers seamless deployment, user transparency, and adaptability to varying application requirements, leveraging the programmability of the network. Through a custom-built testbed based on software-defined 5G base stations (gNBs) and commercial user equipment, we comprehensively evaluate this solution across diverse scenario sets and compare its accuracy against satellite-based positioning. The achieved results demonstrate how this system represents the first effective localization O-RAN micro-service.

Smart Radio Environment (SRE) is a central paradigm in 6 G and beyond, where integrating Smart Radio Environment (SRE) components into the network planning process enables optimized performance for high-frequency Radio Access Network (RAN). This paper presents a comprehensive planning framework utilizing realistic urban scenarios and channel models to analyze diverse SRE components, including Reconfigurable Intelligent Surface (RIS), Network-Controlled Repeater (NCR), and advanced technologies like Simultaneous Transmitting and Reflecting RIS (STAR-RIS) and Trisectoral NCR (3SNCR). We propose two optimization strategies—Full Coverage Minimum Cost (FCMC) and Maximum Budget-Constrained Coverage (MBCC)—that address key cost and coverage objectives by considering both physical characteristics and scalable costs of each component, influenced by factors such as NCR amplification gain and RIS dimensions. Extensive numerical results demonstrate the significant impact of these models in enhancing network planning efficiency for high-density urban environments.

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.

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.

MmWaves have been envisioned as a promising direction to provide Gbps wireless access. However, they are susceptible to high path losses and blockages, which can only be partially mitigated by directional antennas. That makes mmWave networks coverage-limited, thus requiring dense deployments. Integrated access and backhaul (IAB) architectures have emerged as a cost-effective solution for network densification. Resource allocation in mmWave IAB networks must face big challenges originated by heavy temporal dynamics, such as intermittent links caused by user mobility and blockages from moving obstacles. This makes it extremely difficult to find optimal and adaptive solutions. In this article, exploiting the distributed structure of the problem, we propose a Multi-Agent Reinforcement Learning (MARL) framework to optimize user throughput via flow routing and link scheduling in mmWave IAB networks characterized by mobile users and obstacles. The proposed approach implicitly captures the environment dynamics, coordinates the interference, and manages the buffer levels of IAB relay nodes. We design different MARL components, respectively for full-duplex and half-duplex networks. In addition, we propose an online training algorithm, which addresses the feasibility issues of practical systems, especially the communication and coordination among RL agents. Numerical results show the effectiveness of the proposed approach.

Millimeter-Wave (mm-Wave) Radio Access Networks (RANs) are a promising solution to tackle the overcrowding of the sub-6 GHz spectrum, offering wider and underutilized bands. However, they are characterized by inherent technical challenges, such as a limited propagation range and blockage losses caused by obstacles. Integrated Access and Backhaul (IAB) and Reconfigurable Intelligent Surfaces (RIS) are two technologies devised to face these challenges. This work analyzes the optimal network layout of RANs equipped with IAB and RIS in real urban scenarios using MILP formulations to derive practical design guidelines. In particular, it shows how optimizing the peak user throughput of such networks improves the achievable peak throughput, compared to the traditional mean-throughput maximization approaches, without actually sacrificing mean throughputs. In addition, it indicates star-like topologies as the best network layout to achieve the highest peak throughputs.

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

Software-Defined Networking (SDN) is becoming the reference paradigm to provide advanced Traffic Engineering (TE) solutions for future networks. However, taking all TE decisions at the controller, in a centralized fashion, may require long delays to react to network changes. With the most recent advancements in SDN programmability some decisions can (and should indeed) be offloaded to switches.In this paper we present a model to route elastic demands in a general network topology adopting a semi-distributed approach of the control plane to deal with path congestion. Specifically, we envision a Stackelberg approach where the SDN controller takes the role of Leader, choosing the most appropriate subset of routing paths for the selfish users (network switches), which behave as Followers, making local routing decisions based on path congestion. To overcome the complexity of the problem and meet the time requirements of real-life settings, we propose effective heuristic procedures which take into accurate account traffic dynamics, considering a stochastic scenario where both the number and size of flows change over time. We test our framework with a custom-developed simulator in different network topologies and instance sizes. Numerical results show how our model and heuristics achieve the desired balance between making global decisions and reacting rapidly to congestion events.

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.

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.

MmWave communications are expected to provide huge wireless access data rates. However, mmWave signals are strongly affected by high path losses and blockages, which can only be partially alleviated by directional phased-array antennas. This makes mmWave networks coverage-limited, thus requiring network densification. 3GPP has introduced Integrated Access and Backhaul (IAB) architecture as a cost-effective solution. Resource allocation in IAB networks is complicated because it has to cope with directional transmissions, device heterogeneity, intermittent links, and mobile users. While traditional optimization techniques usually struggle in these scenarios, we believe Reinforcement Learning (RL) techniques, especially Multi-Agent RL (MARL), can implicitly capture environment dynamics and lead to interference coordination among nodes. In this paper, we propose an MARL-based framework that shows remarkable effectiveness in addressing flow allocation and link scheduling for mmWave 5G IAB networks in scenarios with random obstacles and mobile users.

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.

As 5G deployments start to roll-out, indoor solutions are increasingly pressed towards delivering a similar user experience. Wi-Fi is the predominant technology of choice indoors and major vendors started addressing this need by incorporating the mmWave band to their products. In the near future, mmWave devices are expected to become pervasive, opening up new business opportunities to exploit their unique properties.In this paper, we present a novel PASsive Intrusion Detection system, namely PASID, leveraging on already deployed indoor mmWave communication systems. PASID is a software module that runs in off-the-shelf mmWave devices. It automatically models indoor environments in a passive manner by exploiting regular beamforming alignment procedures and detects intruders with a high accuracy. We model this problem analytically and show that for dynamic environments machine learning techniques are a cost-efficient solution to avoid false positives. PASID has been implemented in commercial off-the-shelf devices and deployed in an office environment for validation purposes. Our results show its intruder detection effectiveness (~99% accuracy) and localization potential (~ 2 meters range) together with its negligible energy increase cost (~ 2%).

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.

Millimeter-wave (mm-wave) technologies are the main driver to deliver the multiple-Gbps promise in next-generation wireless access networks. However, the GHz-bandwidth potential must coexist with a harsh propagation environment. While strong attenuations can be compensated by directional antenna arrays, the severe impact of obstacle blockages can only be mitigated by smart resource allocation techniques. Multi-connectivity, as multiple mm-wave links from a mobile device to different base stations, is one of them. However, the higher reliability provided by several access alternatives can be fully exploited only if uncorrelated link statuses are guaranteed. Therefore, spatial diversity must be enforced. Moreover, since interposing obstacles can block a link, short access links allow reducing the link unavailability probability. Smart base-station selections can be made once the network is deployed, however, our results show that much better results are achievable if spatial diversity and link-length aspects are directly included in the network planning phase. In this article, we propose an mm-wave access network planning framework that considers base-station spatial diversity, link lengths, and achievable user throughput, according to channel conditions and network congestion. The comparison against traditional k-coverage approaches shows that our approach can obtain much better access reliability, thus providing higher robustness to random obstacles and self-blockage phenomena.

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.

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.

The behavior of exponential backoff (EB) has challenged researchers ever since its introduction, but only approximate and partial results have been produced up to this date. This paper presents accurate results about the effect of protocol parameters on throughput and delay, assuming queues in saturation. Among the manifold results, we first introduce a simple model that provides close-form results for the approximated model known as “decoupling assumption.” Since the latter fails to provide well approximated results in many cases, we also introduce a Markovian model able to trade the precision of the results with complexity even with an infinite number of users, enabling us to get definite throughput results, such as 0.3706 with binary EB, and 0.4303 with an optimized base. Analytical considerations allow to derive the tail of the access-delay distribution, found to be slowly decreasing and with no variance as the number of users goes to infinity. Taking into account the overall performance, preliminary results seem to indicate that the exponential base b=1.35 is more appealing than the standard value b=2.

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

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.

The opportunistic utilization of third party WiFi access devices to offload customer traffic from the mobile network has recently gained momentum as a promising approach to increase the network capacity and simultaneously reduce the energy consumption of the radio access network (RAN) infrastructure. To foster the opportunistic utilization of unexploited Internet connections, we propose a new and open market where a mobile operator can lease the bandwidth made available by third parties (residential users or private companies) through their access points to increase dynamically (and adaptively) the network capacity. We formulate the offloading problem as a reverse auction considering the most general case of partial covering of the traffic to be offloaded. We discuss the conditions (i) to offload the maximum amount of data traffic according to the capacity made available by third party access devices, (ii) to foster the participation of access point owners (individual rationality), and (iii) to prevent market manipulation (incentive compatibility). Finally, we propose three alternative greedy algorithms that efficiently solve the offloading problem, even for large-size network scenarios.

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.

The Radio Access Network (RAN) infrastructure represents the most critical part for capacity planning, which usually accounts for peak traffic conditions. A promising approach to increase the RAN capacity and simultaneously reduce its energy consumption is represented by the opportunistic utilization of third party Wi-Fi access devices. In order to foster the utilization of unexploited Internet connections, we propose a new and open market, where a mobile operator can lease the bandwidth made available by third parties (residential users or private companies) through their access points to increase the network capacity and save large amounts of energy. We formulate the offloading problem as a reverse auction considering the most general case of partial covering of the traffic to be offloaded. We discuss the conditions (i) to offload the maximum amount of data traffic according to the capacity of third party access devices, (ii) to foster the participation of access point owners (individual rationality), and (iii) to prevent market manipulation (incentive compatibility). Finally, we propose a greedy algorithm that solves the offloading problem in polynomial time, even for large-size network scenarios.

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

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

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

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

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

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.