5th Generation (5G) New Radio Unlicensed (NR-U) enables Mobile Network Operators (MNOs) to extend their network capacity by leveraging nearly 2 GHz of mid-band unlicensed spectrum. However, delivering delay-sensitive services over shared spectrum is challenging due to unpredictable contention delays and variable channel conditions. To address this, the 3rd Generation Partnership Project (3GPP) introduced Channel Access Priority Classes (CAPCs), which allow multiple concurrent Listen-Before-Talk (LBT) processes with varying access probabilities. This design aims to balance diverse Quality-of-Service (QoS) requirements alongside fair access in shared spectrum environments. Traffic classes are mapped to one of the four CAPCs, allowing probabilistic time-domain resource slicing. Mapping complex, multi-dimensional QoS requirements to CAPCs, however, remains a non-trivial task, as no single CAPC optimally supports a given traffic flow under varying channel conditions and QoS demands. In this paper, we propose a QoS-aware scheduler that decouples traffic flows from specific CAPC processes. This scheduler prioritizes flows for each Transmit-Time-Interval (TTI) within a valid Channel Occupancy Time (COT) regardless of which CAPC wins contention. Our experimental results demonstrate over a 50% reduction in average delay for high-priority traffic and more than a 43% delay reduction across all traffic classes using this decoupled scheduling mechanism, achieved without modifying contention parameters. By leveraging NR-U's synchronized slot structure and refined QoS controls inherited from licensed-access frameworks, this approach significantly improves COT utilization and the delivery of delay-critical services.

With the proliferation of LEO satellite communications (SatCom) serving rural areas, there is a strong interest on exploring the performance limit (e.g., throughput) with such a service. This problem is challenging due to highly dynamic satellite positions, limited satellite beams and spectrum bandwidth, and wide disparity in number of subscribers across a vast area. Most existing analytical models fail to capture real-world characteristics of operational satellite network, such as long time interval between satellite handover and polarization in transmission. This paper makes a major step in advancing this research area by formalizing an analytical framework for LEO SatCom based on real-world satellite network. Our analytical framework addresses architectural issues such as gateway service region (GSR) and scheduling problems such as satellite/beam/channel-to-cell allocation, interference issues such as co-channel interference avoidance, polarization, and performance issues such as throughput fairness. Simulation results on a real-world satellite ephemeris (Starlink) show that the optimal scheduling solution based on our analytical framework can offer 93% scheduling efficiency while satisfying all design requirements and system constraints.

The Open Radio Access Network (O-RAN) architecture enables the deployment of third-party applications on the RAN Intelligent Controllers (RICs). However, the operation of third-party applications in the Near Real-Time RIC (Near-RT RIC), known as xApps, may result in conflicting interactions. Each xApp can independently modify the same control parameters to achieve distinct outcomes, which has the potential to cause performance degradation and network instability. The current conflict detection and mitigation solutions in the literature assume that all conflicts are known a priori, which does not always hold due to complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we introduce the first data-driven method for reconstructing and labeling conflict graphs in O-RAN. Specifically, we leverage GraphSAGE, an inductive learning framework, to dynamically learn the hidden relationships between xApps, parameters, and KPIs. Our numerical results, based on a conflict model used in the O-RAN conflict management literature, demonstrate that our proposed method can effectively reconstruct conflict graphs and identify the conflicts defined by the O-RAN Alliance.

To facilitate dynamic spectrum sharing, the FCC has designated certified SAS administrators to implement their own spectrum access systems (SASs) that manage the shared spectrum usage in the novel CBRS band. As a premise, different SAS servers must conduct periodic inter-SAS coordination to synchronize service states and avoid allocation conflicts. However, SAS servers may inevitably stop service for regular upgrades, crash down, or even perform maliciously that deviate from the normal routines, posing a fundamental operation security problem --- the system shall be robust against these faults to guarantee secure and efficient spectrum sharing service. Unfortunately, the incumbent inter-SAS coordination mechanism, CPAS, is prone to SAS failures and does not support real-time allocation. Recent proposals that rely on blockchain smart contracts or state machine replication mechanisms to realize fault-tolerant inter-SAS coordination require all SASs to follow a unified allocation algorithm. They however face performance bottlenecks and cannot accommodate the current fact that different SASs hold their own proprietary allocation algorithms.

Beam management procedures needed to support highly directional transmission links in wireless systems have been shown to be susceptible to replay attacks that can induce beam alignment failure. This paper proposes a new replay attack against beam management procedures using passive reconfigurable intelligent surfaces (RISs). For launching the proposed attack, we propose a combinatorial multi-armed bandit (CMAB)-based adversarial RIS that smartly controls which reference signals enter a certain indoor network service area to manipulate indoor user equipments (UEs) into poor beam selection. Our results indicate that our adversarial RIS can degrade outdoor-to-indoor communication by several orders of magnitude, potentially disrupting indoor communication.

While the robustness of rate-splitting multiple access (RSMA) to imperfect channel state information (CSI) is well-documented, its susceptibility to attacks launched with malicious reconfigurable intelligent surfaces (RISs) remains unexplored. This paper fills this gap by investigating three potential RIS-induced attacks against RSMA in a multi-user multiple-input multiple-output (MIMO) network: random interference, aligned interference, and mitigation attack. The random interference attack employs random RIS coefficients to disrupt RSMA. The other two attacks are triggered by optimizing the RIS through weighted-sum strategies based on the projected gradient method. Simulation results reveal significant degradation caused by all the attacks under perfect CSI conditions. Remarkably, when imperfect CSI is considered, RSMA, owing to its flexible power allocation strategy designed to counter CSI-related interference, can be robust to the attacks even when the base station is blind to them. It is also shown that RSMA can significantly outperform conventional space-division multiple access (SDMA).

This article investigates the deployment of a Near-Real-Time Radio Access Network (RAN) Intelligent Controller (near-RT RIC) on a distributed cloud infrastructure composed of multiple physical sites with different amounts of resources and associated costs. The challenge is dynamically adapting the near-RT RIC deployment to the most cost-effective arrangement while meeting the latency requirements between the near-RT RIC and the controlled nodes. We introduce an optimization model to solve the disaggregated near-RT RIC placement problem, considering a cloud-native infrastructure to minimize the placement cost while satisfying the latency-sensitive control loop requirements across the cloud-edge continuum. Moreover, we describe an experimental environment we created using geographically disparate cloud sites. We present data detailing the latencies of the communication links among these sites and the costs incurred in using this real-world infrastructure. We conduct a performance evaluation of the near-RT RIC deployment, comparing the distributed approach versus a traditional monolithic strategy and evaluating positioning costs, deployment, setup and registration times, and the control loop latency considering three scenarios. Our results show that in a cloud-native environment, the disaggregated near-RT RIC allows cost savings of up to 60% in comparison to a monolithic near-RT RIC while satisfying the control loop latency and achieving time efficiency in terms of deployment and registration of xApps and near-RT RIC components.

Open Radio Access Network (O-RAN) offers a new paradigm for the design and deployment of future RANs. The unique architecture of O-RAN presents two main challenges when designing a scheduler. First, it is impractical to obtain accurate and full Channel State Information (CSI) due to estimation errors and limited bandwidth of the fronthaul link between Open Radio Unit (O-RU) and Open Distributed Unit (O-DU). Second, the large-scale processing at an O-DU introduces difficulties in meeting the stringent time requirement in O-RAN, especially in the real-time (RT) control loop. To address these challenges, we propose R3—a real-time robust Multi-user, Multiple Input, Multiple Output (MU-MIMO) scheduler for O-RAN. R3 serves as a comprehensive scheduling solution encompassing RB allocation, MCS selection, and beamforming calculation. Most notably, R3 utilizes a limited number of CSI samples to offer probabilistic QoS guarantees. To meet the timing requirements of O-RAN, R3 decomposes the scheduling problem into two distinct sub-problems and integrates them into separate control loops. Moreover, each sub-problem is designed with a parallel structure, utilizing a reduced search space, and implemented on a GPU platform to accelerate the computation time. Experimental results demonstrate that R3 offers competitive throughput performance as the state-of-the-art while simultaneously fulfilling the QoS guarantees. Further, R3 meets the timing requirements of various control loops in O-RAN over a wide range of operating conditions.

In this paper, we present a framework for experimentation in next-generation Initial Access (IA) procedures for Millimeter Wave (mmWave) and Terahertz (THz) communications called SofTwAre-defined Mmwave INitial Access (STAMINA). The IA procedure is one of the essential components for communication systems in high frequencies, enabling directional transmitters and receivers to acquire each other's relative orientation before data transmission. While effective in establishing communication, the existing IA procedure standardized by 3GPP consumes a significant amount of radio resources. Many research efforts have proposed enhancements over the current-generation IA procedure, e.g., leveraging non-uniform beam sweep sequences or adaptive codebooks. However, no existing experimental mmWave platforms support modifications in their standard-compliant IA procedures, preventing their utilization for conducting experimental research on next-generation IA procedures. Our software-defined mmWave framework addresses this gap by combining the flexibility of Software-defined Radios (SDRs) with the directionality of mmWave front-ends to perform customizable IA procedures. We demonstrate STAMINA's ability to control mmWave frontends correctly, its increased performance over traditional static experiments, and its flexibility to customize the IA parameters to achieve different objectives. Our results show that STAMINA provides experimenters with a flexible platform for performing experiments on next-generation IA procedures.

The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-the-art techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.

In a system with energy-constrained sensors, each transmitted observation comes at a price. The price is the energy the sensor expends to obtain and send a new measurement. The system has to ensure that sensors’ updates are timely, i.e., their updates represent the observed phenomenon accurately, enabling services to make informed decisions based on the information provided. If there are multiple sensors observing the same physical phenomenon, it is likely that their measurements are correlated in time and space. To take advantage of this correlation to reduce the energy use of sensors, in this paper we consider a system in which a gateway sets the intervals at which each sensor broadcasts its readings. We consider the presence of battery-powered sensors as well as sensors that rely on Energy Harvesting (EH) to replenish their energy. We propose a Deep Reinforcement Learning (DRL)-based scheduling mechanism that learns the appropriate update interval for each sensor, by considering the timeliness of the information collected measured through the Age of Information (AoI) metric, the spatial and temporal correlation between readings, and the energy capabilities of each sensor. We show that our proposed scheduler can achieve near-optimal performance in terms of the expected network lifetime.

Millions of battery-powered sensors deployed for monitoring purposes in a multitude of scenarios, e.g., agriculture, smart cities, industry, etc., require energy-efficient solutions to prolong their lifetime. When these sensors observe a phenomenon distributed in space and evolving in time, it is expected that collected observations will be correlated in time and space. This article proposes a deep reinforcement learning (DRL)-based scheduling mechanism capable of taking advantage of correlated information. The designed solution employs deep deterministic policy gradient (DDPG) algorithm. The proposed mechanism can determine the frequency with which sensors should transmit their updates, to ensure accurate collection of observations, while simultaneously considering the energy available. The solution is evaluated with multiple data sets containing environmental observations obtained in multiple real deployments. The real observations are leveraged to model the environment with which the mechanism interacts as realistically as possible. The proposed solution can significantly extend the sensors’ lifetime and is compared to an idealized, all-knowing scheduler to demonstrate that its performance is near optimal. Additionally, the results highlight the unique feature of the proposed design, energy-awareness, by displaying the impact of sensors’ energy levels on the frequency of updates.

Future mobile networks are envisioned to become multi-service systems, enabling the dynamic deployment of services with vastly different performance requirements, accommodating the needs of diverse service providers. Virtualizing the mobile network infrastructure is of fundamental importance for realizing this vision in a cost-effective manner. While there have been extensive research efforts in virtualization for the mobile core network, virtualization in the radio access network (RAN) is still at an early stage. In this article, we present AIRTIME, a new RAN slicing system that enables the dynamic on-the-fly virtualization of RANs, with the programmability required by service providers to customize any aspect of their virtual RAN to meet their service needs. We present a prototype implementation of AIRTIME and evaluate the: (i) capacity to create virtual RANs on-the-fly, (ii) performance experienced by slice owners, (iii) isolation among multiple virtual RANs sharing the same physical infrastructure, and (iv) scalability to accommodate a large number of virtual RANs.

Network slicing markets have the potential to increase significantly the utilization of virtualized network resources and facilitate the low-cost deployment of over-the-top services. However, their success is conditioned on the service providers (SPs) being able to bid effectively for the virtualized resources. In this paper, we consider a hybrid advance-reservation and spot slice market and study how the SPs should reserve resources to maximize their services’ performance while not violating a time-average budget threshold. We consider this problem in its general form where the SP demand and slice prices are time-varying and revealed only after the reservations are decided. We develop a learning-based framework, using the theory of online convex optimization, that allows the SP to employ a no-regret reservation policy, i.e., achieve the same performance with an oracle that has full access to all future demand and prices. We extend the framework to the scenario where the SP decides dynamically its slice orchestration and hence needs to learn the performance-maximizing resource composition; and we further develop a mixed-time scale scheme that allows the SP to leverage spot-market information that is revealed between successive reservations. The proposed learning framework is evaluated using representative simulation scenarios that highlight its efficacy as well as the impact of key system and algorithm parameters.

A key challenge related to Radio Access Network (RAN) slicing is deciding how to efficiently map radio resources from the physical radio to realise RAN slices, known as the virtual wireless network embedding problem. To the best of our knowledge, this is the first paper to model and derive an analytical solution for embedding heterogeneous RAN slices with different waveforms, numerologies and Radio Access Technologies (RATs) with resources isolated down to the Physical (PHY) layer, ultimately enabling secure technology-agnostic RAN as a Service (RANaaS). First, we assess how current virtual wireless network embedding solutions model the allocation of radio resources to realise RAN slices. Then, we propose a graph-based model for embedding heterogeneous RAN slices that considers the guard bands required to ensure isolation in the frequency domain. This approach is transparent to the type and granularity of radio resources of each RAN slice, and can be extended to support RAN slices with new waveforms, numerologies and RATs. Next, we introduce a resource management optimisation problem solved at the Network Provider (NP) to determine the optimal embedding of RAN slices that maximises the total useful bandwidth occupied by tenants; and we propose three different heuristic algorithms to obtain solutions in near real-time. We compare their performance against the analytical solution using different metrics, and our results show that the best heuristic depends on the NP’s business model, e.g., using the Greedy Algorithm (GA) to increase resource utilisation or the Nearest Neighbour Algorithm (NNA) to increase the number of allocated RAN slices.

Emerging network slicing markets promise to boost the utilization of expensive network resources and to unleash the potential of over-the-top services. Their success, however, is conditioned on the service providers (SPs) being able to bid effectively for the virtualized resources. In this paper we consider a hybrid advance-reservation and spot slice market and study how the SPs should reserve slices in order to maximize their performance while not exceeding their budget. We consider this problem in its general form, where the SP demand and slice prices are time-varying and revealed only after the reservations are decided. We develop a learning-based framework, using the theory of online convex optimization, that allows the SP to employ a no-regret reservation policy, i.e., achieve the same performance with a hypothetical policy that has knowledge of future demand and prices. We extend our framework for the scenario the SP decides dynamically its slice orchestration, where it additionally needs to learn which resource composition is performance - maximizing; and we propose a mixed-time scale scheme that allows the SP to leverage any spot-market information revealed between its reservations. We evaluate our learning framework and its extensions using a variety of simulation scenarios and following a detailed parameter sensitivity analysis.

A typical Internet-of-Things (IoT) system consists of three major layers: 1) sensing; 2) communication; and 3) application (i.e., actuation and control) layers. The co-design of these layers has been studied for over two decades, dating back to the concept of communication, computing, and control, i.e., 3C, convergence in the 1990s. Nowadays, with the emergence of wireless-networked machine-type applications, such as connected autonomous driving and factory automation, this co-design is more urgently desired than ever to meet the stringent quality-of-service requirements thereof. To realize this goal, the 5G wireless network of today has mainly focused on the communication part and strived to reliably achieve low air-interface communication delay, i.e., ultra-reliable and low-latency communications (uRLLC). However, more and more wireless communications in IoT are based on status updates instead of general content delivery. The current uRLLC design is insufficient to characterize the status update quality, and thus is unable to optimize for timely status update with constrained wireless resources. Therefore, the performance of computing and control in IoT networks that rely highly on wireless communications is suboptimal.

Abnormal behavior in mobile cellular networks can cause network faults and consequent cell outages, a major reason for operational cost increase and revenue loss for operators. Nonetheless, network faults and cell outages can be avoided by monitoring abnormal situations in the network and acting accordingly. Thus, anomaly detection is an important component of self-healing control and network management. Network operators may use the detected abnormal behavior to quantify numerically their intensity. The quantification of abnormal behavior assists the characterization of potential regions for infrastructure updates and to support the creation of public policies for local connectivity enhancements. We propose an unsupervised learning solution for anomaly detection in mobile networks using Call Detail Records (CDR) data. We evaluate our solution using a real CDR data set provided by an Italian operator and compare it against other state-of-the-art solutions, showing a performance improvement of around 35%. We also demonstrate the relevance of considering the distinct traffic patterns of diverging geographic areas for anomaly detection in mobile networks, an aspect often ignored in the literature.

A key distinction between today’s and future networks is the appetite for reliable communication to support emerging critical-communication services. In this paper, we study multi-operator connectivity as a form of redundancy to support the design of reliable networks and investigate its trade-offs. This approach is motivated by 3GPP standardisation initiatives of dual-connectivity and similar techniques in industrial wired networks. We deploy a risk awareness performance metric to assess reliability: this superquantile metric accounts for periods of connectivity shortfalls. Our analysis shows that multi-operator connectivity brings significant reliability gains, in particular when network deployments by different operators exhibit high complementarity in coverage. We also explore the effects of multi-connectivity on spectral efficiency in times of high demand for bandwidth. Our study is based on a real-world dataset comprising signal strength indicators of three mobile operators in Dublin, Ireland.

In this work, we investigate opportunities for network operators to reduce their expenditures through optimal allocation of virtual Base-Band Units (vBBUs) in Fog Radio Access Networks (F-RANs). The optimal allocation can generate additional revenue opportunities by leasing idle processing resources to Application Service Providers (ASPs). In particular, we address the challenge of improving vBBU allocation in terms of optimal assignment of the workloads of Remote Radio Heads (RRHs) to Micro Data Centers (MDCs) for cost minimisation, considering the trade-off between MDC and RRH distance and processing power consumption. Thus, we propose an optimisation model to decide the assignments between MDCs to RRHs. The optimal solution is obtained through Binary Integer Linear Programming (BILP). We evaluate our solution by applying a real Call Detail Record (CDR) data set, simulating different regions from Milan. K-means clustering was used to identify the Internet traffic behaviour of different regions in Milan. This work results' highlight opportunities for network operators to exploit their infrastructure usage and increase their gains.

The next generation of mobile networks will bring an appetite for reliable communication, which enables emerging critical-communication services. In this paper, we present network sharing between operators as a way to provide increased reliability while using the already existing mobile network infrastructure. Our results indicate that network sharing significantly improves network performance during periods of connectivity shortfalls, benefiting the design of reliable networks. Our conclusions are drawn from a real-world dataset of signal quality indicators for three mobile operators in Dublin, Ireland.

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. Although we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. This article introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts and then moves on to experimentally validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

Network slicing is one of the key enabling techniques for 5G, allowing Mobile Network Operators (MNOs) to support services with diverging requirements on top of their physical network infrastructure. The MNOs should be able to offer Network Slices (NSs) as a Service (NSaaS) and provide customisable and independent virtual networks to tenants. In this paper, we address the functionality and challenges for enabling Radio Access Network (RAN) as a Service (RANaaS) through radio virtualisation. We analyse the requirements for using radio hypervisors to support RANaaS, and we evaluate how the current state-of-the-art on radio virtualisation meets such requirements. We identify, formalise and address the key resource management functionality missing from existing radio hypervisors. Then, we present eXtensible Virtualisation Layer (XVL), a software layer that provides the missing resource management functionality for enabling RANaaS and can be added on top of existing radio hypervisors. We integrated XVL with a radio hypervisor, forming a RANaaS platform that can provision heterogeneous RAN slices as a service. We outline XVL's architecture and design choices, as well as evaluate its performance in terms of the computational overhead, the delay to provision virtual radios, the delay introduced to forward IQ samples, and the signal degradation. Our results show that XVL enables leveraging existing radio hypervisors for supporting the RANaaS paradigm.

There is a trade-off in network slicing between the twin goals of providing tailored performance and increasing resource utilisation through increased opportunities for sharing. To balance this trade-off, we propose a system consisting of assured resources, which are available if needed over the lifetime of a slice, and auxiliary resources, which are offered on a probabilistic basis in the short-term based on forecasted resource demand. We employ the practice of overbooking, which is widely used in many industries such as airlines and hotels, to increase resource utilisation when offering auxiliary resources. After deriving probabilistic results relating to the availability of auxiliary resources, we then design an algorithm to determine how much to overbook by. We also propose several ways of distributing auxiliary resource offers among slices, as well as providing an approach for dealing with overbookings. Finally, we highlight the conditions that are conducive to effective overbooking by examining the effects of varying several key system parameters, providing both analytical and numerical results.

Network slicing is one of the key enabling techniques for 5G, allowing Mobile Network Operators (MNOs) to support services with diverging requirements on top of their infrastructure. The MNOs should be able to offer network slices as a service and provide customisable and independent virtual networks to verticals. The slicing of an end-to-end (E2E) mobile network is divided into Core Network (CN) slicing, and Radio Access Network (RAN) slicing. In this paper, we assess the requirements for using radio hypervisors to enable RAN as a Service (RANaaS). We evaluate the current state-of-the-art on radio virtualisation with respect to these requirements and identify the missing features. Then, we present the eXtensible Virtualisation Layer (XVL), a software layer that provides the missing functionality for enabling RANaaS and can be added on top of existing radio hypervisors. We outline XVL's architecture and design choices, as well as evaluate its performance in terms of the delay to provision virtual radios, the delay introduced to forward IQ samples, and the computational overhead. Our results show that XVL enables leveraging existing radio hypervisors to support RANaaS.

The massive device deployments in the Internet of Things (IoT) generate immense amounts of data that can be leveraged to improve overall network performance. This paper outlines how data gathered from correlated sensor nodes can be used to improve the timeliness of updates of another sensor node in the network. We consider a system of two correlated information sources, i.e., sensor nodes, which periodically send updates to a gateway, regarding the observed physical phenomenon distributed in space and evolving in time. The optimal use of updates in such a system greatly depends on the correlation between the two sources, and to explore this effect we investigate three different models of the covariance between independently obtained observations of the phenomenon of the interest. We extract values for the parameters in the covariance models from data coming from a real sensor network, to provide the reader with a realistic feel for scaling parameters values and the applicability of our analysis in a real scenario. We demonstrate that using correlated information results in a significant increase in device lifetime and compare our approach to others proposed in the literature.

We develop and analyze a new user-centric networking model for ubiquitous spectrum sharing where every user can share and use the spectrum under uncertainty of their traffic models. In this concept, users when connected to the Internet (wired/wireless) can dynamically serve as access points for other users in their vicinity. For this reason, the concept is referred to as user-centric distributed spectrum sharing. Each user in spectrum sharing mode utilizes a part of its available spectrum for its own traffic and remaining part to share with users in spectrum demanding modes. The model is designed as an operator supervised double-Stackelberg game with network operators, access points, and users as main players. We study network reliability and latency of the system under uncertainty of users' traffic patterns. The numerical results show that the proposed model, depending on different settings, can significantly improve both profit and utility for network operators and users, respectively. Furthermore, network reliability is significantly improved depending on the network parameters for both users and operators.

Channel state information (CSI) plays a vital role in wireless communication systems. However, the CSI acquisition overhead is an enormous obstacle to realize the system performance improvements promised by massive connectivity and massive multiple-input-multiple-output (MIMO). To alleviate this overhead, this paper proposes a remote channel inference framework by probing the channels occupied by a source base station (BS) and inferring the channels of target BSs at geographically separated sites. The work generalizes existing literature which mainly focuses on utilizing the CSI linear correlations of adjacent antennas, by adopting a model-free deep learning framework to investigate non-linear dependence among remote CSI. The existence of such cross-BS CSI dependence is first shown by calculating the mutual information between remote channels, and the Cramér-Rao lower bound of remote CSI inference performance based on a one-ring channel model. Inspired by this finding, modern deep learning approaches are leveraged to perform remote channel inference in heterogeneous networks for both single user and multi-user scenarios. The simulation results based on ray tracing data show evident performance advantages over conventional methods, under both homogeneous and heterogeneous frequency coverage. The proposed framework achieves beamformer inference accuracy within 4.6% of the genie-aided optimum at the cost of sweeping only two beams.

Unmanned Aerial Vehicles (UAVs) acting as access points in cellular networks require wireless backhauls to the core network. In this paper we employ stochastic geometry to carry out an analysis of the UAV backhaul performance that can be achieved with a network of dedicated ground stations. We provide analytical expressions for the probability of successfully establishing a backhaul and the expected data rate over the backhaul link, given either an LTE or a millimeter-wave backhaul. We demonstrate that increasing the density of the ground station network gives diminishing returns on the performance of the UAV backhaul, and that for an LTE backhaul the ground stations can benefit from being co-located with an existing base station network.

The fifth generation of mobile networks is envisioned to provide connectivity services to a multitude of devices with vastly different requirements. Current mobile systems rely on inflexible hardware- based RF front- end that provide a "one-size-fits-all" air-interface. Instead, future mobile networks should be flexible, providing different air-interfaces for particular users and applications. In this paper, we present HyDRA, a software-defined radio virtualization layer that enables the execution of multiple programmable air-interfaces on top of one RF front-end. Our solution multiplexes digitized IQ signal samples of multiple virtual radios into a single stream. We have implemented HyDRA and experimentally evaluate its performance in a scenario that considers a base station executing LTE and NB-IoT VRs. Results obtained show that HyDRA is able to efficiently multiplex these two technologies, while the computational analysis shows that HyDRA is not CPU-intensive and can run in standard, commodity computers. HyDRA is a promising framework to enable RRH slicing, multi-radio access networks, and flexible multi-tenant networks.

In this paper, we explore the idea that 5G will permit the use of multiple waveforms, with each service employing a waveform that is best suited for it. We look at a 5G machine-type communication (MTC) scenario consisting of clustered user equipment employing device-to-device (D2D) communication, such as a smart factory with intercommunicating machinery. The overhead associated with synchronizing a large number of machine-type D2D user equipment (DUE) comes at a cost that may render synchronous communication infeasible or undesirable. Based on this motivation, we consider multiple possible combinations of prominent 5G waveform candidates for cellular users and DUEs, examining the asynchronous performance of all waveforms under consideration and using the performance of synchronous orthogonal frequency division multiplexing (OFDM) as a baseline for comparison. Specifically, we focus on the coexistence of waveforms in which the ordinary cellular users employ OFDM for synchronous communication, as in LTE, and the machine-type DUEs, operating asynchronously, employ a different waveform. When DUEs employ filter bank multicarrier with offset-QAM, the average achieved rate is marginally greater than the synchronous OFDM baseline case, and approximately 43% greater than the asynchronous OFDM case. This result is encouraging, as the benefits of asynchronous D2D communication could be enjoyed in MTC scenarios without suffering any performance reduction compared to the synchronous OFDM scenario. We then investigate how the relative performance of different waveform choices depends on the scenario by varying key parameters. Notably, for asynchronous communication, increasing the transmit power of DUEs results in diminishing benefits unless the DUEs employ a waveform that mitigates interdevice leakage interference.

In a world of open data and large-scale measurements, it is often feasible to obtain a real-world trace to fit to one's research problem. Feasible, however, does not imply simple. Taking next-generation cellular network planning as a case study, in this paper we describe a large-scale dataset, combining topology, traffic demand from call detail records, and demographic information throughout a whole country. We investigate how these aspects interact, revealing effects that are normally not captured by smaller-scale or synthetic datasets. In addition to making the resulting dataset available for download, we discuss how our experience can be generalized to other scenarios and case studies, i.e., how everyone can construct a similar dataset from publicly available information.

We investigate the problem of beamformer design for communications systems coexisting with legacy radar systems with mechanically rotating antennas. Based on measurement campaign findings that support the assumption of fast decoherence for the radar-to-base station interference within a radar rotation, as well as the assumption of very strong correlation of interference channel realizations corresponding to the same radar rotation phase, we introduce beamforming solutions exploring these channel characteristics, without imposing the need for current channel state information. Different beamformer designs are proposed, characterized by different levels of computational complexity, that are applicable both in the case of synchronous systems, where the communication system is aware of the radar rotation phase, as well as in the case of asynchronous systems.

Wireless access points on unmanned aerial vehicles (UAVs) are being considered for mobile service provisioning in commercial networks. To be able to efficiently use these devices in cellular networks it is necessary to first have a qualitative and quantitative understanding of how their design parameters reflect on the service quality experienced by the end user. In this paper we set up a scenario where a network of UAVs operating at a certain height above ground provide wireless service within coverage areas shaped by their directional antennas. We provide an analytical expression for the coverage probability experienced by a typical user as a function of the UAV parameters and demonstrate the performance trade-offs that occur as these parameters are varied.

The success of the Internet of Things (IoT) strongly depends on the development of efficient strategies for gathering and processing large amounts of data while saving energy and increasing device lifetime. This work investigates the use of correlated sources of information to improve the timeliness of data collected from sensing devices in the IoT. We consider information sources that transmit periodic updates to a gateway regarding the status of an observed process and determine the optimal update strategy for these sources. We show that there is an optimal waiting time for the first update to be sent by a secondary, correlated source such that estimation error is the lowest.

In this paper, we present a spectrum monitoring framework for the detection of radar signals in spectrum sharing scenarios. The core of our framework is a deep Convolutional Neural Network (CNN) model that enables Measurement Capable Devices (MCDs) to identify the presence of radar signals in the radio spectrum, even when these signals are overlapped with other sources of interference, such as commercial Long-Term Evolution (LTE) and Wireless Local Area Network (WLAN). We collected a large dataset of RF measurements, which include the transmissions of multiple radar pulse waveforms, downlink LTE, WLAN, and thermal noise. We propose a pre- processing data representation that leverages the amplitude and phase shifts of the collected samples. This representation allows our CNN model to achieve a classification accuracy of 99.6% on our testing dataset. The trained CNN model is then tested under various SNR values, outperforming other models, such as spectrogram-based CNN models.

Service providers are expected to play an increasingly central role in the mobile market and their relationship with the traditional mobile network operators (MNOs) is starting to change. The dilemma faced by over-the-top service-providers (OTTs) is now whether to enter into a service level agreement with the MNOs (in the same spirit of mobile virtual network operator agreements) or to invest in deploying their own network infrastructure to serve their demand. The purpose of this paper is to study the factors shaping the agreements between OTTs and MNOs and how these factors impact network planning decisions. To this end, we build a synthetic model of cellular network deployment that explores how traditional mobile operators and OTTs compete in deploying new infrastructure. Using our model in conjunction with real-world data, we find that service-driven networks are heavily influenced by regulatory decisions, and that cost structures and demand characteristics play non-marginal roles in the definition of service-driven networks.

Next generation mobile networks will rely ever more heavily on resource sharing. In this paper, we study the sharing of radio access network and spectrum among mobile operators. We assess the impact of sharing these two types of resources on the performance of spatially distributed mobile networks. We apply stochastic geometry to observe the combined effect of, for example, the level of spatial clustering among the deployed base stations, the shared network size, or the coordination in shared spectrum use on network coverage and expected user data rate. We uncover some complex effects of mobile network resource sharing, which involve nonlinearly scaling gains and performance tradeoffs related to the sharing scenario or the spatial clustering level.

Resource sharing in mobile wireless networks has been employed to reduce costs, extend coverage, and ease the entry of new players in the market. The introduction of programmability and virtualization is expected to amplify these benefits of resource sharing. In this paper, we study a new virtualization-based paradigm for resource sharing in mobile wireless networks. Specifically, we consider the problem of resource allocation, particularly when user demands are uncertain. We formulate several two-stage sequential stochastic allocation schemes that provide tradeoffs between cost and user satisfaction. These allocation schemes are studied under different resource provider pricing models. Our simulations demonstrate that: First, while reducing cost significantly, virtualization considerably improves user satisfaction, and virtualization gains increase with the number of operators that share resources. Second, the improvements in cost, user satisfaction, and resource usage increase substantially with the level of user clustering.

In this work we propose a virtualized network architecture for an infrastructure provider that shares the physical resources of a Massive MIMO cell among several virtual network operators (VNOs) using spatial multiplexing. In this architecture the infrastructure provider allocates spatial streams to the VNOs, which enables each VNO to select its own scheduling policy and user priority to differentiate its service from the other VNOs. To assign the spatial streams to the VNOs that value them the most, we propose an auction-based spatial stream allocation approach. We show that the proposed auction- based approach performs very close to the optimal (fixed) approach in the case of homogeneous static VNOs demand. In case of heterogeneous demands, the auction mechanism is able to dynamically allocate the resources according to the needs of different VNOs.

This paper analyses a scenario where a Device-To-Device (D2D) pair coexists with an Orthogonal Frequency Division Multiplexing (OFDM) based incumbent network. D2D transmitter communicates in parts of spectrum left free by cellular users, while respecting a given spectral mask. The D2D pair is misaligned in time and frequency with the cellular users. Furthermore, the D2D pair utilizes alternative waveforms to OFDM proposed for 5G. In this study, we show that it is not worth synchronising the D2D pair in time with respect to the cellular users. Indeed, the interference injected into the incumbent network has small variations with respect to time misalignment. We provide interference tables that encompass both time and frequency misalignment. We use them to analyse the maximum rate achievable by the D2D pair when it uses different waveforms. Then, we present numerical results showing what waveform should be utilized by the D2D pair according to the time-frequency resources that are not used by the incumbent network. Our results show that the delay induced by linearly convolved waveforms make them hardly applicable to short time windows, but that they dominate OFDM for long transmissions, mainly in the case where cellular users are very sensitive to interference.

The success of smartphones has encouraged over-the-top service providers to seek ways in which they can have more control over the wireless service offered to their users. Google Project Fi is an example for this type of action, where control over the wireless service is achieved by either deploying own wireless infrastructure or entering into service level agreements with mobile network operators. Following this example, we construct a game theoretic model for the interaction between mobile network operators and over-the-top service providers and assess how the spatial distribution of mobile demand impacts the outcomes of cooperation, unevenly affecting the utilities achieved by the two parties. We also show how the cost of fixed infrastructure deployed by the mobile operator, if too high, may render cooperation between the two parties ineffective.

This paper considers machine-to-machine (M2M) communication for wireless-powered Internet-of-Things (IoT) based networking systems. Motivated by the observation that transmitting signals generally requires more energy than receiving signals for most IoT-based systems, we study a special wireless-powered M2M communication system in which the receiver can send its surplus energy to the transmitter. We propose a framework of wireless powered full-duplex M2M communication (WP-FD-M2M) in which the energy transfer from the receiver to the transmitter and the data transmission from the transmitter to the receiver take place at the same time over the same frequency. We establish a stochastic game-based model, referred to as the M2M game, to characterize the interaction between autonomous M2M transmitter and receiver. We prove that, if the transmitter and receiver can sequentially optimize their data transmission and energy transfer based on the Markov strategy, it is possible to achieve the maximum long-term performance for M2M communication without a centralized controller or coordination between the transmitter and receiver. Numerical results show that our proposed approach can significantly improve the performance for M2M communication under various situations.

Through massive deployment of additional small cell infrastructure, Dense Small cell Networks (DSNs) are expected to help meet the foreseen increase in traffic demand on cellular networks. Performance assessment of architectural and protocol solutions tailored to DSNs will require system and network level simulators that can appropriately model the complex interference environment found in those networks. This paper identifies the main features of DSN simulators, and guides the reader in the selection of an appropriate simulator for their desired investigations. We extend our discussion with a comparison of representative DSN simulators.

We study the problem of how autonomous cognitive nodes (CNs) can arrive at an efficient and fair opportunistic channel access policy in scenarios where channels may be non-homogeneous in terms of primary user (PU) occupancy. In our model, a CN that is able to adapt to the environment is limited in two ways. First, CNs have imperfect observations (such as due to sensing and channel errors) of their environment. Second, CNs have imperfect memory due to limitations in computational capabilities. For efficient opportunistic channel access, we propose a simple adaptive win-shift lose-randomize (WSLR) strategy that can be executed by a twostate machine (automaton). Using the framework of repeated games (with imperfect observations and limited memory), we show that the proposed strategy enables the CNs (without any explicit coordination) to reach an outcome that: 1) maximizes the total network payoff and also ensures fairness among the CNs; 2) reduces the likelihood of collisions among CNs; and 3) requires a small number of sensing steps (attempts) to find a channel free of PU activity. We compare the performance of the proposed autonomous strategy with a centralized strategy and also test it with real spectrum data collected at RWTH Aachen.

Stochastic models of base station infrastructure deployment by multiple mobile operators can be an invaluable tool for deriving fundamental results about wireless network sharing. In this paper, we study stochastic geometry models for a shared cellular network consisting of base stations deployed by multiple mobile operators, based on real cellular network data coming from three European countries. Relying on a statistical approach as well as the evaluation of wireless network performance metrics, we show that the log-Gaussian Cox process provides the most compelling fitness results with real multi-operator base station deployment patterns and a model that offers some degree of analytical tractability. The model captures the fact that, in urban areas, there is strong correlation between the locations where the base stations of different operators are deployed. In contrast to that, in rural areas we observe some repulsion between antenna locations of different operators. Moreover, we observe that the behavior which can be modelled with the help of these processes occurs over and over again for similar areas in different countries, which suggests universality of the proposed models.

This paper describes the Flexible Internetwork Stack (FINS) Framework, an open-source tool to enable implementation-based experimental research in computer networking. The FINS Framework uses a module-based architecture that allows cross-layer behavior and runtime reconfiguration of the protocol stack. The FINS Framework is general enough to enable experimental setups under various network architectures (e.g., MANET, infrastructure, mesh) and to accelerate prototyping solutions for evolving areas (e.g., cognitive networks, cross-layer design, context-aware applications). Version 1.0 of the framework makes use of existing physical and data link layer functionality, while enabling modifications to the stack at the network layer and above, or even the implementation of a clean-slate, non-layered protocol architecture. Protocols, stubs for communicating with intact layers, and management and supervisory functions are implemented as FINS Framework modules, interconnected by a central switch. This paper describes the FINS Framework architecture, presents an initial assessment along with experiments enabled by the tool, and documents an intuitive mechanism for transparently intercepting socket calls that maintains efficiency and flexibility. Performance testing shows that the FINS Framework is capable of supporting experiments requiring IEEE 802.11g hardware speeds and operating in varying networking architecture and experimental scenarios on both Ubuntu laptops and Android devices.

A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.

This paper studies carrier aggregation between multiple mobile network operators (MNOs), referred to as interoperator carrier aggregation (IO-CA). In IO-CA, each MNO can transmit on its own licensed spectrum and aggregate the spectrum licensed to other MNOs. We focus on the case that MNOs are partitioned and distributed into small groups, called IO-CA pairs, each of which consists of two MNOs that mutually agree to share their spectrum with each other. We model the IO-CA pairing problem between MNOs as a stable roommate market and derive a condition for which a stable matching structure among all MNOs exist. We propose an algorithm that achieves a stable matching if it exists. Otherwise, the algorithm results in a stable partition. For each IO-CA pair, we derive the optimal transmit power for each spectrum aggregator and establish a Stackelberg game model to analyze the interaction between the licensed subscribers and aggregators in the spectrum of each MNO. We derive the Stackelberg equilibrium of our proposed game and then develop a joint optimization algorithm that achieves the stable matching structure among MNOs as well as the optimal transmit powers for the aggregators and prices for the subscribers of each MNO.

In this paper we present an approach towards empowering online telecommunications engineering education by enabling hands-on remote experimentation over Trinity College Dublin's wireless testbed. Moreover, in order to offer a flexible testbed, capable of fulfilling the different and particular requirements of experimenters, we have created a framework that allows the virtualization of our testbed resources to create experimentation units to be used by remote experimenters/learners. Furthermore, we present the FORGEBox framework that offers an environment and resources to create online material capable to access the virtualized and physical testbed resources for incorporating experimentation into HTML-based online educational material.

In this work we investigate the reassignment of User Equipments (UEs) between adjacent small cells to concurrently enable spatial multiplexing gains through Multi-User MIMO (MU-MIMO) and reductions in energy consumption though switching emptied small cells to a sleep state. We consider a case where UEs can be reassigned between adjacent small cells provided that the targeted neighbouring cell contains a UE with which the reassigned UE can perform MU-MIMO without experiencing excessive multi-user interference, and whilst achieving a minimum expected gain in spectral efficiency over the previous original cell transmissions as a result. We formulate the selection decision of which UEs to reassign as a set covering problem with the objective of maximising the number of small cell base stations to switch to a sleep state. Our results show that, for both indoor and outdoor LTE small cell scenarios, the proposed MU-MIMO-based reassignments achieve significant reductions in the required number of active small cell base stations, whilst simultaneously achieving increases in spectral efficiency.

This paper considers an energy harvesting communication system consisting of a communication link powered by the energy harvested from the natural environment and the energy wirelessly transferred from a remote dedicated energy source. We study three optimization approaches for this system: transmit power scheduling which allows the transmitter to sequentially optimize its transmit power, energy requesting which lets the transmitter to sequentially decide the amount of energy to be requested from the dedicated energy source, and a joint optimization approach for the transmitter to jointly decide the transmit power scheduling and energy requesting. We derive the optimal policy for each of the above approaches that can maximize the long-term average payoff of the communication link. We present numerical results to compare the performances of different optimizations under different settings and conditions.

This paper presents switched Multi-Element Antennas (MEAs) as a simple, yet effective, method of enhancing the performance of small cell heterogeneous networks and compensating for the small cell base station sub-optimal placement. The switched MEA system is a low-cost system which enables the small cell to dynamically direct its transmission power toward locations of high user density, in other words demand hotspots. Our simulation results show that small cell base stations equipped with switched MEA systems offer greater performance than base stations equipped with omni-directional antennas in terms of both the number of users that can be served (and hence offloaded from the macrocell network) and in terms of overall network capacity. We also compare the performance of the switched MEA with fixed directional antennas and show that fixed-directional antennas can only outperform the switched MEA if the misalignment between their direction of transmission and the direction to the demand hotspot is less than 22.5°.

This paper considers a cognitive radio network where the licensed network, referred to as the primary user (PU) network, consists of a hierarchical structure in which multiple operators coexist in the same coverage area where each of the operators controls an exclusive set of frequency sub-bands. Unlicensed users, referred to as the secondary users (SUs), first send their requests to the operators, and can only access the sub-bands controlled by the operators that accept their requests. SUs are selfish and cannot exchange private information with each other. We model the dynamic spectrum access (DSA) problem of the SUs as a Bayesian game, referred to as the DSA game. We model the PU network as a forest where the roots represent the operators and the leaves represent the operators' sub-bands. We propose a novel forest matching market to model the interaction between the SUs and the PU network. In this market, a set of SUs can be first matched to a set of operators and the SUs matched to the same operator can then be matched to the corresponding sub-bands. We propose a distributed algorithm that results in a stable forest matching structure, which coincides with the optimal Bayesian Nash equilibrium of the DSA game. We prove that the Bayesian hierarchical mechanism associated with our proposed algorithm incentivizes truth-telling by SUs. Our algorithm does not require each SU to know the preference and conflict-solving rule of the PU network or the payoffs and actions of other SUs, and the complexity of each iteration in the worst case is given by O(L2N2K) where L is the number of operators, N is the maximum number of sub-bands of each operator, and K is the number of SUs.

This paper studies energy-harvesting communication systems in which different energy-harvesting devices (EHDs) can harvest different amounts of energy and transmit different numbers of data packets in different time slots. We introduce a dynamic energy trading framework that allows the EHDs to transfer and trade their harvested energy with each other. The EHDs are divided into two groups: seller EHDs that can harvest more energy than they can use, and buyer EHDs that cannot harvest sufficient energy to support their required communication services. In the proposed framework, the role of each EHD as a seller EHD or a buyer EHD as well as the amount of energy that each EHD can buy or sell to others change over time. Each EHD cannot observe complete information regarding the harvested energy or the number of data packets transmitted by other EHDs. We introduce a simple energy trading scheduling protocol for the EHDs to discover their nearby EHDs and establish energy trading links with each other. We formulate a new game theoretic model called stochastic energy trading game to analyze the dynamic energy trading among EHDs in a stochastic environment. We derive an optimal energy-trading policy for each EHD to sequentially optimize its decisions. We prove that the proposed policy can achieve a stable and optimal sequence of matchings between buyer and seller EHDs. We present numerical results to compare our proposed energy trading policy with an existing transmit packet scheduling approach, under various network settings and conditions.

Implementation of carrier sensing-based medium access control (MAC) protocols on inexpensive reconfigurable radio platforms has proven challenging due to long and unpredictable delays associated with both signal processing on a general purpose processor (GPP) and the interface between the radio frequency (RF) front end and the GPP. This paper describes the development and implementation of a split-functionality architecture for a contention-based carrier sensing MAC, in which some of the functions reside on an field-programmable gate array (FPGA) and others reside in the GPP. We provide an FPGA-based implementation of a carrier sensing block and develop two versions of a carrier sense multiple access (CSMA) MAC protocol based upon this block. We experimentally test the performance of the resulting protocols in a multihop environment in terms of end-to-end throughput and required frame retransmissions. We cross-validate these results with a network simulator with modules modified to reflect the mean and variance of delays measured in components of the real software-defined radio system.

The LTE standards account for the use of relays to enhance coverage near the cell edge. In a traditional topology, a mobile can either establish a direct link to the base station (BS) or a link to the relay, but not both. In this paper, we consider the benefit of multipoint connectivity in allowing user equipment (UEs) to split their transmit power over simultaneous links to the BS and the relay, in effect transmitting two parallel flows. We model decisions by the UEs as to: (i) which point of access to attach to (either a relay or a relay and the BS or only the BS); and (ii) how to allocate transmit power over these links so as to maximize their total rate. We show that this flexibility in the selection of points of access leads to substantial network capacity increase against when nodes operate in a fixed network topology. Individual adaptations by UEs, in terms of both point of access and transmit power, are interdependent due to interference and to the possibility of over-loading of the backhaul links. We show that these decisions can converge without any explicit cooperation and derive a closed-form expression for the transmit power levels.

This paper proposes a two-stage process for assigning fungible orthogonal channel sets to multiple cognitive radios (CRs) for opportunistic spectrum access. Assigning orthogonal channel sets to the CRs eliminates the possibility of collision among them, and allows the CRs to focus on avoiding collisions with the primary user (PU). In particular, each CR uses a learning-based dynamic channel selection (DCS) algorithm to maximize the exploitation of the assigned channels. We propose a neural network that can accurately estimate the performance of the adopted learning-based DCS algorithm on a set of channels, using the duty cycle and the complexity of the PU's behavior on the channels. Our simulations on synthetic and real measurement data sets show that the proposed channel sets allocation algorithm, together with the neural network, significantly outperforms a method that selects channels with the lowest duty cycle.

We consider the spectrum sharing problem between a set of device-to-device (D2D) links and multiple co-located cellular networks. Each cellular network is controlled by an operator which can provide service to a number of subscribers. Each D2D link can either access a sub-band occupied by a cellular subscriber or obtain an empty sub-band for its exclusive use. We introduce a new spectrum sharing mode for D2D communications in cellular networks by allowing two or more D2D links with exclusive use of sub-bands to share their sub-bands with each other without consulting the operators. We establish a new game theoretic model called Bayesian non-transferable utility overlapping coalition formation (BOCF) game. We show that our proposed game can be used to model and analyze the above spectrum sharing problem. However, we observe that the core of the BOCF game can be empty, and we derive a sufficient condition for which the core is non-empty. We propose a hierarchical matching algorithm which can detect whether the sufficient condition is satisfied and, if it is satisfied, achieve a stable and unique matching structure which coincides with the overlapping coalition agreement profile in the core of the BOCF game.

This paper presents the Forging Online Education through FIRE (FORGE) initiative, which aims to transform the Future Internet Research and Experimentation (FIRE) testbed facilities, already vital for European research, into a learning resource for higher education. From an educational perspective this project aims at promoting the notion of Self-Regulated Learning (SRL) through the use of a federation of highperformance testbeds and at building unique learning paths based on the integration of a rich linked-data ontology. Through FORGE, traditional online courses will be complemented with interactive laboratory courses. It will also allow educators to efficiently create, use and re-use FIRE-based learning experiences through our tools and techniques. And, most importantly, FORGE will enable equity of access to the latest ICT systems and tools independent of location and at low cost, strengthening the culture of online experimentation tools and remote facilities.

We consider cooperative energy harvesting communication networks in which a set of source-to-destination pairs competes for a limit number of relay nodes with energy harvesting ability. The performance of each source has been affected by two interactions: the interaction between the sources and relay nodes and the interaction among sources. We model the first interaction as a college admission market and then fits this market into a stochastic environment. We formulate an interactive partially observable Markov decision process (I-POMDP) to study the second interaction. We derive the optimal policy for the sources to sequentially optimize their decisions. Numerical results show that our proposed policy significantly improves the performance of sources.

Wi-Fi, small cells and macrocell networks serve users with different advantages and drawbacks. In this paper, we propose a multi-leader multi-follower Stackelberg game between these three types of networks and mobile users. In the multi-leader game, each network first sets the optimal price according to the behaviors of the other networks and the prediction of each mobile user's optimal strategies. Subsequently, mobile users choose their optimal network, accordingly. In the proposed game, there exists a Stackelberg equilibrium between the leader level and the follower level, and two Nash Equilibria within leaders and within followers, respectively. This fact differentiates the proposed work from most existing literature. Simulation results show that the proposed approach yields high social welfare at the equilibrium.

The main contribution of this work is the proposal and assessment of the MU-MIMO across Small Cells concept. MU-MIMO is the spatial multiplexing of multiple users on a single time-frequency resource. In small cell networks, where the number of users per cell is low, finding suitable sets of users to be co-scheduled for MU-MIMO is not always possible. In these cases we propose MU-MIMO-based cell reassignments of users into adjacent cells to enable MU-MIMO operation. From system level simulations we found that, when the initial number of users per small cell is four, cell reassignment results in a 21.7% increase in the spectral efficiency gain attributed to MU-MIMO, and a higher percentage increase when the initial number of users per cell is lower. Going forward, we will extend this work to also consider energy savings through switching off small cells which are emptied by the reassignment process.

In this paper, we present an economic model for offloading data from subscribers of a large scale cellular operator to a small scale WLAN in a multi-hop cellular environment. We make use of a hexagonal tessellation deployed with relay elements to model the multi-hop capability. An incentive-based model helps to determine the behavior of the cellular and WLAN operator, as the cellular operator decides to offload its users depending upon the price charged by the WLAN operator for each offloaded user. The simulated results quantify the benefits of collaboration between the operators in terms of the offload ratio, network efficiency, and revenue gains.

With increasing mobile data demand there is a push towards heterogeneous networks. Small-scale operators (SSOs) of WLANs are becoming more prevalent, while Mobile Network Operators (MNOs) seek an outlet for their customers' data usage. These conditions prompt the need for an effective relationship between the two parties for the purpose of offloading cellular data traffic to WLANs in a way that is economically beneficial to all involved. This paper presents a model of such a relationship, in which the SSO sets a strategic offloading price per subscriber and the MNO chooses how many subscribers it wants to offload in order to minimize its costs. The application of this model is simulated in a real-world WLAN deployment in Oulu, Finland. Our findings can be used by both MNOs and SSOs to make informed network deployment decisions, even before engaging in an offloading relationship.

The purpose of this paper is to present a vision for future mobile and wireless networks. The vision, which we call Networks without Borders (NwoB), is based on a marketplace of virtual network operators which construct networks from a pool of shared resources (e.g., base stations, spectrum, core network components, cloud resources, processing capabilities, etc.). The resources will be sourced from traditional industry players as well as crowdsourced from individuals. The paper describes this approach from a value-chain perspective. The proposed value chain is substantially different from the value-chain models that are currently used to illustrate mobile and wireless networks. The economic imperatives and innovation drivers for this approach are discussed. Early work showing the promise of this vision is presented. This work focuses on diverse examples which advocate the removal of traditional and historical restrictions on spectrum and infrastructure and move toward more dynamic use of shared resources. In the first example, we look at how frequency-division-duplexing (FDD) and time-division-duplexing (TDD) restrictions on spectrum usage can be relaxed; we remove the borders between TDD and FDD. In the second example, we look at the aggregation and pooling of corporate infrastructure which uses exclusive spectrum and removes the borders between different mobile operators. Finally, we look at the aggregation of user-deployed or crowdsourced infrastructure that opportunistically uses spectrum and removes the borders between independently deployed hotspots. These are starting points, and the full realization of the vision will involve more dynamic access to spectrum and more extensive sharing of infrastructure. Hence, the final part of the paper describes the resulting research challenges.

LTE-Advanced standards contemplate support for heterogeneous networks, through the use of relays, as well as the ability to perform carrier aggregation in order to achieve higher data rates. In this paper, we model the User Equipment's (UE) ability to select its current point-of-access to the network (either a relay or the base station), aggregate additional channels, and perform power adaptations. The adaptations by UEs affect one another, by impacting the interference conditions in the network as well as congestion conditions in the relay-to-base station links. We propose an adaptation mechanism whereby the UEs consider these factors and select their transmit power, channels, and point of access to the network to optimize a combination of achieved data rate and energy consumption. Our results show these adaptations to be more efficient than adaptations that simply consider current channel quality indicators, combined with greedy channel aggregation.

Carrier aggregation is a key feature of next generation wireless networks to deliver high-bandwidth links. This paper studies carrier aggregation for autonomous networks operating in shared spectrum. In our model, networks decide how many and which channels to aggregate in multiple frequency bands, hence extending the distributed channel allocation framework. Moreover, our model takes into the account physical layer issues, such as the out-of-channel interference in adjacent frequency channels and the cost associated with inter-band carrier aggregation. We propose learning algorithms that converge to Nash equilibria in a reasonable number of iterations under the assumption of incomplete and imperfect information.

Under the opportunistic spectrum access paradigm, the shared pool of spectrum bands that the multiple autonomous cognitive radios (CRs) need to compete for is not necessarily homogeneous. The non-homogeneity in channels may lead to payoff distribution conflict among autonomous CRs, as each CR would prefer the outcome in which it selects the more desirable channels. To address this challenge, we have designed an adaptive strategy that (without explicit coordination) enables the CRs to autonomously reach an outcome that maximizes the total CR network throughput and minimizes the payoff distribution conflict among the CRs. We utilize the framework of repeated games with private monitoring to: 1) study the dynamic channel selection problem; 2) analyze the stability of the proposed strategy; and 3) investigate the impact of deviations by a selfish CR on the performance of the proposed strategy. In our model, multiple autonomous CRs are not able to observe the channel selections of other competing CRs. Rather, they get a signal from which the selections must be inferred.

To improve their ability to find spectrum opportunities, intelligent secondary radios (SR) can learn from their past observations and predict possible spectrum opportunities. However, because of the diverse behavior of primary users (PU) in different spectrum bands, spectrum holes exhibit diverse characteristics, which in turn affect the performance of a learning algorithm. This paper studies the effect of the PU's activity on channel predictability. In particular, we introduce a Markov process-based learning algorithm, and we investigate the dependency of its spectrum decisions on the duty cycle (DC) and on the complexity of each channel activity, for both synthetic and real data. Our findings show that the probability of finding a free channel among a group of considered channels strongly depends on the DC and the complexity of the channel activity. Moreover, it is possible to reduce the number of observed channels without compromising the probability of finding a free channel, by only considering the more informative channels.

In this paper, we investigate the performance of the dynamic allocation of resources between separate cellular networks. We propose a Dynamic Internetworking Carrier Aggregation (DI-CA) framework which involves every network operator releasing some of its exclusive, but excess, spectrum to another network operator for a limited time. We derive the basic condition for which DI-CA can improve the performance for all the operators and then propose a distributed scheduling framework that uses coalition formation with uncertainty, in which each independent operator can decide whether or not DI-CA can improve its performance without having information regarding channel conditions or load experienced by other operators. We propose a distributed Bayesian coalition formation algorithm to approach a neighborhood of the Bayesian Nash equilibrium.

We propose a general framework to analyze incentives for user cooperation, and characterize the tradeoff between fairness and efficiency for cooperative networks. More specifically, we define the incentive region as a set of action profiles that provides cooperation benefits to all users and focus on the optimization of efficiency and fairness within this region. We introduce a linear resource allocation (LRA) scheme and show that most existing fairness measures can be converted to LRA with different linear coefficient vectors. We then propose the concept of strong price of fairness (SPoF) to study the network efficiency of the strong equilibrium. We show that both the SPoF and fairness measures are connected to the linear coefficient vector of LRA, which makes it possible to study the fairness and efficiency relationship. We then use the random access (RA) system as an example to show how to use the proposed framework to study a specific wireless network.

In this paper, we present an approach to managing resources in a large-scale heterogeneous wireless network that supports reconfigurable devices. The system under study embodies internetworking concepts requiring independent wireless networks to cooperate in order to provide a unified network to users. We propose a multi-attribute scheduling algorithm implemented by a central Global Resource Controller (GRC) that manages the resources of several different autonomous wireless systems. The attributes considered by the multi-attribute optimization function consist of system spectral efficiency, battery lifetime of each user (or overall energy consumption), and instantaneous and long-term fairness for each user in the system. To compute the relative importance of each attribute, we use the Analytical Hierarchy Process (AHP) that takes interview responses from wireless network providers as input and generates weight assignments for each attribute in our optimization problem. Through Matlab/CPLEX based simulations, we show an increase in a multi-attribute system utility measure of up to 57% for our algorithm compared to other widely studied resource allocation algorithms including Max-Sum Rate, Proportional Fair, Max-Min Fair and Min Power.

The advancement of cognitive radio (CR) has uncovered new dynamics in multi-hop, wireless networking. Given the increased agility of a transceiver's frequency assignment, the network topology can be optimized to address end-to-end networking goals. We propose a channel assignment scheme for cognitive radio networks (CRNs) that balances the need for topology adaptation focusing on flow rate maximization and the need for a stable baseline topology that supports network connectivity. We focus on CRNs in which nodes are equipped with multiple radios or transceivers, each of which can be assigned to a channel. First, our approach assigns channels independently of traffic, to achieve basic network connectivity and support light loads such as control traffic, and second, it dynamically assigns channels to the remaining transceivers in response to traffic demand. In this paper, we focus on the traffic-independent (TI) channel assignment with the goal of dedicating as few transceivers as possible to achieving baseline connectivity. By conserving transceivers in the TI assignment, the network is more able to adapt to any traffic demands in a subsequent traffic-driven (TD) assignment. We formulate the problem as a two-stage mixed integer linear program (MILP), with a TI stage and a TD stage. We propose a centralized greedy approach to TI assignment which performs nearly identically to the optimum obtained from the two-stage MILP in terms of the number of transceivers assigned and flow rate in the evaluated scenarios. Subsequently, we propose a distributed greedy TI approach that performs within 9% of the optimum in terms of the number of transceivers assigned and within 1.5% of the optimum in terms of flow rate.

We explore the question of when learning improves the performance of opportunistic dynamic channel selection by characterizing the primary user (PU) activity using the concept of Lempel-Ziv complexity. We evaluate the effectiveness of a reinforcement learning algorithm by testing it with real spectrum occupancy data collected in the GSM, ISM, and DECT bands. Our results show that learning performance is highly correlated with the level of PU activity and the amount of structure in the use of spectrum. For low levels of PU activity and/or high complexity in its utilization of channels, reinforcement learning performs no better than simple random channel selection. We suggest that Lempel-Ziv complexity might be one of the features considered by a cognitive radio when deciding which channels to opportunistically explore.

We design an efficient sensing order selection strategy for a distributed cognitive radio (CR) network, where two or more autonomous CRs sense the channels sequentially (in some sensing order) for spectrum opportunities. We are particularly interested in the case where CRs with false alarms autonomously select the sensing orders in which they visit channels, without coordination from a centralized entity. We propose an adaptive persistent sensing order selection strategy and show that this strategy converges and reduces the likelihood of collisions among the autonomous CRs as compared to a random selection of sensing orders. We also show that, when the number of CRs is less than or equal to the number of channels, the proposed strategy enables the CRs to converge to collision-free channel sensing orders. The proposed adaptive persistent strategy also reduces the expected time of arrival at collision-free sensing orders as compared to the randomize after every collision strategy, in which a CR, upon colliding, randomly selects a new sensing order.

This paper considers spatial spectrum sharing-based carrier aggregation (SSS-CA) from a game theoretic perspective. In SSS-CA, a network operator can not only transmit on its own licensed spectrum but it can also access and aggregate the licensed spectrum of other operators on payment of a certain price. The difference between operators and the aggregators in each licensed spectrum makes this network heterogeneous. We first model the pairing problem between potential operators as a pairing game and then derive the condition for which both operators are incentivized to form an SSS-CA pair. We then introduce the power control game to derive the optimal transmit power of each spectrum aggregator. Finally, we consider the pricing optimization problem by forming a pricing adjustment game. We observe that these three problems are linked by the price function of the operators and hence can be jointly optimized by using a hierarchical game theoretic framework. We derive the Stackelberg equilibrium for the pricing and power joint optimization problem and present the numerical results to compare the performance improvement brought by our proposed joint optimization method.

This mini workshop is organized to provide an interactive forum for the introduction of a set of five new curriculum modules developed under IEEE's Real World Engineering Projects (RWEP) program. The modules, which are representative of a larger collection of curriculum modules available to the public via an open-access RWEP web portal, are designed for use in the first-year engineering and computer science classroom, and are hands-on, team-based projects that emphasize the societal impact of the work that engineers do. After a brief introduction to the RWEP program and the five showcased curriculum modules, the authors of the modules will work one-on-one with the audience providing tutorials on the laboratory activities associated with their modules in a highly interactive, simultaneous mode.

A current trend in spectrum regulation is to incorporate spectrum sharing through the design of spectrum access rules that support Dynamic Spectrum Access (DSA). This paper develops a decision- theoretic framework for regulators to assess the impacts of different spectrum access rules on both primary and secondary operators. We analyze access rules based on sensing and exclusion areas, which in practice can be enforced through geolocation databases. Our results show that receiver-only sensing provides insufficient protection for primary and co-existing secondary users and overall low social welfare. On the other hand, using combining sensing information of the transmitter and receiver of a communication link provides dramatic increases in system performance. The performance of using these link end points is relatively close to that of using many cooperative sensing nodes associated to the same access point and large link exclusion areas. These results are useful to regulators and network developers in understanding in developing rules for future DSA regulation.

In this paper, we examine the problem of channel assignment in multi-hop wireless networks in which each node is equipped with multiple transceivers. We summarize various approaches to transceiver channel assignment. Most approaches in the literature seek to assign a channel to each available transceiver. In contrast, we seek to achieve a baseline connected topology that conserves resources, keeping some fraction of the transceivers inactive so as to dynamically react to changing traffic stimuli. We call our scheme resource-minimized channel assignment (RMCA). We show that RMCA achieves basic network connectivity and low interference, while using fewer resources compared to other protocols, and performs well under varied node density and number of transceivers per node. Finally, we evaluate several channel assignment schemes through simulation using a set of widely-accepted networking and graph-theoretic metrics.

In this paper, we discuss the use of spectrum sensing as a contingency for future wireless microphones (WM). We represent post-switchover operation conditions as a framework where WM sense for signals of digital television (DTV) and TV band devices (TVBD). Using this model, we propose two semi-blind channel monitoring mechanisms that ensure quality of service through fast and fine sensing. To implement such mechanisms, we analyze the pros and cons of five potential detection methods with respect to computational complexity, amount of prior information required for detection, classification ability, and performance under AWGN and frequency-selective fading. Our analysis suggests that methods based on the cyclic prefix (CP) are promising for fast sensing as long as portable TVBD can be detected. With respect to fine sensing, we suggest methods that exploit scattered pilots to improve classification ability and robustness against noise uncertainty, CP length variations, and multipath. The proposed mechanisms incur no performance loss in comparison to fast sensing methods in isolation.

Simulation is still the most widely adopted performance evaluation technique in mobile ad hoc network research, in spite of a growing number of questions about the fidelity of this technique. Implementation-based testing and evaluation of wireless networks tends to produce believable results, but the technique sometimes suffers from poor repeatability, high implementation cost, and complex experimental logistics. The topic of this paper is software tools to enable implementation-based experimental research on wireless networks. We review some of the existing tools and propose the Flexible Internetwork Stack (FINS) framework, our open-source solution for network protocol implementation, integration, and testing. FINS aims to provide researchers with monitoring, logging, and reconfiguring utilities similar to the ones provided by simulation environments or emulation testbeds.1

Although establishing cooperation in a wireless network is a dynamic process, most game theoretic coalition formation models proposed in the literature are static. We analyze a dynamic coalition formation game based on a Markovian model for the spectrum sharing problem in an interference channel. Our model is dynamic in the sense that distributed transmitter/receiver pairs, with partial channel knowledge, reach stable coalition structures (CSs) through a time-evolving sequence of steps. Depending on an interference environment, we show that the game process either converges to the absorbing state of the grand coalition or to the absorbing state of internal and external stability. We also show that, due to myopic links, it is possible that the core of the game is nonempty, but links cannot form the grand coalition to utilize the core rate allocations. We then formulate a condition for the formation of the stable grand coalition. Using simulation we show that coalition formation yields significant gains in terms of average rates per link for different network sizes. We also show average maximum coalition sizes for different distances between the transmitters and their own receivers. Finally, we analyze the mean and variance of the time for the game to reach the stable coalition structures.

Cognitive radios have been touted as a solution to communicating in a Dynamic Spectrum Access environment. This paper examines how cognitive radios initially find one another among the expanse of ever-changing open spectrum, termed the rendezvous problem. Specifically, it addresses the problem of rendezvous under varying levels of system capabilities, spectrum policies, and environmental conditions. The focus is on rendezvous when there are are no control channels or centralized controllers, which we term the blind rendezvous problem. Under these conditions, a sequence-based and modular clock blind rendezvous algorithms are proposed, and it is shown that the performance of these algorithms compares favorably to that of a random blind rendezvous algorithm. Specifically, the sequence-based algorithm provides a bounded Time To Rendezvous (TTR) and the ability to prioritize channels where rendezvous is more likely to occur; the modular clock algorithm reduces the expected TTR, requires little precoordination among radios attempting to rendezvous, and is robust to radios sensing different sets of available channels.

This paper studies downlink resource reuse schemes for cellular networks with coexisting femtocells and macrocells. We examine two reuse schemes, termed split reuse and shared reuse. In this paper, we develop an analytical model of resource allocation based on random graphs. In our model, arbitrarily chosen communication links interfere with each other with a certain probability, which depends upon whether the links belong to femtocell or macrocell users. Using this model, we establish asymptotic bounds on the minimum number of resource blocks required to make an interference-free resource assignment for all the users in the network for large numbers of users. We assess these bounds using a simple greedy resource allocation algorithm to demonstrate that the bounds are reasonable in finite networks of plausible size. By applying the bounds, we establish the expected impact of femtocell networks on macrocell resource allocation under a wide variety of interference scenarios. Further, we assess the efficiency loss associated with split reuse, as an aid to determining whether resource allocators should use the simpler split reuse scheme or attempt to tackle the complexity and overhead associated with shared reuse.

Reputation management systems have been proposed as a cooperation enforcement solution in ad hoc networks. Typically, the functions of reputation management (evaluation, detection, and reaction) are carried out homogeneously across time and space. However, the dynamic nature of ad hoc networks causes node behavior to vary both spatially and temporally due to changes in local and network-wide conditions. When reputation management functions do not adapt to such changes, their effectiveness, measured in terms of accuracy (correct identification of node behavior) and promptness (timely identification of node misbehavior), may be compromised. We propose an adaptive reputation management system that realizes that changes in node behavior may be driven by changes in network conditions and that accommodates such changes by adapting its operating parameters. We introduce a time-slotted approach to allow the evaluation function to quickly and accurately capture changes in node behavior. We show how the duration of an evaluation slot can adapt according to the network's activity to enhance the system accuracy and promptness. We then show how the detection function can utilize a Sequential Probability Ratio Test (SPRT) to distinguish between cooperative and misbehaving neighbors. The SPRT adapts to changes in neighbors' behavior that are a by-product of changing network conditions, by using the node's own behavior as a benchmark. We compare our proposed solution to a nonadaptive system, showing the ability of our system to achieve high accuracy and promptness in dynamic environments. To the best of our knowledge, this is the first work to explore the adaptation of the reputation management functions to changes in network conditions.

In a cognitive network, autonomous and adaptive radios select their operating parameters to achieve individual and network-wide goals. The effectiveness of these adaptations depends on the amount of knowledge about the state of the network that is available to the radios. We examine the price of ignorance in topology control in a cognitive network with power- and spectral-efficiency objectives. We propose distributed algorithms that, if radios possess global knowledge, minimize both the maximum transmit power and the spectral footprint of the network. We show that while local (as opposed to global) knowledge has little effect on the maximum transmission power used by the network, it has a significant effect on the spectral performance. Furthermore, we show that due to the high cost of maintaining network knowledge for highly dynamic networks, the cost/performance tradeoff makes it advantageous for radios to operate under some degree of local knowledge, rather than global knowledge.We also propose distributed algorithms for power and frequency adaptations as radios join or leave the network, and assess how partial knowledge impacts the performance of these adaptations.

Multi-channel operation in an ad hoc network can improve robustness and reliability by efficiently managing interference and reducing contention. In this paper, we model four dynamic channel assignment techniques under the same set of assumptions, comparing the efficiency of their power and channel allocations. As the number of channels increases, the differences in the performance of the four techniques become more pronounced. Among the techniques studied, the conflict graph-based technique achieves the highest number of feasible links and the lowest average power consumption.

In this paper, we investigate stochastic multichannel load balancing in a distributed cognitive network coexisting with primary users. In particular, we propose a probabilistic technique for traffic distribution among a set of data channels by incorporating statistical information of primary users' activities in different channels into the selection process without centralized control. Moreover, the proposed scheme is enabled by a multi-channel binary exponential backoff mechanism to further facilitate contention resolution in a multi-channel environment. It is shown through simulations that the proposed MAC layer enhancement outperforms well-known multi-channel MAC protocols both in terms of aggregate end-to-end throughput and average frame end-to-end delay. Furthermore, its performance is also compared to two heuristic channel selection techniques in a multi-channel cognitive network, coexisting with incumbents.

More than a dozen Wireless @ Virginia Tech faculty are working to address the broad research agenda of cognitive radio and cognitive networks. Our core research team spans the protocol stack from radio and reconfigurable hardware to communications theory to the networking layer. Our work includes new analysis methods and the development of new software architectures and applications, in addition to work on the core concepts and architectures underlying cognitive radios and cognitive networks. This paper describes these contributions and points towards critical future work that remains to fulfill the promise of cognitive radio. We briefly describe the history of work on cognitive radios and networks at Virginia Tech and then discuss our contributions to the core cognitive processing underlying these systems, focusing on our cognitive engine. We also describe developments that support the cognitive engine and advances in radio technology that provide the flexibility desired in a cognitive radio node. We consider securing and verifying cognitive systems and examine the challenges of expanding the cognitive paradigm up the protocol stack to optimize end-to-end network performance. Lastly, we consider the analysis of cognitive systems using game theory and the application of cognitive techniques to problems in dynamic spectrum sharing and control of multiple-input multiple-output radios.

Recent studies on spectrum usage reveal poor utilization, both spatially and temporally. Opportunistic use of licensed spectrum while limiting interference to primary users can enhance spectrum reuse and provide orders of magnitude improvement in available channel capacity. This calls for spectrum sharing protocols that are dynamic, flexible, and efficient, in addition to being fair to end users. We employ cooperative game theory to address the opportunistic spectrum access problem. Specifically, we develop a game-theoretic model to analyze a scenario in which nodes in a wireless network seek to agree on a fair and efficient allocation of spectrum. First, we show that in high interference environments, the utility space of the game is non-convex, making certain optimal allocations unachievable with pure strategies. To mitigate this, we show that as the number of channels available increases, the utility space approaches convexity, thereby making optimal allocations achievable with pure strategies. Second, by comparing and analyzing three bargaining solutions, we show that the Nash bargaining solution achieves the best tradeoff between fairness and efficiency, using a small number of channels. Finally, we develop a distributed algorithm for spectrum sharing that is general enough to accomodate non-zero disagreement points, and show that it achieves allocations reasonably close to the Nash bargaining solution.

The paper proposes a method for optimization in wireless networks that incorporates the nonconvex dynamics of the wireless medium that occur when the objective is a function of signal to interference and noise ratio. We present a case study to show the application of the model to continuous optimization problems. We propose a branch and bound heuristic, based on the Relaxation Linearization Technique (RLT), for approximating the solution of continuous, nonconvex optimization problems. Finally, we present results that show that the branch and bound heuristic converges to values that outperform time constrained integer approximations as well as nonlinear optimization using random starting points.

Ad hoc networks employ a decentralized unstructured networking model that relies on node cooperation for key network functionalities such as routing and medium access. In this paper, we develop and evaluate a model based on the sequential probability ratio test to characterize how nodes can differentiate between routes that include misbehaving nodes (infected routes) and routes that do not. An advantage of the model is that the number of observations required to evaluate a route need not be determined in advance.

There is a need for new spectrum access protocols that are opportunistic, flexible and efficient, yet fair. Game theory provides a framework for analyzing spectrum access, a problem that involves complex distributed decisions by independent spectrum users. We develop a cooperative game theory model to analyze a scenario where nodes in a multi-hop wireless network need to agree on a fair allocation of spectrum. We show that in high interference environments, the utility space of the game is non-convex, which may make some optimal allocations unachievable with pure strategies. However, we show that as the number of channels available increases, the utility space becomes close to convex and thus optimal allocations become achievable with pure strategies. We propose the use of the Nash Bargaining Solution and show that it achieves a good compromise between fairness and efficiency, using a small number of channels. Finally, we propose a distributed algorithm for spectrum sharing and show that it achieves allocations reasonably close to the Nash Bargaining Solution.

The idea of the cognitive network, drawn from previous research in cognitive radios and cross-layer design, is an attempt to address issues of complexity and scalability by allowing the network to self-adapt and reach end-to-end goals. This paper identifies three critical design decisions that must be considered when creating a cognitive network: the behavior of the decision-making elements, amount of computational state available to these elements, and the degree of control they have. To measure the effect of these design tradeoffs, we develop and define a metric called the "price of a feature." This metric is used to quantify the effect of these properties on a cognitive network with the objective of maximizing the lifetime of a multicast flow in a wireless environment. For this cognitive network, we show that the price of selfish behavior and partial control are more significant than the price of ignorance.

Wireless topology control is the process of structuring the connectivity between network nodes to achieve some network-wide goal. This paper presents a cognitive network approach to achieving the objectives of power and spectrum management. We cast the problem as a two phased non-cooperative game and use the properties of potential game theory to ensure the existence of, and convergence to, a desirable Nash equilibrium. Although this is a multi-objective optimization and the spectrum management problem is NP-hard, this selfish cognitive network constructs a topology that minimizes the maximum transmission power while simultaneously using, on average, less than 12% extra spectrum, as compared to the ideal solution.

In self-organizing networks, nodes are expected to perform services (such as forwarding packets) for one another. The design of incentive mechanisms that lead nodes to cooperate towards a network-wide goal is crucial for the deployment of such networks in commercial environments. In this paper we employ game theory to analyze a behavior-based incentive scheme for node cooperation in packet forwarding. Our analysis includes an evaluation of the effectiveness of the scheme in achieving an optimal solution, as well as stability considerations. One of the contributions of this work is the modeling of imperfect monitoring, recognizing that in practice nodes do not have perfect information regarding their neighbors' actions on which to base their decisions of whether to cooperate.

Energy and spectrum constrained wireless ad hoc networks must employ efficient forwarding schemes to conserve resources. The incorporation of directional antennas allows for focusing transmitted energy toward a specific node, or increasing reception gain in a specific direction. This provides not only a means to conserve power, but also allows for spatial multiplexing by reducing inter-node interference. In many cases of interest, it is likely that only a subset of nodes in a network will be equipped with directional transmission/reception capabilities. The existing literature on the impact of directional antennas on the lifetime of ad hoc networks does not consider node heterogeneity or model directional listening. In this paper, we develop a Mixed Integer Linear Program to model directional reception. The program's objective is to maximize network lifetime in multicast applications. We consider cases where some (but not all) network nodes are equipped with directional antennas. Our results illustrate the impact of node density and proportion of nodes with directional capabilities on network lifetime.

For ad hoc networks to realize their potential in commercial deployments, it is important that they incorporate adequate security measures. Selfish behavior of autonomous network nodes could greatly disrupt network operation. Such behavior should be discouraged, detected, and isolated. In this paper, we propose a reputation-based mechanism to detect and isolate selfish nodes in an ad hoc network. The proposed mechanism allows a node to autonomously evaluate the "reputation" of its neighbors based on the completion of the requested service. The underlying principle is that when a node forwards a packet through one of its neighbors, it holds that neighbor responsible for the correct delivery of the packet to the destination. Our mechanism is efficient and immune to node collusion since, unlike most contemporary mechanisms for reputation-based trust, it does not depend on exchanging reputation information among nodes. We also explore various reputation functions and report on their effectiveness in isolating selfish nodes and reducing false positives. Our simulation results demonstrate that the choice of the reputation function greatly impacts performance and that the proposed mechanism, with a carefully selected function, is successful in isolating selfish nodes while maintaining false positives at a reasonably low level.

In this paper, we present a practical framework that can be used as a basis for dimensioning 3G systems and beyond that support heterogeneous traffic (voice and multirate data). We start by performing multiplexing simulations as a means to capture the temporal randomness for different traffic profiles, prior to using static system simulation. We present results of system simulation scenarios that demonstrate the importance of traffic characterization in capacity improvement. We illustrate the applicability of the simulation methods proposed here through a capacity study of a UMTS system. We confirm the results of the simplest scenario analytically and use cross-validation between two independent simulation tools for the more complex scenarios.

Most of the current research in wireless sensor networks (WSNs) is constraint driven and focuses on optimizing the use of limited resources (e.g., power) at each sensor. While such constraints are important, there is a energy for more general performance metrics to assess the effectiveness of WSNs. There is also a need for a unified formal model that would enable comparison of different types of WSNs and provide a framework for WSN operations. We propose a new service-centric model that focuses on services provided by a WSN and views a WSN as a service provider. A WSN is modeled at different levels of abstraction. For each level, a set of services and a set of metrics are defined. Services and their interfaces are defined in a formal way to facilitate automatic composition of services, and enable interoperability and multitasking of WSNs at the different levels. A two-way mapping between two neighboring levels is then defined as a decomposition (from higher to lower level) and composition (from lower to higher level). A composite mapping between metrics at different levels connects high-level, mission-oriented metrics and low-level, capability-oriented metrics. The service-centric model consists of mission, network, region, sensor, and capability layers. Each layer has associated semantics that use lower level components as syntactic units (except for the capability layer). Within each layer there are four planes or functionality sets; communication, management, application, and generational learning. The combination of layers and planes enables a service-based visualization paradigm that can provide better understanding of the WSN. The service-centric model provides a holistic approach to measuring and presenting WSNs effectiveness. In addition, it presents a general and flexible framework in which various more specific WSN models can be represented and evaluated.

Catastrophic failures of power systems are phenomena which occur with some regularity throughout the world. It is recognized that these cannot be prevented, although with the use of newer developments in power engineering, in communication systems, and in computer engineering it would be possible to reduce their frequency and their impact on society. Analyses of many blackouts point to some salient features which are common to most such events: power systems under stress because of high load levels or outages of important facilities, some initiating event-usually a fault, often followed by cascading effects due to unwanted operation of some protection systems. In particular, the role of hidden failures (HFs) in protection systems in propagating power system disturbances has become clearer with some of the recent research reported in the literature. This paper explores further the issue of HFs of protection systems and possible countermeasures. Regarding the countermeasures, adapting the protection systems so that they would change their operational logic from OR to a VOTING protocol has been discussed in the literature, and is well within the capability of present technology. Other hardware solutions, such as "Hidden Failure Monitoring and Protection Systems," have also been discussed in the literature. Most of these countermeasures will require intensive use of communication networks. Communication infrastructure will be utilized for real-time data transfer, as well as for slower speed data gathering tasks related to the condition of the power system. In this paper, we concentrate on the communication facilities and their applications for providing countermeasures against catastrophic failures of power systems.

The management of mobile ad hoc networks (MANETs) is a challenging task. Previous work on this topic has focused largely on the monitoring and data collection aspects of network management; literature on the provisioning of devices and protocol support for MANET configuration is scarce. We present a solution suite to extend policy-based management, for the first time, to MANETs. The solution suite consists of four schemes - k-hop clustering, dynamic service redundancy (DynaSeR), policy negotiation, and automated service discovery - each addressing different challenges in managing ad hoc networks. We propose extensions to the standard common open policy service (COPS) protocol for use in MANETs, and evaluate the system performance using simulations.

Ing-Ray Chen, Luiz DaSilva, Scott Midkiff; Editorial: Mobile and Pervasive Computing, The Computer Journal, Volume 47, Issue 4, 1 January 2004, Pages 404, https

The paper compares VoATM and VoIP in terms of their suitability for carrying voice traffic over DSL. ATM is currently the preferred protocol due to its built-in QoS mechanisms. Through simulations, we show that IP QoS mechanisms can also be used to achieve comparable performance for voice traffic over DSL. Our performance metrics are the end-to-end delay of voice packets across the DSL access network and the bandwidth requirements of a voice call. We also propose an implicit signaling mechanism to provide admission control for individual voice calls over DSL. We implement a simulation model that uses our mechanism and perform simulations to verify its effectiveness We conclude that by incorporating appropriate QoS mechanisms and implicit signaling, it is possible to achieve performance for VoIP comparable to that provided by ATM. In this case, the ubiquity of IP makes it a very attractive candidate for future deployments of VoDSL.

Third generation (3G) mobile communication systems are intended to provide enhanced high-speed data, multimedia, and voice services to mobile users. Beamforming techniques have been proposed to increase the spectral efficiency of the wireless channel. In a commercially deployed 3G system, it is likely that these techniques will coexist with resource management. Resource management in a wireless system should take care of channel impairments and non-ideal antenna patterns. Mobile users moving from one beam to another give rise to resource reallocation issues. We propose a resource allocation and management scheme tailored to systems with smart antennas supporting heterogeneous users. The algorithm works by comparing the received power in each beam. Elasticity of user requirements for data services is exploited to provide adaptive QoS to the users, thereby preventing dropping of a call due to user mobility. Simulation results showing the channel and MAI effects on system performance are presented. The throughput advantage obtained using the management scheme is compared to a system without resource management. It is also seen that the throughput of the system increases for a user population exhibiting higher elasticity. In addition, the algorithm can be modified to handle call hand-off as well.

We discuss theoretical and practical aspects related to the dimensioning of hybrid traffic (voice and data) for mobile wireless networks such as the third-generation (3G) systems. The major aspect looked at is the user profile description, with characterization of the main metrics and models that describe the user's voice and data demand. The user profile leads to discussion of the asymmetric nature of the subscriber, which has direct implications on how the network links are designed in order to provide balanced spectrum distribution between downlink and uplink. Next we present a practical approach to explore the dynamics of user geographical distribution in creating multi-service, multi-class traffic layers that serve as input for network traffic simulation algorithms.

Quality of service (QoS) mechanisms are needed in integrated networks so that the performance requirements of heterogeneous applications are met. We outline a framework for predicting end-to-end QoS at the application layer based on mapping of QoS guarantees across layers in the protocol stack and concatenation of guarantees across multiple dissimilar sub-networks. Mapping is needed to translate QoS guarantees provided in the lower layers into their effects on upper-layer performance indicators. We illustrate the process with some preliminary results for QoS mapping due to segmentation of packets; we conduct a worst-case analysis, generating a closed-form mapping of delay and losses between two layers. Future extensions of this work will take into account flow aggregation processes and the combination of quantitative and qualitative guarantees.

The objective of this paper is to characterize and analyze the performance of TCP/IP when used over point-to-point protocol (PPP) and asynchronous transfer mode (ATM) on asymmetric links. This will allow us to gain insight into this new protocol architecture used in the asymmetric digital subscriber line (ADSL) access network and to investigate the related quality of service (QoS) issues. Using simulation, we verified the effects of asymmetry on the performance of TCP and additional throughput degradation caused by segmentation at the ATM layer. This study was done for unidirectional and bidirectional data transfer using different traffic mixes. We also quantified the improvement in the downstream throughput obtained by delaying the TCP acknowledgements and using TCP/IP header compression. Although these techniques are effective for unidirectional TCP/IP data transfer over asymmetric links, they do not prove as effective for bidirectional traffic, and the problem further exacerbates when ATM enters the scenario. Hence, there is a need for additional enhancements in such scenarios. Further, we modified the existing protocol stack model to implement PPP encapsulation over the ATM adaptation layer (AAL5). We characterized and analyzed the effect of the additional PPP overhead on system performance in terms of throughput degradation and additional delay. We also evaluated the use of TCP/IP header compression for improving performance in presence of PPP encapsulation.

We analyze and characterize the performance of TCP/IP over the point-to-point protocol (PPP) over ATM using asymmetric links in the presence of multiple clients competing for bandwidth. This is part of an ongoing research project that studies techniques to provide end-to-end quality of service to flows when ADSL is used in the access network. The results presented illustrate the decrease in fairness and the link utilization that occurs in the presence of both bi-directional and unidirectional flows in an ADSL environment.

Experiments in asynchronous transfer mode (ATM) wide area network (WAN) environments are discussed in this work. The lessons learned through practical experiences with ATM systems and how these can be applied to improving performance are presented. Factors ranging from the transport protocol to the equipment used and the network conditions are considered.

A replacement technique for lost speech packets is presented. This technique is based on the classification of the packets into four distinct classes: background noise, voiced speech, fricatives, and other. Different encoding schemes and lost packet replacement techniques are used for each class. Results of subjective tests indicate that giving preferential delivery treatment to packets based on class can be used to improve subjective quality. The replacement strategy renders the reconstructed signal indistinguishable from the original utterance for packet loss rates up to 47% for background noise packets, 8% for fricative packets, and 4% for other packets. For voiced packets, the replacement is distinguishable from the original signal even for packet loss rates lower than 5%, but significant improvement may be possible by reducing the memory associated with the voiced speech coding process.>

The authors discuss the control of short-term congestion, which is referred to as overload, in integrated packet networks (IPNs) containing a mix of data, speech, and possibly other types of signals. A system model is proposed that assigns a delivery priority to each packet (speech or otherwise) at the transmitter and discards speech packets according to delivery priority at any point in the network in response to overload. This model attempts to minimize per-packet processing at networks nodes. The research described is guided by two principles for IPN design: minimal per-packet processing and flexibility due to signal structure. The quality of the received speech is maintained by classifying speech segments according to their structure and coding them in a way that ensures ease of lost-packet regeneration at the receiver. The results of an experiment are reported that confirmed the general validity of this model from the standpoint of transmitter and receiver processing and subjective quality.>