Continual configuration tuning in cellular radio access networks (RANs) is critical for maintaining performance, reliability, energy efficiency, and user experience. However, this task remains largely manual in practice. Automating it needs to confront high-dimensional configuration spaces, sparse and biased exploration, strong parameter interactions, and substantial environmental confounding. Existing RAN configuration tuning approaches have limited effectiveness in addressing these challenges. In this paper, we present CausalTune, a novel causal learning framework for automated RAN configuration optimization based on observational telemetry. CausalTune disentangles configuration effects from environmental and operational confounders, generalizes to sparse and previously unseen parameter settings, and captures high-impact multi-parameter interactions. Our key insight is that effective causal inference in operational RANs requires reshaping raw telemetry to expose confounding and learning environment-invariant mechanisms. Guided by this insight, CausalTune employs a multistage pipeline that integrates distributional representation learning, causal modeling, and interaction-aware recommendation. We evaluate CausalTune using 10 months of RAN measurement data from 1M commercial cells of a major cellular operator. Our comparison to state-of-the-art baselines shows that CausalTune achieves up to 3X KPI improvement on the held-out dataset. In terms of causal modeling quality, CausalTune achieves up to 12X lower KPI reconstruction error; on recommended configuration safety, it achieves 4X higher agreement with expert engineers while significantly reducing off-target recommendations. These findings demonstrate the potential of causal learning to enable reliable, scalable, and interpretable RAN configuration tuning.

The accuracy of digital twin models hinges on the prompt collection of information from the vehicular environment. However, the high mobility of vehicles and the dynamically changing network environment pose significant challenges. Dynamic twin model migration can reduce the Age of Information (AoI) by bringing twin models closer to their vehicles. Existing works rarely consider the inherent differences in optimization cycles between digital twin model migration and data upload, which potentially leads to suboptimal cost efficiency and information freshness. Specifically, real-time vehicular data must be rapidly uploaded to edge servers to ensure the accuracy and timeliness of digital twin models, while frequent migration of twin models over short periods incurs substantial costs. Therefore, we propose a dual-timescale bilevel learning approach, where the upper-layer learning optimizes twin model migration decisions on a long timescale to achieve forward-looking model migration, and the lower-layer learning optimizes data upload and resource allocation decisions on a short timescale to ensure the accuracy and timeliness of digital twin models. Then, we design a multi-agent selective parameter sharing approach based on spatiotemporal dependency correlations to accelerate model convergence and reduce communication costs among agents. Furthermore, through a rigorous theoretical analysis, we prove the convergence of the dual-timescale bilevel learning with broad applicability. Finally, numerical results demonstrate that our algorithm outperforms comparison algorithms in terms of convergence, AoI, and system cost, achieving at least a 21.30% reduction in AoI and a 14.58% reduction in system cost compared to the benchmark algorithms.

The next generation of mobile networks are embracing disaggregation, reflected by the industry trend towards Open RAN. Private 5G networks are viewed as particularly suitable contenders for adopting Open RAN, owing to their setting, high degree of control, and opportunity for innovation. Motivated by this, we have recently deployed the first of its kind campus-wide, O-RAN-compliant private 5G testbed across the central campus of the University of Edinburgh. We first present the rationale behind our testbed along with an overview of its make-up. Then, we outline our plan to showcase the coverage, flexibility, and the operational view of the testbed from both network side and user perspectives.

Foundation model training is increasingly centralized in large cloud data centers because it demands immense compute and memory resources. Training over decentralized edge devices could democratize this ecosystem by harnessing otherwise idle compute, but prior edge-training systems fall short: they scale poorly with model size and device count, exceed per-device memory budgets, incur prohibitive collective communication, and are fragile to heterogeneous and dynamic device availability. We present Cleave, a parameter-server-centric framework that makes tensor-parallel training practical at the edge. Cleave introduces selective hybrid tensor parallelism, which finely shards GEMM-dominated training operations into memory-feasible sub-tasks while avoiding peer-to-peer collectives that become bottlenecks on asymmetric edge links. A cost model guides device selection and shard placement to mitigate stragglers and rapidly adapt to churn. Across OPT and Llama2 models, Cleave matches cloud GPU training efficiency while scaling to thousands of devices. It supports up to 8× more devices than prior edge approaches, reduces per-batch training time by up to 10×, and achieves 100× faster recovery from device failures.

Large constellations of low-earth orbit satellites enable frequent high-resolution earth imaging for numerous geospatial applications. They generate large volumes of data in space, hundreds of Terabytes per day, which much be transported to Earth through constrained intermittent connections to ground stations. The large volumes lead to large day-level delay in data download and exorbitant cloud storage costs. We propose DeepSpace, a new deep learning-based super-resolution approach that compresses satellite imagery by over two orders of magnitude, while preserving image quality using a tailored mixture of experts (MoE) super-resolution framework. DeepSpace reduces the network bandwidth requirements for space-Earth transfer, and can compress images for cloud storage. DeepSpace achieves such gains with the limited computational power available on small LEO satellites. We extensively evaluate DeepSpace against a wide range of state-of-the-art baselines considering multiple satellite image datasets and demonstrate the above mentioned benefits. We further demonstrate the effectiveness of DeepSpace through several distinct downstream applications (wildfire detection, land use and cropland classification, and fine-grained plastic detection in oceans).

Fourier features based positional encoding (PE) is commonly used in machine learning tasks that involve learning high-frequency features from low-dimensional inputs, such as 3D view synthesis and time series regression with neural tangent kernels. Despite their effectiveness, existing PEs require manual, empirical adjustment of crucial hyperparameters, specifically the Fourier features, tailored to each unique task. Further, PEs face challenges in efficiently learning high-frequency functions, particularly in tasks with limited data. In this paper, we introduce sinusoidal PE (SPE), designed to efficiently learn adaptive frequency features closely aligned with the true underlying function. Our experiments demonstrate that SPE, without hyperparameter tuning, consistently achieves enhanced fidelity and faster training across various tasks, including 3D view synthesis, Text-to-Speech generation, and 1D regression. SPE is implemented as a direct replacement for existing PEs. Its plug-and-play nature lets numerous tasks easily adopt and benefit from SPE.

In the ongoing evolution of mobile networks, a key emerging focus is on "openness": disaggregation to enable multiple vendors. The obvious example of this is OpenRAN, a movement to open the traditionally-single-vendor RAN, to multiple vendors. However, despite the clear benefits that this approach has brought to the RAN, there has been little interest in applying this philosophy to other parts of the mobile network such as the mobile core. We argue that applying the philosophy of openness to the mobile core will result in significant ecosystem improvements to both mobile network operators and governments. In addition, we identify the key challenges for an "open core", formulate a set of criteria for evaluating openness of core designs, and assess current alternatives with respect those criteria.

The emerging nature of low-earth-orbiting satellite constellations has brought forth an era of global connectivity. Star-link has the potential to offer a truly global, high-performant service. An obvious question arising from this is whether Starlink can replace existing terrestrial services. In this work we describe the key findings from a detailed study of the performance of Starlink, investigating both its potential and shortcomings to act as a "global ISP".

The Open RAN architecture, with disaggregated and virtualized RAN functions communicating over standardized interfaces, promises a diversified and multi-vendor RAN ecosystem. However, these same features contribute to increased operational complexity, making it highly challenging to troubleshoot RAN related performance issues and failures. Tackling this challenge requires a dependable, explainable anomaly detection method that Open RAN is currently lacking. To address this problem, we introduce SpotLight, a tailored system archtecture with a distributed deep generative modeling based method running across the edge and cloud. SpotLight takes in a diverse, fine grained stream of metrics from the RAN and the platform, to continually detect and localize anomalies. It introduces a novel multi-stage generative model to detect potential anomalies at the edge using a light-weight algorithm, followed by anomaly confirmation and an explain-ability phase at the cloud, that helps identify the minimal set of KPIs that caused the anomaly. We evaluate SpotLight using the metrics collected from an enterprise-scale 5G Open RAN deployment in an indoor office building. Our results show that compared to a range of baseline methods, SpotLight yields significant gains in accuracy (13% higher F1 score), explain-ability (2.3 -- 4X reduction in the number of reported KPIs) and efficiency (4 -- 7X bandwidth reduction).

The Open RAN architecture, featuring disaggregated and virtualized RAN functions communicating over standardized interfaces, promises a diverse, multi-vendor ecosystem. However, these features also increase operational complexity, complicating the troubleshooting of RAN performance issues and failures. Addressing this challenge requires a reliable, explainable anomaly detection method, which Open RAN currently lacks. To address this problem, we have developed SpotLight, a tailored distributed deep learning method running across the edge and cloud. SpotLight continuously detects and localizes anomalies by analyzing a diverse, fine-grained stream of metrics from the RAN and platform. It employs a novel multi-stage generative model to identify potential anomalies at the edge using a lightweight algorithm, followed by anomaly confirmation and an explainability phase in the cloud, which pinpoints the minimal set of KPIs responsible for the anomaly. In this demo, using a carrier-grade indoor Open RAN testbed with configurable anomaly event generation and replay, we highlight (1) the difficulty of troubleshooting problems in Open RAN and (2) accurate, efficient, and explainable online anomaly detection with SpotLight and corresponding visualization in comparison with prior art.

Leveraging the public cloud for the Core Network (CN) remains uncommon amongst mobile network operators despite the economical and operational benefits. In this paper, we present a holistic feasibility analysis for various methods to deploy a cloud-native CN to AWS. Our findings confirm the economical and technical feasibility, and present a suitable deployment method for further adoption of the public cloud.

The Starlink network from SpaceX stands out as the only commercial LEO network with over 2M+ customers and more than 4000 operational satellites. In this paper, we conduct a first-of-its-kind extensive multi-faceted analysis of Starlink performance leveraging several measurement sources. First, based on 19.2M crowdsourced M-Lab speed tests from 34 countries since 2021, we analyze Starlink global performance relative to terrestrial cellular networks. Second, we examine Starlink's ability to support real-time latency and bandwidth-critical applications by analyzing the performance of (i) Zoom conferencing, and (ii) Luna cloud gaming, comparing it to 5G and fiber. Third, we perform measurements from Starlink-enabled RIPE Atlas probes to shed light on the last-mile access and other factors affecting its performance.Finally, we conduct controlled experiments from Starlink dishes in two countries and analyze the impact of globally synchronized "15-second reconfiguration intervals'' of the satellite links that cause substantial latency and throughput variations. Our unique analysis paints the most comprehensive picture of Starlink's global and last-mile performance to date.

Given the central role mobile core plays in supporting mobile network operations, the efficiency, cost-effective dynamic scalability and resilience of the core control plane are paramount. Achieving these goals, however, presents two main challenges: (i) decoupling core network state from processing; (ii) decoupling control plane processing in the core from its interface to the radio access network (RAN). To overcome them, we present CoreKube, a novel message focused and cloud-native mobile core system design, which features truly stateless workers (processing units) that interface with a common database (to hold the core network state) and with the RAN through a frontend. The fully stateless and generic nature of the workers to process any control plane message enables efficient message handling. Orchestration of containerized CoreKube components using Kubernetes, allows leveraging the latter's autoscaling and self-healing properties. We develop 4G and 5G standard-compliant CoreKube implementations, exploiting the agile development methodology enabled by CoreKube's message focused design. Results from our extensive experimental evaluations over the Powder platform relative to prior art show that CoreKube efficiently processes control plane messages, scales dynamically while using minimal compute resources and recovers seamlessly from failures.

Given the central role that the mobile core plays in supporting mobile network operations, the efficiency, cost-effective dynamic scalability and resilience of the core control plane are paramount. Achieving these goals, however, presents two main challenges: (i) decoupling core network state from processing; (ii) decoupling control plane processing in the core from its interface to the radio access network (RAN). To address these challenges, our proposed solution, CoreKube, is based on a novel message-focused and cloud-native design with truly stateless workers that interface with a common database (to hold the core network state) and with the RAN through a frontend in a standard compliant manner. The fully stateless and generic nature of the workers to process any control plane message enables efficient message handling. Orchestration of containerized CoreKube components using Kubernetes allows leveraging the latter's autoscaling and self-healing properties. This demo highlights three key features of CoreKube: dynamic scaling of core in the face of varying control plane traffic while maintaining low user-perceived latency, resilience to failures, and the ability to seamlessly interface with standard-compliant RAN and commodity hardware.

In Mobile Edge Computing (MEC) networks, dynamic service migration can support service continuity and reduce user-perceived delay. However, service migration in MEC networks faces significant challenges due to the uncertainty in future traffic demands, the distributed architecture of MEC networks, high operating costs and the dynamism of network resources. Digital Twins (DT), which achieve the mapping of physical entities to virtual digital models in cyberspace, provide new perspectives for intelligent and efficient service provisioning in MEC networks. In this paper, we propose a traffic prediction-assisted federated deep reinforcement learning scheme to efficiently migrate services and improve the cost efficiency of DT-enabled MEC networks. Specifically, to address the coupled spatio-temporal dependencies of mobile traffic and the imbalance in traffic data, a Multi-order Spatio-temporal information integration-based distributed Traffic Prediction (MSTP) scheme is proposed, which achieves high-accuracy mobile traffic prediction at a low cost. Then, we propose a Federated Cooperative cost-efficient Service Migration (FCSM) algorithm that adaptively adjusts service migration strategies in a distributed manner to respond to future traffic demands. Moreover, a theoretical model is developed to analyze the convergence of FCSM and derive the upper bound of the time-average squared gradient norm. Finally, extensive simulations demonstrate that the proposed schemes achieve excellent traffic prediction performance, enhance users’ Quality of Service (QoS), and significantly reduce the system cost of MEC networks.

Drive testing continues to play a key role in mobile network optimization for operators but its high cost is a big concern. Alternative approaches like virtual drive testing (VDT) target device testing in the lab whereas MDT or crowdsourcing based approaches are limited by the incentives users have to participate and contribute measurements. With the aim of augmenting drive testing and significantly reducing its cost, we propose GenDT, a novel deep generative model that synthesizes high-fidelity time series of key radio network key performance indicators (KPIs). The training of GenDT relies on a relatively small amount of real-world measurement data along with corresponding and easily accessible network and environment context data. Through this, GenDT learns the relationship between context and radio network KPIs as they vary over time, and therefore trained GenDT model can subsequently be relied on to generate time series for different KPIs for new drive test routes (trajectories) without having to collect field measurements. GenDT represents an initial attempt at enabling efficient drive testing via generative modeling. Evaluations with real-world mobile network drive testing measurement datasets from two countries demonstrate that GenDT can synthesize significantly more dependable data than a range of baselines. We further show that GenDT has the potential to significantly reduce the drive testing related measurement effort, and that GenDT-generated data yields similar results to that with real data in the context of two downstream use cases - QoE prediction and handover analysis.

We propose XRC, an explicit rate control algorithm that overcomes the poor performance of commonly used TCP variants in cellular networks. XRC exploits explicit feedback from the radio access network that is aware of the physical, network and transport layer information of all UEs as well as resource distribution policies for users with different traffic characteristics. XRC co-exists fairly with other XRC and non-XRC flows at the wireless and non-wireless bottlenecks while it strictly controls queuing delay within a small threshold. We implement XRC in NS-3 and examine its performance across a range of network loads and dynamics. When competing with CUBIC at a wireless bottleneck, XRC achieves a Jain’s fairness index of 99.7% while providing a 3x lower median queuing delay compared to when CUBIC competes with CUBIC in the same setup.

Understanding link-level performance is key to assuring the quality of cloud-based and OTT services, optimal path selection, robust network operations and beyond. However, direct measurement of each link not only incurs high overhead at the Internet-scale but also is infeasible due to lack of access to network measurement information beyond AS boundaries and functional limitations at relay nodes. Although network tomography is well suited, existing approaches are insufficient due to their unrealistic assumptions with respect to stability, controllability, and visibility. Motivated by this, we propose PAINT, an online iterative algorithm that estimates and refines link-level performance metrics based on path-level measurement. In PAINT, the link metrics are iteratively estimated by minimizing their least square error (LSE) and calibrated based on the comparison of weight between the estimated shortest paths (SPs) and best-known paths from end-to-end path measurements. The key insight is that when there is inconsistency between these paths, then weights of links on the estimated SP are likely mis-estimated, triggering a further round of estimation to refine the estimated link metrics. Evaluation of PAINT, focusing on link delay estimation, using four different real network topologies and two real-world measurement datasets (including one we collected) shows that relative to existing approaches, it yields up to 3x gain in absolute link delay estimation accuracy and improves decisions dependent on link delay estimation by up to 5x in relative error.

Wireless jammer activity from malicious or malfunctioning devices cause significant disruption to mobile network services and user QoE degradation. In practice, detection of such activity is manually intensive and costly, taking days and weeks after the jammer activation to detect it. We present a novel data-driven jammer detection framework termed JADE that leverages continually collected operator-side cell-level KPIs to automate this process. As part of this framework, we develop two deep learning based semi-supervised anomaly detection methods tailored for the jammer detection use case. JADE features further innovations, including an adaptive thresholding mechanism and transfer learning based training to efficiently scale JADE for operation in real-world mobile networks. Using a real-world 4G RAN dataset from a multinational mobile network operator, we demonstrate the efficacy of proposed jammer detection methods relative to commonly used anomaly detection methods. We also demonstrate the robustness of our proposed methods in accurately detecting jammer activity across multiple frequency bands and diverse types of jammers. We present real-world validation results from applying our methods in the operator’s network for online jammer detection. We also present promising results on pinpointing jammer locations when our methods spot jammer activity in the network along with cell site location data.

Mobile network traffic data offers unprecedented opportunities for innovative studies within and beyond networking. However, progress is hindered by the very limited access that the research community at large has to the real-world mobile network data that is needed to develop and dependably test mobile traffic data-driven solutions. As a contribution to overcome this barrier, we propose CartaGenie, a generator of realistic mobile traffic snapshots at city scale. Taking a deep generative modeling approach and through a tailored conditional generator design, CartaGenie can synthesize high-fidelity and artifact-free spatial traffic snapshots using only contextual information about the target geographical region that is easily found in public repositories. Hence, CartaGenie allows researchers to create their own realistic datasets of spatial traffic from open data about their region of interest. Experiments with real-world mobile traffic measurements collected in multiple metropolitan areas show that CartaGenie can produce dependable network traffic loads for areas where no prior traffic information is available, significantly outperforming a comprehensive set of benchmarks. Moreover, tests with practical case studies demonstrate that the synthetic data generated by CartaGenie is as good as real data in supporting diverse research-oriented mobile traffic data-driven applications.

Service-level mobile traffic data enables research studies and innovative applications with a potential to shape future service-oriented communication systems and beyond. However, real-world datasets reporting measurements at the individual service level are hard to access as such data is deemed commercially sensitive by operators. APPSHOT is a model for generating synthetic high-fidelity city-scale snapshots of service level mobile traffic. It can operate in any geographical region and relies solely on easily available spatial context information such as population density, thus allowing the generation of new and open traffic datasets for the research community. The design of APPSHOT is informed by an original characterization of service-level mobile traffic data. APPSHOT is a novel conditional GAN design instantiated by a convolutional neural network generator and two discriminators. The model features several other innovative mechanisms including multi-channel and overlapping patch based generation to address the unique challenges involved in generating mobile service traffic snapshots. Experiments with ground-truth data collected by a major European operator in multiple metropolitan areas show that APPSHOT can produce realistic network loads at the service level for areas where it has no prior traffic knowledge, and that such data can reliably support service-oriented networking studies.

City-scale spatiotemporal mobile network traffic data can support numerous applications in and beyond networking. However, operators are very reluctant to share their data, which is curbing innovation and research reproducibility. To remedy this status quo, we propose SpectraGAN, a novel deep generative model that, upon training with real-world network traffic measurements, can produce high-fidelity synthetic mobile traffic data for new, arbitrary sized geographical regions over long periods. To this end, the model only requires publicly available context information about the target region, such as population census data. SpectraGAN is an original conditional GAN design with the defining feature of generating spectra of mobile traffic at all locations of the target region based on their contextual features. Evaluations with mobile traffic measurement datasets collected by different operators in 13 cities across two European countries demonstrate that SpectraGAN can synthesize more dependable traffic than a range of representative baselines from the literature. We also show that synthetic data generated with SpectraGAN yield similar results to that with real data when used in applications like radio access network infrastructure power savings and resource allocation, or dynamic population mapping.

RAN energy consumption is a major OPEX source for mobile telecom operators, and 5G is expected to increase these costs by several folds. Moreover, paradigm-shifting aspects of the 5G RAN architecture like RAN disaggregation, virtualization and cloudification introduce new traffic-dependent resource management decisions that make the problem of energy-efficient 5G RAN orchestration harder. To address such a challenge, we present a first comprehensive virtualized RAN (vRAN) system model aligned with 5G RAN specifications, which embeds realistic and dynamic models for computational load and energy consumption costs. We then formulate the vRAN energy consumption optimization as an integer quadratic programming problem, whose NP-hard nature leads us to develop GreenRAN, a novel, computationally efficient and distributed solution that leverages Lagrangian decomposition and simulated annealing. Evaluations with real-world mobile traffic data for a large metropolitan area are another novel aspect of this work, and show that our approach yields energy efficiency gains up to 25% and 42%, over state-of-the-art and baseline traditional RAN approaches, respectively.

Given the wide interest on mobile core systems and their pivotal role in the operations of current and future mobile network services, we focus on the issue of their effective evaluation, considering the radio access network (RAN) emulation methodology. While there exist a number of different RAN emulators, following different paradigms, they are limited in their scalability and flexibility, and moreover there is no one commonly accepted RAN emulator. Motivated by this, we present Nervion, a scalable and flexible RAN emulator for mobile core system evaluation that takes a novel cloud-native approach. Nervion embeds innovations to enable scalability via abstractions and RAN element containerization, and additionally supports an even more scalable control-plane only mode. It also offers ample flexibility in terms of realizing arbitrary RAN emulation scenarios, mapping them to compute clusters, and evaluating diverse core system designs. We develop a prototype implementation of Nervion that supports 4G and 5G standard compliant RAN emulation and integrate it into the Powder platform to benefit the research community. Our experimental evaluations validate its correctness and demonstrate its scalability relative to representative set of existing RAN emulators. We also present multiple case studies using Nervion that highlight its flexibility to support diverse types of mobile core evaluations.

With the mobile networks evolving towards a software-based architecture with 5G, the research community has proposed several alternative core designs to address the issues recognized with the 4G core network architecture. It is notable, however, that these proposals are evaluated in bespoke ways which do not allow evaluate other proposals or even standard-compliant core networks, presenting several limitations in terms of the number of devices and the network load patterns that can be generated. To this end, we present Nervion, a cloud-native RAN emulator for scalable and flexible core network evaluations. Nervion leverages a compute cluster via containerization to emulate a large number of standard-compliant UEs and eNBs/gNBs generating workloads along both the control- and data-plane with a high degree of customization. This demo highlights the features of Nervion via the evaluation of a 5G core network and serves as a guide on how to use the public profile of Nervion on the Powder platform.

Today almost half the world's population does not have Internet access. This is particularly the case in rural and undeserved regions where providing Internet access infrastructure is challenging and expensive. To this end, we present demonstration of WhiteHaul [5], a low-cost hybrid cross-layer aggregation system for TV White Space (TVWS) based backhaul. WhiteHaul features a custom-designed frequency conversion substrate that efficiently handles multiple noncontiguous chunks of TVWS spectrum using multiple low-cost COTS 802.11n/ac cards but with a single antenna. At the software layer, WhiteHaul uses MPTCP as a link-level tunnel abstraction to efficiently aggregate multiple chunks of the TVWS spectrum via a novel uncoupled, cross-layer congestion control algorithm. This demo illustrates the unique features of the WhiteHaul system based on a prototype implementation employing a modified version of MPTCP Linux Kernel and a custom-designed conversion substrate. Using this prototype, we highlight the performance of the WhiteHaul system under various configurations and network conditions.

The recent trend towards cloudifying mobile networks brings more flexibility and shortens deployment times. However, it results in an architecture spanning several independent layers from the bare metal to the service level thus complicating troubleshooting and service assurance. In this work, we experimentally explore whether we can accurately and efficiently identify bottlenecks across the different locations of the network and layers of the cloudified architecture. Our findings confirm the complexity of this task and lead us to promising solutions through the use of Machine Learning.

We address the challenge of backhaul connectivity for rural and developing regions, which is essential for universal fixed/mobile Internet access. To this end, we propose to exploit the TV white space (TVWS) spectrum for its attractive properties: low cost, abundance in under-served regions and favorable propagation characteristics. Specifically, we propose a system called WhiteHaul for the efficient aggregation of the TVWS spectrum tailored for the backhaul use case. At the core of WhiteHaul are two key innovations: (i) a TVWS conversion substrate that can efficiently handle multiple non-contiguous chunks of TVWS spectrum using multiple low cost 802.11n/ac cards but with a single antenna; (ii) novel use of MPTCP as a link-level tunnel abstraction and its use for efficiently aggregating multiple chunks of the TVWS spectrum via a novel uncoupled, cross-layer congestion control algorithm. Through extensive evaluations using a prototype implementation of WhiteHaul, we show that: (a) WhiteHaul can aggregate almost the whole of TV band with 3 interfaces and achieve nearly 600Mbps TCP throughput; (b) the WhiteHaul MPTCP congestion control algorithm provides an order of magnitude improvement over state of the art algorithms for typical TVWS backhaul links. We also present additional measurement and simulation based results to evaluate other aspects of the WhiteHaul design.

We consider the problem of monitoring in the context of emerging and future mobile networks which are shaping up to feature diverse set of services composed of customized chains of virtual network functions (VNFs) realized over (edge) cloud environments. In such a setting, not only is monitoring a critical component for service quality assurance, but it also needs to be efficient, adaptable and flexible. Informed by the experience analyzing state-of-the-art management and orchestration (MANO) platforms and monitoring solutions for softwarized mobile networks, we present our monitoring system design termed PliMon that aims to meet the above requirements by exploiting diverse temporal variability characteristics across different metrics (measurement features) and VNFs, and by grouping such metrics into tiers based on their relative significance. Using an experimental testbed, we verify the hypothesis that different measurement features and VNFs exhibit diversity in their variability and crucially show substantial reduction in monitoring overhead compared to representative monitoring solution from the literature. Additionally, we integrate PliMon with OSM, a well known open source MANO platform, and demonstrate the salient aspects of our approach using the integrated PliMon-OSM system.

This study is a first attempt to experimentally explore the range of performance bottlenecks that 5G mobile networks can experience. To this end, we leverage a wide range of measurements obtained with a prototype testbed that captures the key aspects of a cloudified mobile network. We investigate the relevance of the metrics and a number of approaches to accurately and efficiently identify bottlenecks across the different locations of the network and layers of the system architecture. Our findings validate the complexity of this task in the multi-layered architecture and highlight the need for novel monitoring approaches that intelligently fuse metrics across network layers and functions. In particular, we find that distributed analytics performs reasonably well both in terms of bottleneck identification accuracy and incurred computational and communication overhead.

Knowledge of cell tower locations enables multiple applications including identifying unserved or poorly served regions. We consider the problem of estimating the locations of cell towers using crowdsourced measurements, which is challenging due to the uncontrolled nature of the sample collection process. Using large-scale crowdsourced datasets from OpenCelliD with ground-truth cell tower locations, we find that none of the several commonly used localization algorithms (e.g., Weighted Centroid) nor the state of the art Filtered Weighted Centroid (FWC) approach that filters out less predictive measurements manage to deliver robust localization performance. We propose a novel supervised machine learning based approach termed as Adaptive Algorithm Selection (AAS) that adaptively selects the localization algorithm likely to provide the most accurate localization performance for a given cell and its crowdsourced samples. We show that AAS not only significantly outperforms the state-of-the-art FWC approach, with median error improvement over 65%, but also achieves localization performance within 20% of an idealized Oracle solution. We validate the applicability of AAS in new and different settings (including WLAN AP localization) before presenting case studies in three different African countries that demonstrate the use of AAS based cell tower localization to reliably infer mobile infrastructure in developing countries.

We investigate spatial patterns in mobile service consumption that emerge at national scale. Our investigation focuses on a representative case study, i.e., France, where we find that: (i) the demand for popular mobile services is fairly uniform across the whole country, and only a reduced set of peculiar services (mainly operating system updates and long-lived video streaming) yields geographic diversity; (ii) even for such distinguishing services, the spatial heterogeneity of demands is limited, and a small set of consumption behaviors is sufficient to characterize most of the mobile service usage across the country; (iii) the spatial distribution of these behaviors correlates well with the urbanization level, ultimately suggesting that the adoption of geographically-diverse mobile applications is linked to a dichotomy of cities and rural areas. We derive our results through the analysis of substantial measurement data collected by a major mobile network operator, leveraging an approach rooted in information theory that can be readily applied to other scenarios.

We consider indoor mobile access, a vital use case for current and future mobile networks. For this key use case, we outline a vision that combines a neutral-host-based shared small-cell infrastructure with a common pool of spectrum for dynamic sharing as a way forward to proliferate indoor small-cell deployments and open up the mobile operator ecosystem. Toward this vision, we focus on the challenges pertaining to managing access to shared spectrum [e.g., 3.5-GHz U.S. Citizen Broadband Radio Service (CBRS) spectrum]. We propose Iris, a practical shared spectrum access architecture for indoor neutral-host small-cells. At the core of Iris is a deep reinforcement learning-based dynamic pricing mechanism that efficiently mediates access to shared spectrum for diverse operators in a way that provides incentives for operators and the neutral-host alike. We then present the Iris system architecture that embeds this dynamic pricing mechanism alongside cloud-RAN and RAN slicing design principles in a practical neutral-host design tailored for the indoor small-cell environment. Using a prototype implementation of the Iris system, we present the extensive experimental evaluation results that not only offer insight into the Iris dynamic pricing process and its superiority over alternative approaches but also demonstrate its deployment feasibility.

Mobile phones and innovative data oriented mobile services have the potential to bridge the digital divide in Internet access and have transformative developmental impact. However as things stand currently, economics come in the way for traditional mobile operators to reach out and provide high-end services to under-served regions. We propose a do-it-yourself (DIY) model for deploying mobile networks in such regions that is in the spirit of earlier community cellular networks but aimed at provisioning high-end (4G and beyond) mobile services. Our proposed model captures and incorporates some of the key trends underlying 5G mobile networks and look to expand their scope beyond urban areas to reach all by empowering small-scale local operators and communities to build and operate modern mobile networks themselves. We showcase a particular instance of the proposed deployment model through a trial deployment in rural UK to demonstrate its practical feasibility.

Narrowband IoT (NB-IoT) is a new cellular network technology that has been designed for low capability, low power consumption devices that are expected to operate for more than 10 years on a single battery. These types of devices will be inexpensive (less than $5) and deployed on massive scales. This long life expectancy will lead to the need for occasional software updates, to very large groups of devices. While a new multicast mechanism has recently been proposed for the efficient multicast transmission of such updates, it assumes that devices can be grouped together and synchronized in order to receive the multicast data. In this paper, we explore three different approaches to achieve device grouping, with different trade-offs between bandwidth usage, energy consumption and compliance with the NB-IoT standard. To assess the performance of each pproach, we conducted a thorough experimental evaluation under realistic operating conditions.

Narrowband-Internet of Things (NB-IoT) has been released by 3GPP to provide extended coverage and low energy consumption for low-cost machine-type devices. Requiring only a reasonably low-cost hardware update to the already deployed long term evolution base stations and being compatible with current core network and enhanced core solutions that aim to reduce the battery consumption and minimize the signaling, NB-IoT deployments are quickly increasing, making NB-IoT a dominating technology for low-power wide area networks. To this aim, in this paper, we focus on group communications (i.e., multicast) in NB-IoT to efficiently support the transmission of firmware, software, task updates, or commands toward a large set of devices. We discuss the architectural and procedural enhancements needed to support the unique features of group communications in machine-type environments, such as customer-driven group formation. We also extend the NBIoT frame to include a channel for multicast transmissions. Finally, we propose two transmission strategies for multicast content delivery and evaluate their performance considering the impact on the downlink background traffic and the channel occupancy.

The availability of multiple collocated wireless networks using heterogeneous technologies and the multiaccess support of contemporary mobile devices have allowed wireless connectivity optimization, enabled through vertical handover (VHO) operations. However, this comes at high energy consumption on the mobile device due to the inherently expensive nature of some of the involved operations. This work proposes exploiting short‐range cooperation among collocated mobile devices to improve the energy efficiency of vertical handover operations. The proactive exchange of handover‐related information through low‐energy short‐range communication technologies, like Bluetooth, can help in eliminating expensive signaling steps when the need for a VHO arises. A model is developed for capturing the mean energy expenditure of such an optimized VHO scheme in terms of relevant factors by means of closed‐form expressions. The descriptive power of the model is demonstrated by investigating various typical usage scenarios and is validated through simulations. It is shown that the proposed scheme has superior performance in several realistic usage scenarios considering important relevant factors, including network availability, the local density of mobile devices, and the range of the cooperation technology. Finally, the paper explores cost/benefit trade‐offs associated with the short‐range cooperation protocol. It is demonstrated that the protocol may be parametrized so that the trade‐off becomes nearly optimized and the cost is maintained affordable for a wide range of operational scenarios.

The accuracy of measurement-driven mobile coverage maps depends on the quality, density and pattern of the signal strength observations. Thus, identifying an efficient measurement data collection methodology is essential, especially when considering the cost associated with the measurement collection approaches (e.g., drive tests, crowd approaches). We propose ZipWeave, a novel measurement data collection and fusion framework for building efficient and reliable measurement-based mobile coverage maps. ZipWeave incorporates a novel nonuniform sampling strategy to achieve reliable coverage maps with reduced sample size. Assuming prior knowledge of the propagation characteristics of the region of interest, we first examine the potential gains of this non-uniform sampling strategy in different cases via a measurement-based statistical analysis methodology; this involves irregular spatial tessellation of the region of interest into sub-regions with internally similar radio propagation characteristics and sampling based on these sub-regions. We then present a practical form of ZipWeave nonuniform sampling strategy that can be used even without any prior information. In all our evaluations, we show that the ZipWeave non-uniform sampling approach reduces the samples by half compared to the common systematic-random sampling, while maintaining similar accuracy. Moreover, we show that the other key feature of ZipWeave to combine high-quality controlled measurements (that present limited geographic footprint similar to drive tests) with crowdsourced measurements (that cover a wider footprint) leads to more reliable mobile coverage maps overall.

Machine Type Communications (MTC) collectively refers to the exchange of data among devices that operate without human intervention. A significant number of such devices are currently served by cellular networks, and that number is expected to grow in the near future. However, cellular networks, including current fourth generation LTE networks, face several challenges when it comes to handling MTC traffic as they were primarily designed for Human Type Communications (HTC) which have very different traffic patterns. In this paper we focus on periodic MTC devices, such as sensors and meters, which cause significant signaling load and increased collisions over standard LTE networks. We propose ASPIS, a holistic mechanism designed to overcome these problems. ASPIS reduces the signaling load by partly preserving a device's connection to the network in conjunction with a new Random Access process and efficient support for short message transmissions. In addition, it uses a proactive preamble split scheme to alleviate collisions. ASPIS is easy to implement without requiring hardware changes while at the same time maintains security and can be incrementally deployed alongside legacy devices/infrastructure. We showcase the practicality of ASPIS by implementing it on the OpenAirInterface platform. Further, we demonstrate its effectiveness through extensive evaluations via a combination of small-scale experimental evaluation and large-scale, realistic simulations.

Emerging 5G mobile networks are envisioned to become multi-service environments, enabling the dynamic deployment of services with a diverse set of performance requirements, accommodating the needs of mobile network operators, verticals and over-the-top (OTT) service providers. Virtualizing the mobile network in a flexible way is of paramount importance for a cost-effective realization of this vision. While virtualization has been extensively studied in the case of the mobile core, virtualizing the radio access network (RAN) is still at its infancy. In this paper, we present Orion, a novel RAN slicing system that enables the dynamic on-the-fly virtualization of base stations, the flexible customization of slices to meet their respective service needs and which can be used in an end-to-end network slicing setting. Orion guarantees the functional and performance isolation of slices, while allowing for the efficient use of RAN resources among them. We present a concrete prototype implementation of Orion for LTE, with experimental results, considering alternative RAN slicing approaches, indicating its efficiency and highlighting its isolation capabilities. We also present an extension to Orion for accommodating the needs of OTT providers.

Emerging 5G mobile networks are envisioned to support the dynamic deployment of services with diverse performance requirements, accommodating the needs of mobile network operators and verticals. Virtualizing the mobile network components in a flexible and cost-effective way is therefore of paramount importance. In this work, we highlight the capabilities of Orion, a novel RAN slicing architecture that enables the dynamic virtualization of base stations and flexible customization of slices to meet their respective service needs. Our demonstration of Orion's capabilities is based on a prototype implementation employing a modified version of the OpenAirInterface software LTE platform. Using this prototype, we demonstrate the functional and performance isolation, and the efficient sharing of radio hardware and spectrum that can be achieved among Orion RAN slices. Moreover, we show how Orion can be used in an end-to-end network slicing setting and demonstrate the effects of the slices' configuration and placement of virtual functions in the overall quality of the deployed services.

Although SDN is considered as one of the key technologies behind the impending 5G evolution of mobile networks, the opportunity of reaping its benefits is largely still untapped on the Radio Access Network (RAN) side due to the lack of a software-defined RAN (SD-RAN) platform. In this work we demonstrate the capabilities of FlexRAN, an open-source SD-RAN platform developed to fill this void. FlexRAN separates the RAN control and data planes with a custom-tailored southbound API. Besides it features a hierarchical control plane architecture that enables programmability, flexible and dynamic control function placement (allowing different degrees of coordination within and among base stations) and real-time control. Virtualized control functions and control delegation are two key features in FlexRAN that makes these capabilities possible. This demo illustrates the capabilities and the performance of FlexRAN based on a prototype implementation, while its applicability is highlighted through a Mobile Edge Computing use case, where it acts as an enabler of a video bitrate adaptation application based on the radio conditions at the network edge.

Although the radio access network (RAN) part of mobile networks offers a significant opportunity for benefiting from the use of SDN ideas, this opportunity is largely untapped due to the lack of a software-defined RAN (SD-RAN) platform. We fill this void with FlexRAN, a flexible and programmable SD-RAN platform that separates the RAN control and data planes through a new, custom-tailored southbound API. Aided by virtualized control functions and control delegation features, FlexRAN provides a flexible control plane designed with support for real-time RAN control applications, flexibility to realize various degrees of coordination among RAN infrastructure entities, and programmability to adapt control over time and easier evolution to the future following SDN/NFV principles. We implement FlexRAN as an extension to a modified version of the OpenAirInterface LTE platform, with evaluation results indicating the feasibility of using FlexRAN under the stringent time constraints posed by the RAN. To demonstrate the effectiveness of FlexRAN as an SD-RAN platform and highlight its applicability for a diverse set of use cases, we present three network services deployed over FlexRAN focusing on interference management, mobile edge computing and RAN sharing.

OFDM is currently the most popular PHY-layer carrier modulation technique, used in the latest generations of cellular, Wi-Fi and TV standards. OFDM systems use cycle prefix to mitigate inter-symbol interference. However, most of the existing systems over-provision the size of the cycle prefix considering the worst case scenarios which rarely occur. We propose a novel OFDM PHY receiver design, called CPRecycle , that exploits the redundant cycle prefix to reduce the effects of interference from neighboring nodes. CPRecycle is based on the key observation that the starting position of the FFT window within the cyclic prefix at the OFDM receiver does not affect the received signal but can substantially reduce interference from concurrent transmissions. We further develop an algorithm that is able to find the optimal starting position of the FFT window for each subcarrier using a Gaussian kernel density function and a fixed sphere maximum likelihood detector. Through implementation and extensive evaluations using USRP and off- the-shelf IEEE 802.11g transmitters/interferers, we show the effectiveness of CPRecycle in significantly mitigating interference. CPRecycle only requires local modifications at the receiver and does not require changes in standards, making it incrementally deployable.

Using the plethora of apps on smartphones andtablets entails giving them access to different types of privacysensitive information, including the device's location. This canpotentially compromise user privacy when app providers shareuser data with third parties (e.g., advertisers) for monetizationpurposes. In this paper, we focus on the interface for datasharing between app providers and third parties, and devisean attack that can break the strongest form of the commonlyused anonymization method for protecting the privacy of users. More specifically, we develop a mechanism called Comberthat given completely anonymized mobility data (without anypseudonyms) as input is able to identify different users andtheir respective paths in the data. Comber exploits the observationthat the distribution of speeds is typically similar amongdifferent users and incorporates a generic, empirically derivedhistogram of user speeds to identify the users and disentangletheir paths. Comber also benefits from two optimizations thatallow it to reduce the path inference time for large datasets. Weuse two real datasets with mobile user location traces (MobileData Challenge and GeoLife) for evaluating the effectivenessof Comber and show that it can infer paths with greater than 90% accuracy with both these datasets.

Usage of WiFi is becoming increasingly popular in public wireless LAN (WLAN) settings like malls, airports and train stations. Similarly to other prominent examples of WiFi usage like enterprise and home settings, public WLANs could also benefit from an SDN-based coordinated management framework that deals with issues like interference and mobility management. However, unlike these settings, public WLANs present a few differences in their characteristics, such as the need to offer location-aware services and dynamic categorization of users, and the consequent need to provide sophisticated association strategies. Motivated by this we propose VALI, an SDN management framework tailored to meet the needs of public WLAN settings. We give an overview of VALI and present initial results obtained using our prototype implementation deployed over a testbed that resembles a realistic public WLAN environment. Our results demonstrate that VALI is a promising solution that could be used to effectively manage public WLAN settings and enable location-aware WiFi access.

The coexistence of LTE-Unlicensed (LTE-U) and WiFi in unlicensed spectrum is studied in the context of airtime sharing. We consider core problem where a set of LTE-U cells from different operators share the same channel as a co-located WiFi access point (AP). We assume that LTE-U cells utilize Listen-Before-Talk (LBT) as the default channel access mechanism. Principally, we deal with the following question: how should an operator's LTE-U cell adjust its contention window in order to provide a fair coexistence both with WiFi and co-located LTE-U cells of other operators? We consider that LTE-U cells behave altruistically both among themselves and to WiFi. Cooperation of LTE-U cells is studied using a coalition formation game framework which is based on the well-known Shapley value. We define a payoff configuration scheme in the coalition game which involves altruism. We prove that the coalitional game is always zero-monotonic, and Shapley value is also max-min fair. We compare airtime sharing performance of Shapley value with weighted proportional fairness via numerical results and show that Shapley value provides much better fairness than proportional fairness as determined by entropy and Jain's index metrics while having roughly equal average airtime.

Licensed Shared Access (LSA) is a new shared spectrum access model that is gaining traction for unlocking incumbent spectrum to mobile network operators in a form similar to licensed spectrum, thus having the potential to alleviate the spectrum crunch below 6 GHz. Short-term spectrum auctions can pave the way for dynamic LSA in the future and to create incentives for incumbents to voluntarily participate in the LSA model, thereby increase spectrum availability. Different from existing auction schemes that are mostly based on the sealed-bid auction format, we consider an ascending bid format which is theoretically equivalent to a sealed bid format but comes with better behavioral properties. We develop a novel auction mechanism called GAVEL that follows the ascending bid auction format and is well-suited for the dynamic LSA context. GAVEL, besides being strategy-proof, satisfies the three additional desirable properties of supporting heterogeneous spectrum, fine-grained spectrum sharing and bidder privacy protection. In fact, GAVEL is the first mechanism to satisfy all these properties. Through simulation-based evaluations, GAVEL is shown to outperform two recently proposed schemes in terms of revenue, social welfare, number of winners and achieving high spectrum utilization while at the same time performing close to the LP based optimal solution.

Due to the dramatic growth in mobile data traffic on one hand and the scarcity of the licensed spectrum on the other hand, mobile operators are considering the use of unlicensed bands (especially those in 5 GHz) as complementary spectrum for providing higher system capacity and better user experience. This approach is currently being standardized by 3GPP under the name of LTE Licensed-Assisted Access (LTE-LAA). In this paper, we take a holistic approach for LTE-LAA small cell traffic balancing by jointly optimizing the use of the licensed and unlicensed bands. We pose this traffic balancing as an optimization problem that seeks proportional fair coexistence of WiFi, small cell and macro cell users by adapting the transmission probability of the LTE-LAA small cell in the licensed and unlicensed bands. The motivation for this formulation is for the LTE-LAA small cell to switch between or aggregate licensed and unlicensed bands depending on the interference/traffic level and the number of active users in each band. We derive a closed form solution for this optimization problem and additionally propose a transmission mechanism for the operation of the LTE-LAA small cell on both bands. Through numerical and simulation results, we show that our proposed traffic balancing scheme, besides enabling better LTE-WiFi coexistence and efficient utilization of the radio resources relative to the existing traffic balancing scheme, also provides a better tradeoff between maximizing the total network throughput and achieving fairness among all network flows compared to alternative approaches.

The availability of multiple collocated wireless networks using heterogeneous technologies and the multi-access support of contemporary mobile devices have allowed wireless connectivity optimization, enabled through vertical handover (VHO) operations. However, this comes at a high energy consumption on the mobile device, due to the inherently expensive nature of some of the involved operations. This work proposes exploiting short-range cooperation among collocated mobile devices to improve the energy efficiency of vertical handover operations. The proactive exchange of handover-related information through low-energy short-range communication technologies, like Bluetooth, can help in eliminating expensive signaling steps when the need for a VHO arises. A model is developed for capturing the mean energy expenditure of such an optimized VHO scheme in terms of relevant factors by means of closed-form expressions. This model is validated through simulations and results demonstrate that the proposed scheme has superior performance in several realistic usage scenarios considering important relevant factors, including network availability, the local density of mobile devices and the range of the cooperation technology.

Software-Defined Networking promises to deliver more flexible and manageable networks by providing a clear decoupling between control plane and data plane and by implementing the latter in a logically centralized controller. However, if such principles are to be applied also to wireless networks, new primitives and abstractions capable of providing programmers with a global view of the network capturing channel quality and interference must be devised. Moreover, the dynamic radio environment necessitates fast adaptation of physical parameters such as power, modulation and coding schemes. So the wireless SDN abstractions should allow for such adaptations to happen closer to the air interface. In this paper, we present high level abstractions for channel quality, interference and network reconfiguration; the latter permits operations differing in timescales to be carried out at different controller entities. The proposed concepts have been implemented and evaluated over a WiFi-based WLAN. Empirical measurements show that the proposed platform can be used to implement typical WiFi network management tasks such as channel assignment and interference monitoring.

Software-Defined Networking (SDN) has received, in the last years, significant interest from the academic and the industrial communities alike. The decoupled control and data planes found in an SDN allows for logically centralized intelligence in the control plane and generalized network hardware in the data plane. Although the current SDN ecosystem provides a rich support for wired packet-switched networks, the same cannot be said for wireless networks where specific radio data-plane abstractions, controllers, and programming primitives are still yet to be established. In this work, we present a set of programming abstractions modeling the fundamental aspects of a wireless network, namely state management, resource provisioning, network monitoring, and network reconfiguration. The proposed abstractions hide away the implementation details of the underlying wireless technology providing programmers with expressive tools to control the state of the network. We also present a Software-Defined Radio Access Network Controller for Enterprise WLANs and a Python--based Software Development Kit implementing the proposed abstractions. Finally, we experimentally evaluate the usefulness, efficiency and flexibility of the platform over a real 802.11-based WLAN.

The environmental context of a mobile device determines where/how it is used, which can be exploited for efficient operation and better usability. In this work we describe a general method using only the lightweight sensors on a smartphone to detect if a device is indoor or outdoor. Using semi-supervised machine learning techniques, our method automatically learns characteristics of new environments and devices, thereby achieves detection accuracy of over 90% even in unfamiliar circumstances. Therefore, it easily outperforms existing indoor-outdoor detection techniques based on static algorithms, or relying on energy hungry and unreliable GPS.

Programming wireless networks requires accounting for multiple complex operations, such as monitoring interference and allocating radio resources. Employing the Software-Defined Networking (SDN) paradigm eases the implementation of such tasks when augmented with suitable high-level programming abstractions. In this work, we present a set of programming abstractions modeling three fundamental aspects of a wireless networks, namely state management, resource provisioning, and network state collection. We also describe our proof-of-concept implementation of the proposed abstractions focusing on WiFi networks and show its use for realizing typical control tasks such as mobility management and traffic engineering as Network Apps.

We present a mobile crowdsensing approach for urban WiFi characterization that leverages commodity smartphones and the natural mobility of people. Specifically, we report measurement results obtained for Edinburgh, a representative European city, on detecting the presence of deployedWiFi APs via the mobile crowdsensing approach. They show that few channels in 2.4GHz are heavily used; in contrast, there is hardly any activity in the 5GHz band even though relatively it has a greater number of available channels. Spatial analysis of spectrum usage reveals that mutual interference among nearby APs operating in the same channel can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city-scale are similar to that of WiFi deployments in public spaces of different indoor environments. We validate our approach in comparison with wardriving, and also show that our findings generally match with previous studies based on other measurement approaches. As an application of the mobile crowdsensing based urban WiFi monitoring, we outline a cloud based WiFi router configuration service for better interference management with global awareness in urban areas.

We consider for the first time available bandwidth estimation (ABE) in the context of 802.11n, which is fast replacing the legacy 802.11a/b/g networks. We experimentally show that the frame aggregation (FA) feature of 802.11n is the dominant one among 802.11n features affecting the ABE. Using an indoor 802.11n wireless testbed, we compare three ABE tools (WBest, DietTopp and pathChirp) in various cross-traffic scenarios. We find that FA significantly hurts the accuracy of all ABE tools; DietTopp and pathChirp are relatively more robust than WBest. Because faster available bandwidth estimation and less intrusiveness are desirable properties of any ABE tool and WBest satisfies them relatively better than the other two tools, we conduct an in-depth investigation into the harmful effect of FA on ABE using WBest. This in turn led us to come up with two key design principles to counter FA effects: (1) treating aggregated probes as one jumbo probe; and (2) generating a larger number of probes. We then develop an enhanced version of WBest termed WBest+ that incorporates these principles. Our evaluation shows that the new version is effective in achieving accurate ABE in the presence of FA.

The environmental context of a mobile device determines how it is used and how the device can optimize operations for greater efficiency and usability. We consider the problem of detecting if a device is indoor or outdoor. Towards this end, we present a general method employing semi-supervised machine learning and using only the lightweight sensors on a smartphone. We find that a particular semi-supervised learning method called co-training, when suitably engineered, is most effective. It is able to automatically learn characteristics of new environments and devices, and thereby provides a detection accuracy exceeding 90% even in unfamiliar circumstances. It can learn and adapt online, in real time, at modest computational costs. Thus the method is suitable for on-device learning. Implementation of the indoor-outdoor detection service based on our method is lightweight in energy use -- it can sleep when not in use and does not need to track the device state continuously. It is shown to outperform existing indoor-outdoor detection techniques that rely on static algorithms or GPS, in terms of both accuracy and energy-efficiency.

WiFi in indoor environments exhibits spatio-temporal variations in terms of coverage and interference in typical WLAN deployments with multiple APs, motivating the need for automated monitoring to aid network administrators to adapt the WLAN deployment in order to match the user expectations. We develop Pazl, a mobile crowdsensing based indoor WiFi monitoring system that is enabled by a novel hybrid localization mechanism to locate individual measurements taken from participant phones. The localization mechanism in Pazl integrates the best aspects of two well known localization techniques, pedestrian dead reckoning and WiFi fingerprinting; it also relies on crowdsourcing for constructing the WiFi fingerprint database. Compared to existing WiFi monitoring systems based on static sniffers, Pazl is low cost and provides a user-side perspective. Pazl is significantly more automated than wireless site survey tools such as Ekahau Mobile Survey tool by drastically reducing the manual point-and-click based measurement location determination. We implement Pazl through a combination of Android mobile app and cloud backend application on the Google App Engine. Experimental evaluation of Pazl with a trial set of users shows that it yields similar results to manual site surveys but without the tedium.

WiFi fingerprinting has received much attention for indoor mobile phone localization. In this study, we examine the impact of various aspects underlying a WiFi fingerprinting system. Specifically, we investigate different definitions for fingerprinting and location estimation algorithms across different indoor environments ranging from a multi-storey office building to shopping centers of different sizes. Our results show that the fingerprint definition is as important as the choice of location estimation algorithm and there is no single combination of these two that works across all environments or even all floors of a given environment. We then consider the effect of WiFi frequency bands (e.g., 2.4GHz and 5GHz) and the presence of virtual access points (VAPs) on location accuracy with WiFi fingerprinting. Our results demonstrate that 5GHz signals are less prone to variation and thus yield more accurate location estimation. We also find that the presence of VAPs improves location estimation accuracy.

The large number of applications that rely on indoor positioning encourages more advancement in this field. Smartphones are becoming a common presence in our daily life, so taking advantage of their sensors can help to provide ubiquitous positioning solution. We propose HiMLoc, a novel solution that synergistically uses Pedestrian Dead Reckoning (PDR) and WiFi fingerprinting to exploit their positive aspects and limit the impact of their negative aspects. Specifically, HiMLoc combines location tracking and activity recognition using inertial sensors on mobile devices with location-specific weighted assistance from a crowd-sourced WiFi fingerprinting system via a particle filter. By using just the most common sensors available on the large majority of smartphones (accelerometer, compass, and WiFi card) and offering an easily deployable method (requiring just the locations of stairs, elevators, corners and entrances), HiMLoc is shown to achieve median accuracies lower than 3 meters in most cases.

The 802.11n standard introduces a number of new MAC and PHY features to achieve high throughput and reliability. We conduct a comprehensive characterization of 802.11n performance with respect to its constituent features across a wide variety of scenarios with the aid of 802.11n wireless LAN testbed based measurements and statistical techniques including regression analysis. Our results show that different 802.11n features are interdependent when optimizing performance metrics such as throughput; the nature of interdependence as well as their relative impact are scenario dependent. We show the feasibility of online sender-side interference type detection, a key part of identifying the operational scenario for comprehensive 802.11n link adaptation, via a supervised machine learning based classifier. Finally, we highlight the unfairness problem of 802.11n networks that is linked to the frame aggregation feature.

We consider the mobile phone location tracking problem in indoor environments. We propose a hybrid approach for energy-efficient indoor mobile phone localization by combining two well-known techniques namely, pedestrian dead reckoning and WiFi fingerprinting. Our preliminary evaluation of the proposed approach shows that it achieves better location accuracy (median error around 2m) than either of the two techniques on which it is based.

We consider the traffic adaptive channel allocation problem in long-distance 802.11 mesh networks. Our approach is to exploit the capability provided by 802.11 hardware to use different channel widths and assign channel widths to links based on their relative traffic volume. We show that this traffic-aware channel width assignment problem is NP-complete and propose a polynomial time, greedy channel allocation algorithm that guarantees valid channel allocations for each node. Evaluation of the proposed algorithm via simulations of real network topologies shows that it consistently outperforms the current approach of fixed width allocation due to its ability to adapt to spatio-temporal variations in traffic demands.

UHF RFID has emerged as a mature technology with important applications in inventory control, pervasive computing and e-commerce but integration into mobile phone platforms has not yet been fully realized. One of the primary barriers contributing to this is a lack of understanding about how readers will impact the battery life of a mobile smartphone. In this work we describe our experimental investigation into the energy consumption impact of compact UHF Class-1 Generation-2 RFID readers as compared with existing sensors that are common in smartphones. Our evaluation shows that the energy consumption of compact RFID readers is affected by environmental conditions and network parameters that influence reader-tag link quality and that reader energy consumption is comparable to existing integrated Bluetooth, GPS and WiFi interfaces on smartphones. Existing experimental work on UHF readers (e.g., [1]) has focused mainly on tag read performance while our goal is to characterize how various factors impact the energy consumption of compact readers relative to existing smartphone sensors.

Stix is a platform managing emerging large-scale broadband wireless access (BWA) networks. It has been developed to make it easy to manage such networks for community deployments and wireless Internet service providers while keeping the network management infrastructure scalable and flexible. Stix is based on the notions of goal-oriented and in-network management. With Stix, administrators graphically specify network management activities as workflows, which are deployed at a distributed set of agents within the network that cooperate in executing those workflows and storing management information. We implement the Stix system on embedded boards and show that the implementation has a low memory footprint. Using real topology and logging data from a large-scale BWA network operator, we show that Stix is significantly more scalable (via reduction in management traffic) compared to the commonly employed centralized management approach. Finally we use two case studies to demonstrate the ease with which Stix platform can be used for carrying out network reconfiguration and performance management tasks, thereby also showing its potential as a flexible platform to realize self-management mechanisms.

Recurrent applications that mostly run in the background are a significant source of power consumption on battery-limited mobile phones. We highlight the pitfalls of scheduling such applications independently without awareness of each other's schedules. We illustrate the significant energy savings that can be achieved via batch scheduling of recurrent mobile phone applications. We then present our on-going work on developing a general batch scheduling framework for such applications and also outline our early experiences studying the benefit of batch scheduling on two different mobile phone platforms-Nokia N95 and HTC (Android) - that are commonly used in the research community.

We consider the distributed channel allocation problem in IEEE 802.11-based multi-radio wireless mesh networks. We develop a new scalable protocol termed LCAP for efficient and adaptive distributed multi-radio channel allocation. In LCAP, nodes autonomously learn their channel allocation based on neighborhood and channel usage information, which is obtained via a novel neighbor discovery protocol that enables neighboring nodes to efficiently discover each other even when they do not share a common channel. Extensive simulation-based evaluation of LCAP relative to the state-of-the-art Asynchronous Distributed Coloring (ADC) protocol demonstrates that LCAP is able to achieve its stated objectives of efficient channel utilization across diverse traffic patterns, protocol scalability and adaptivity to factors such as external interference. We also present a prototype implementation of the LCAP neighbor discovery module that is key to implementing the proposed approach.

We consider the multi-rate retry (MRR) capability provided by current 802.11 implementations and carry out simulation-based study of its impact on performance with state-of- the-art rate control mechanisms in typical indoor wireless LAN scenarios. We find that MRR is more effective in non-congested environments, necessitating the need for a mechanism to differentiate between congested and non-congested situations to better exploit the MRR capability. We also observe that decoupling the long-term rate adaptation algorithm from the MRR mechanism is key to fully realizing the benefits of MRR.

We consider RFID-based sensing applications enabled by passive or semi-passive tags and mobile devices equipped with readers. We experimentally investigate the feasibility of such RFID-based mobile sensor data gathering applications, focusing on UHF RFID devices and indoor scenarios. We examine the impact of various factors, including reader mobility, multiple closely located tags and other key related metrics. Our measurement results suggest the feasibility of using RFID for such applications.

We consider the impact of system-related overheads on the performance of two reliable transport protocols (TCP and XCP) over wireless links. Our measurement results indicate that various components providing networking support at a wireless host collectively have significant impact on transport layer performance. Furthermore, the relative performance of protocols is dependent on the host's network subsystem configuration. These results highlight the importance of considering system-dependent behaviors in wireless network protocol evaluations

We examine the impact of physical layer rate adaptation mechanisms on the performance of real applications over 802.11 wireless links in diverse channel environments. Our evaluations are based on a testbed with real wireless devices equipped with commodity 802.11 hardware and a hardware channel emulator. We consider two different and well-known 802.11 rate adaptation mechanisms (Onoe and SampleRate) and study their performance under several realistic workloads, including multimedia streaming and Web browsing. We observe that the application performance with different rate adaptation mechanisms is dependent on the specific tradeoffs these mechanisms make at the link layer in an application-oblivious manner between improving throughput and limiting frame loss. More importantly, their relative performance for a given workload is quite sensitive to the channel quality and environment. These observations highlight the importance of choosing the rate selection strategy adaptively in an application and channel aware manner

Abstract We develop an on‐demand, multipath distance vector routing protocol for mobile ad hoc networks. Specifically, we propose multipath extensions to a well‐studied single path routing protocol known as ad hoc on‐demand distance vector (AODV). The resulting protocol is referred to as ad hoc on‐demand multipath distance vector (AOMDV). The protocol guarantees loop freedom and disjointness of alternate paths. Performance comparison of AOMDV with AODV using ns‐2 simulations shows that AOMDV is able to effectively cope with mobility‐induced route failures. In particular, it reduces the packet loss by up to 40% and achieves a remarkable improvement in the end‐to‐end delay (often more than a factor of two). AOMDV also reduces routing overhead by about 30% by reducing the frequency of route discovery operations. Copyright © 2006 John Wiley & Sons, Ltd.

We consider the channel assignment problem in a multi-radio wireless mesh network that involves assigning channels to radio interfaces for achieving efficient channel utilization. We propose the notion of a traffic-independent base channel assignment to ease coordination and enable dynamic, efficient and flexible channel assignment. We present a novel formulation of the base channel assignment as a topology control problem, and show that the resulting optimization problem is NP-complete. We then develop a new greedy heuristic channel assignment algorithm (termed CLICA) for finding connected, low interference topologies by utilizing multiple channels. Our extensive simulation studies show that the proposed CLICA algorithm can provide large reduction in interference (even with a small number of radios per node), which in turn leads to significant gains in both link layer and multihop performance in 802.11-based multi-radio mesh networks.

No abstract available.

We examine two aspects concerning the influence of unidirectional links on routing performance in multihop wireless networks. In the first part of the paper we evaluate the benefit from utilizing unidirectional links for routing as opposed to using only bidirectional links. Our evaluations are based on three transmit power assignment models that reflect some realistic network scenarios with unidirectional links. Our results indicate that the marginal benefit of using a high-overhead routing protocol to utilize unidirectional links is questionable.Most common routing protocols however simply assume that all network links are bidirectional and thus may need additional protocol actions to remove unidirectional links from route computations. In the second part of the paper we investigate this issue using a well known on-demand routing protocol Ad hoc On-demand Distance Vector (AODV) as a case study. We study the performance of three techniques for AODV for efficient operation in presence of unidirectional links viz. BlackListing Hello and ReversePathSearch. While BlackListing and Hello techniques explicitly eliminate unidirectional links the ReversePathSearch technique exploits the greater network connectivity offered by the existence of multiple paths between nodes. Performance results using ns-2 simulations under varying number of unidirectional links and node speeds show that all three techniques improve performance by avoiding unidirectional links the ReversePathSearch technique being the most effective.

In this paper, we propose the virtual dynamic backbone protocol (VDBP) for ad hoc networks. VDBP constructs a backbone from a subset of the nodes in the network satisfying these two properties: (i) Any node in the network is either a backbone node or is a neighbor of a backbone node (dominating set property), (ii) any pair of backbone nodes is connected via other backbone nodes (connectivity property). VDBP provides explicit mechanisms to handle the mobility of the nodes and depends on local computations to a large extent. Thus it is highly self-organizing even under highly dynamic topologies. Such a backbone can be potentially used by a routing protocol to disseminate broadcast or multicast messages as well as to handle QoS traffic in mobile ad hoc networks. We present the performance of VDBP in terms of backbone size and connectivity using simulations.

In this paper we present RBRP, a TDMA-based distributed medium access control (MAC) protocol for broadcast reservations in mobile ad hoc networks. The primary design goal of RBRP is robustness of broadcast reservations in the presence of hidden terminals, reservation deadlocks and mobility. Specifically, RBRP is characterized by four important features: (i) a two step signaling process in which the reservation signaling part is completely decoupled from the data portion of the frame; (ii) a multiple mini-slot reservation request phase within a reservation slot to prevent deadlocks; (iii) special signaling at the beginning of every data slot to adapt to topology changes and (iv) an optimization to improve the spatial reuse of the channel, and hence the utilization, with unicast traffic. Using a detailed simulation model in ns-2, we evaluated the performance of RBRP in comparison with the IEEE 802.11 distributed coordination function (DCF) MAC. Our simulation results show that RBRP achieves more than 100% throughput improvement over 802.11 DCF in the presence of hidden terminals and mobility, both for broadcast and unicast traffic, while having comparable performance in a fully connected network.

We develop an on-demand multipath distance vector protocol for mobile ad hoc networks. Specifically, we propose multipath extensions to a well-studied single path routing protocol known as ad hoc on-demand distance vector (AODV). The resulting protocol is referred to as ad hoc on-demand multipath distance vector (AOMDV). The protocol computes multiple loop-free and link-disjoint paths. Loop-freedom is guaranteed by using a notion of "advertised hopcount". Link-disjointness of multiple paths is achieved by using a particular property of flooding. Performance comparison of AOMDV with AODV using ns-2 simulations shows that AOMDV is able to achieve a remarkable improvement in the end-to-end delay-often more than a factor of two, and is also able to reduce routing overheads by about 20%.