Nokia (Ireland)

The rapid growth of consumer unmanned aerial vehicles (UAVs) is creating promising new business opportunities for cellular operators. On the one hand, UAVs can be connected to cellular networks as new types of user equipment, therefore generating significant revenues for the operators that can guarantee their stringent service requirements. On the other hand, UAVs offer the unprecedented opportunity to realize UAV-mounted flying base stations (BSs) that can dynamically reposition themselves to boost coverage, spectral efficiency, and user quality of experience. Indeed, the standardization bodies are currently exploring possibilities for serving commercial UAVs with cellular networks. Industries are beginning to trial early prototypes of flying BSs or user equipments, while academia is in full swing researching mathematical and algorithmic solutions to address interesting new problems arising from flying nodes in cellular networks. In this paper, we provide a comprehensive survey of all of these developments promoting smooth integration of UAVs into cellular networks. Specifically, we survey: 1) the types of consumer UAVs currently available off-the-shelf; 2) the interference issues and potential solutions addressed by standardization bodies for serving aerial users with the existing terrestrial BSs; 3) the challenges and opportunities for assisting cellular communications with UAV-based flying relays and BSs; 4) the ongoing prototyping and test bed activities; 5) the new regulations being developed to manage the commercial use of UAVs; and 6) the cyber-physical security of UAV-assisted cellular communications.

The increasing consumption of multimedia services and the demand of high-quality services from customers has triggered a fundamental change in how we administer networks in terms of abstraction, separation, and mapping of forwarding, control and management aspects of services. The industry and the academia are embracing 5G as the future network capable to support next generation vertical applications with different service requirements. To realize this vision in 5G network, the physical network has to be sliced into multiple isolated logical networks of varying sizes and structures which are dedicated to different types of services based on their requirements with different characteristics and requirements (e.g., a slice for massive IoT devices, smartphones or autonomous cars, etc.). Softwarization using Software-Defined Networking (SDN) and Network Function Virtualization (NFV)in 5G networks are expected to fill the void of programmable control and management of network resources. In this paper, we provide a comprehensive review and updated solutions related to 5G network slicing using SDN and NFV. Firstly, we present 5G service quality and business requirements followed by a description of 5G network softwarization and slicing paradigms including essential concepts, history and different use cases. Secondly, we provide a tutorial of 5G network slicing technology enablers including SDN, NFV, MEC, cloud/Fog computing, network hypervisors, virtual machines & containers. Thidly, we comprehensively survey different industrial initiatives and projects that are pushing forward the adoption of SDN and NFV in accelerating 5G network slicing. A comparison of various 5G architectural approaches in terms of practical implementations, technology adoptions and deployment strategies is presented. Moreover, we provide a discussion on various open source orchestrators and proof of concepts representing industrial contribution. The work also investigates the standardization efforts in 5G networks regarding network slicing and softwarization. Additionally, the article presents the management and orchestration of network slices in a single domain followed by a comprehensive survey of management and orchestration approaches in 5G network slicing across multiple domains while supporting multiple tenants. Furthermore, we highlight the future challenges and research directions regarding network softwarization and slicing using SDN and NFV in 5G networks.

This work describes silicon nanoparticle-based lithium-ion battery negative electrodes where multiple nonactive electrode additives (usually carbon black and an inert polymer binder) are replaced with a single conductive binder, in this case, the conducting polymer PEDOT: PSS. While enabling the production of well-mixed slurry-cast electrodes with high silicon content (up to 95 wt %), this combination eliminates the well-known occurrence of capacity losses due to physical separation of the silicon and traditional inorganic conductive additives during repeated lithiation/delithiation processes. Using an in situ secondary doping treatment of the PEDOT: PSS with small quantities of formic acid, electrodes containing 80 wt % SiNPs can be prepared with electrical conductivity as high as 4.2 S/cm. Even at the relatively high areal loading of 1 mg/cm(2), this system demonstrated a first cycle lithiation capacity of 3685 mA·h/g (based on the SiNP mass) and a first cycle efficiency of ∼78%. After 100 repeated cycles at 1 A/g this electrode was still able to store an impressive 1950 mA·h/g normalized to Si mass (∼75% capacity retention), corresponding to 1542 mA·h/g when the capacity is normalized by the total electrode mass. At the maximum electrode thickness studied (∼1.5 mg/cm(2)), a high areal capacity of 3 mA·h/cm(2) was achieved. Importantly, these electrodes are based on commercially available components and are produced by the standard slurry coating methods required for large-scale electrode production. Hence, the results presented here are highly relevant for the realization of commercial LiB negative electrodes that surpass the performance of current graphite-based negative electrode systems.

One weakness of batteries is the rapid falloff in charge-storage capacity with increasing charge/discharge rate. Rate performance is related to the timescales associated with charge/ionic motion in both electrode and electrolyte. However, no general fittable model exists to link capacity-rate data to electrode/electrolyte properties. Here we demonstrate an equation which can fit capacity versus rate data, outputting three parameters which fully describe rate performance. Most important is the characteristic time associated with charge/discharge which can be linked by a second equation to physical electrode/electrolyte parameters via various rate-limiting processes. We fit these equations to ~200 data sets, deriving parameters such as diffusion coefficients or electrolyte conductivities. It is possible to show which rate-limiting processes are dominant in a given situation, facilitating rational design and cell optimisation. In addition, this model predicts the upper speed limit for lithium/sodium ion batteries, yielding a value that is consistent with the fastest electrodes in the literature.

We consider a cellular network deployment where UAV-to-UAV (U2U) transmit-receive pairs share the same spectrum with the uplink (UL) of cellular ground users (GUEs). For this setup, we focus on analyzing and comparing the performance of two spectrum sharing mechanisms: (i) underlay, where the same time-frequency resources may be accessed by both UAVs and GUEs, resulting in mutual interference, and (ii) overlay, where the available resources are divided into orthogonal portions for U2U and GUE communications. We evaluate the coverage probability and rate of both link types and their interplay to identify the best spectrum sharing strategy. We do so through an analytical framework that embraces realistic height-dependent channel models, antenna patterns, and practical power control mechanisms. For the underlay, we find that although the presence of U2U direct communications may worsen the uplink performance of GUEs, such effect is limited as base stations receive the power-constrained UAV signals through their antenna sidelobes. In spite of this, our results lead us to conclude that in urban scenarios with a large number of UAV pairs, adopting an overlay spectrum sharing seems the most suitable approach for maintaining a minimum guaranteed rate for UAVs and a high GUE UL performance.

Constrained shortest distance (CSD) querying is one of the fundamental graph query primitives, which finds the shortest distance from an origin to a destination in a graph with a constraint that the total cost does not exceed a given threshold. CSD querying has a wide range of applications, such as routing in telecommunications and transportation. With an increasing prevalence of cloud computing paradigm, graph owners desire to outsource their graphs to cloud servers. In order to protect sensitive information, these graphs are usually encrypted before being outsourced to the cloud. This, however, imposes a great challenge to CSD querying over encrypted graphs. Since performing constraint filtering is an intractable task, existing work mainly focuses on unconstrained shortest distance queries. CSD querying over encrypted graphs remains an open research problem. In this paper, we propose Connor, a novel graph encryption scheme that enables approximate CSD querying. Connor is built based on an efficient, tree-based ciphertext comparison protocol, and makes use of symmetric-key primitives and the somewhat homomorphic encryption, making it computationally efficient. Using Connor, a graph owner can first encrypt privacy-sensitive graphs and then outsource them to the cloud server, achieving the necessary privacy without losing the ability of querying. Extensive experiments with real-world data sets demonstrate the effectiveness and efficiency of the proposed graph encryption scheme.

Experiments show, for the first time, two water droplets coalescing and jumping on a superhydrophobic surface. Adhesion, contact angle hysteresis, and initial wetting behavior governed by the surface structure morphology and length scale, are all shown to play a role in droplet jumping.

Wearable devices with built-in cameras present interesting opportunities for users to capture various aspects of their daily life and are potentially also useful in supporting users with low vision in their everyday tasks. However, state-of-the-art image wearables available in the market are limited to capturing images periodically and do not provide any real-time analysis of the data that might be useful for the wearers. In this paper, we present DeepEye - a match-box sized wearable camera that is capable of running multiple cloud-scale deep learn- ing models locally on the device, thereby enabling rich analysis of the captured images in near real-time without offloading them to the cloud. DeepEye is powered by a commodity wearable processor (Snapdragon 410) which ensures its wearable form factor. The software architecture for DeepEye addresses a key limitation with executing multiple deep learning models on constrained hardware, that is their limited runtime memory. We propose a novel inference software pipeline that targets the local execution of multiple deep vision models (specifically, CNNs) by interleaving the execution of computation-heavy convolutional layers with the loading of memory-heavy fully-connected layers. Beyond this core idea, the execution framework incorporates: a memory caching scheme and a selective use of model compression techniques that further minimizes memory bottlenecks. Through a series of experiments, we show that our execution framework outperforms the baseline approaches significantly in terms of inference latency, memory requirements and energy consumption.

Heterogeneous integration of III-V materials onto silicon photonics has experienced enormous progress in the last few years, setting the groundwork for the implementation of complex on-chip optical systems that go beyond single device performance. Recent advances on the field are expected to impact the next generation of optical communications to attain low power, high efficiency and portable solutions. To accomplish this aim, intense research on hybrid lasers, modulators and photodetectors is being done to implement optical modules and photonic integrated networks with specifications that match the market demands. Similarly, important advances on packaging and thermal management of hybrid photonic integrated circuits (PICs) are currently in progress. In this paper, we report our latest results on hybrid III-V on Si transmitters, receivers and packaged optical modules for high-speed optical communications. In addition, a review of recent advances in this field will be provided for benchmarking purposes.

Network functions virtualization (NFV) continues to gain attention as a paradigm shift in the way telecommunications services are deployed and managed. By separating network function from traditional middleboxes, NFV is expected to lead to reduced capital expenditure and operating expenditure, and to more agile services. However, one of the main challenges to achieving these objectives is how physical resources can be efficiently, autonomously, and dynamically allocated to virtualized network function (VNF) whose resource requirements ebb and flow. In this paper, we propose a graph neural network-based algorithm which exploits VNF forwarding graph topology information to predict future resource requirements for each VNF component (VNFC). The topology information of each VNFC is derived from combining its past resource utilization as well as the modeled effect on the same from VNFCs in its neighborhood. Our proposal has been evaluated using a deployment of a virtualized IP multimedia subsystem, and real VoIP traffic traces, with results showing an average prediction accuracy of 90%, compared to 85% obtained while using traditional feed-forward neural networks. Moreover, compared to a scenario where resources are allocated manually and/or statically, our technique reduces the average number of dropped calls by at least 27% and improves call setup latency by over 29%.

Recent advances in high speed rails (HSRs) are propelling the need for acceptable network service in high speed mobility environments. However, previous studies show that the performance of traditional single-path transmission degrades significantly during high speed mobility due to frequent handoff. Multi-path transmission with multiple carriers is a promising way to enhance the performance, because at any time, there is possibly at least one path not suffering a handoff. In this paper, for the first time, we measure multi-path TCP (MPTCP) with two cellular carriers on HSRs with a peak speed of 310km/h. We find a significant difference in handoff time between the two carriers. Moreover, we observe that MPTCP can provide much better performance than TCP in the poorer of the two paths. This indicates that MPTCP's robustness to handoff is much higher than TCP's. However, the efficiency of MPTCP is far from satisfactory. MPTCP performs worse than TCP in the better path most of the time. We find that the low efficiency can be attributed to poor adaptability to frequent handoff by MPTCP's key operations in sub-flow establishment, congestion control and scheduling. Finally, we discuss possible directions for improving MPTCP for such scenarios.

Abstract One weakness of batteries is the rapid falloff in charge-storage capacity with increasing charge/discharge rate. Rate performance is related to the timescales associated with charge/ionic motion in both electrode and electrolyte. However, no general fittable model exists to link capacity-rate data to electrode/electrolyte properties. Here we demonstrate an equation which can fit capacity versus rate data, outputting three parameters which fully describe rate performance. Most important is the characteristic time associated with charge/discharge which can be linked by a second equation to physical electrode/electrolyte parameters via various rate-limiting processes. We fit these equations to ~200 data sets, deriving parameters such as diffusion coefficients or electrolyte conductivities. It is possible to show which rate-limiting processes are dominant in a given situation, facilitating rational design and cell optimisation. In addition, this model predicts the upper speed limit for lithium/sodium ion batteries, yielding a value that is consistent with the fastest electrodes in the literature.

Rare earth oxides (REOs) are attracting attention for use as cost-effective, high-performance dropwise condensers because of their favorable thermal properties and robust nature. However, to engineer a suitable surface for industrial applications, the mechanism governing wetting must be first fully elucidated. Recent studies exploring the water-wetting state of REOs have suggested that these oxides are intrinsically hydrophobic owing to the unique electronic structure of the lanthanide series. These claims have been countered with evidence that they are inherently hydrophilic and that adsorption of contaminants from the environment is responsible for the apparent hydrophobic nature of these surfaces. Here, using X-ray photoelectron spectroscopy and dynamic water contact angle measurements, we provide further evidence to show that REOs are intrinsically hydrophilic, with ceria demonstrating advancing water contact angles of ≈6° in a clean surface state and similar surface energies to two transition metal oxides (≳72 mJ/m2). Using two model volatile species, it is shown that an adsorption mechanism is responsible for the apparent hydrophobic property observed in REOs as well as in transition metal oxides and silica. This is correlated with the screening of the polar surface energy contribution of the underlying oxide with apparent surface energies reduced to <40 mJ/m2 for the case of nonane adsorption. Moreover, we show that the degree of surface hydroxylation plays an important role in the observed contact angle hysteresis with the receding contact angle of ceria increasing from ∼10° to 45° following thermal annealing in an inert atmosphere. Our findings suggest that high atomic number metal oxides capable of strongly adsorbing volatile species may represent a viable paradigm toward realizing robust surface coating for industrial condensers if certain challenges can be overcome.

As future networks aim to meet the ever-increasing requirements of high-data rate applications, dense, and heterogeneous networks (HetNets) will be deployed to provide better coverage and throughput. Besides the important implications for energy consumption, the trend toward densification calls for more and more wireless links to forward a massive backhaul traffic into the core network. It is critically important to take into account the presence of a wireless backhaul for the energy-efficient design of HetNets. In this paper, we provide a general framework to analyze the energy efficiency of a two-tier MIMO heterogeneous network with wireless backhaul in the presence of both uplink and downlink transmissions. We find that under spatial multiplexing the energy efficiency of a HetNet is sensitive to the network load, and it should be taken into account when controlling the number of users served by each base station. We show that a two-tier HetNet with wireless backhaul can be significantly more energy efficient than a one-tier cellular network. However, this requires the bandwidth division between radio access links and wireless backhaul to be optimally designed according to the load conditions.

The application of wireless backhaul communication and power transfer to outdoor small cells (SCs) could significantly decrease their installation cost. In this paper, the concept of indoor optical wireless power transfer to SCs is investigated in the absence of ambient light, i.e., during darkness hours. An experimental study is conducted by the use of up to four red laser diodes (LDs), a crystalline silicon solar panel and cell placed at 5.2 m. A value of 69% is measured for the fill factor of the solar panel. Also, a total power efficiency of 3.2% is measured for an optical wireless (OW) link with an average efficiency of two LDs of 26.8%, a solar cell efficiency of 13.3% and only 10.6% of geometrical losses. A comparison of this link with a state-of-the-art inductive power transfer system shows an improvement of the total power efficiency by 2.7 times. Another OW link is implemented with a divergence of full width at 36.8% of the peak intensity of 3 and 5.75 mrad along the small and large axes of the beam, respectively. The experimental levels of harvested power are in the order of mW, whereas approximately 1 W is required for the operation of a SC. Therefore, a 42 laser-based transmitter is designed both analytically and by the use of the simulation tool Zemax. The respective results show the feasibility of delivering 7.2 W of optical power to a solar cell of up to 30 m distance with geometrical losses of only 2%, but a beam enclosure is also required due to eye safety restrictions.

The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations, supporting the proliferation of diverse network models such as Attributed Graphs, Heterogeneous Networks, Multilayer Networks, Temporal Networks, Location-aware Networks, Knowledge Networks, Probabilistic Networks, and many other task-driven and data-driven models. In this paper, we propose an overview of these models and their main applications, described under the common denomination of Feature-rich Networks, i. e. models where the expressive power of the network topology is enhanced by exposing one or more peculiar features. The aim is also to sketch a scenario that can inspire the design of novel feature-rich network models, which in turn can support innovative methods able to exploit the full potential of mining complex network structures in domain-specific applications.

This work investigates the thermal conductivity of parts which have been additively manufactured using the aluminium alloy AlSi10Mg by selective laser melting, a laser-based powder bed fusion technique. Thermal conductivity characterisation is of particular importance to thermal engineers wishing to make use of additive manufacturing in next generation thermal management solutions. A number of processing parameters and scanning strategies were employed to fabricate samples for experimental characterisation. While the porosity of produced parts had a significant impact on thermal conductivity, after an anneal heat treatment post-processing step, thermal conductivity increased by 18–41% without any measurable change in porosity. Even though the parts produced with the “points” strategy have higher levels of porosity compared to the “contour-hatch” strategy, it has been found that after the heat treatment step, its thermal conductivity can be increased up to the “contour-hatch” strategy. Analysis of the resulting microstructures using scanning electron microscope and energy-dispersive X-ray showed precipitation and coalescence of Si with increasing heat treatment temperature, with dwell time having a lower impact. While there is a desire for additively manufactured parts with little to no porosity, it has been shown in this study that it is possible to reduce laser energy density requirements by approximately one order of magnitude and still produce parts with acceptable levels of thermal conductivity which could be used for components that are not subjected to strenuous loading conditions, such as heat sinks.

In order to cope with the forecasted 1000× increase in wireless capacity demands by 2030, network operators will aggressively densify their network infrastructure to reuse the spectrum as much as possible. However, it is important to realize that these new ultra-dense small cell networks are fundamentally different from the traditional macrocell or sparse small cell networks, and thus UDNs cannot be deployed and operated in the same way as in the last 25 years. In this article, we systematically investigate and visualize the performance impacts of several fundamental characteristics of UDNs that mobile operators should consider when deploying UDNs. Moreover, we also provide new deployment and management guidelines to address the main challenges brought by UDNs in the future.

Over the past years, TCP has gone through numerous updates to provide performance enhancement under diverse network conditions. However, with respect to losses, little can be achieved with legacy TCP detection and recovery mechanisms. Both fast retransmission and retransmission timeout take at least one extra round trip time to perform, and this might significantly impact the performance of latency-sensitive applications, especially in lossy or high delay networks. While forward error correction (FEC) is not a new initiative in this direction, the majority of the approaches consider FEC inside the application. In this paper, we design and implement a framework, where FEC is integrated within TCP. Our main goal with this design choice is to enable latency sensitive applications over TCP in high delay and lossy networks, but remaining application agnostic. We further incorporate this design into multipath TCP (MPTCP), where we focus particularly on heterogeneous settings, considering the fact that TCP recovery mechanisms further escalate head-of-line blocking in multipath. We evaluate the performance of the proposed framework and show that such a framework can bring significant benefits compared with legacy TCP and MPTCP for latency-sensitive real application traffic, such as video streaming and web services.

In this paper, we propose a new downlink non-orthogonal multiuser superposition transmission scheme for future 5G cellular networks, which we refer to as the lattice partition multiple access (LPMA). In this proposed design, the base station transmits multilevel lattice codes for multiple users. Each user's code level corresponds to a distinct prime and is weighted by a product of all distinct primes of the other users excluding its own. Due to the structural property of lattice codes, each user can cancel out the interference from the other code levels by using the modulo lattice operation in a successive/parallel manner. LPMA can provide better user fairness in symmctrical broadcast channels, compared with non- orthogonal multiple access (NOMA). We demonstrate that the proposed LPMA shows a clear throughput enhancement over the current NOMA scheme.