Showing papers by "Nokia published in 2016"

A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

Abstract: After about a decade of intense research, spurred by both economic and operational considerations, and by environmental concerns, energy efficiency has now become a key pillar in the design of communication networks. With the advent of the fifth generation of wireless networks, with millions more base stations and billions of connected devices, the need for energy-efficient system design and operation will be even more compelling. This survey provides an overview of energy-efficient wireless communications, reviews seminal and recent contribution to the state-of-the-art, including the papers published in this special issue, and discusses the most relevant research challenges to be addressed in the future.

Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things.

Abstract: Mobile-Edge computing (MEC) is an emerging technology currently recognized as a key enabler for 5G networks. Compatible with current 4G networks, MEC will address many key uses of the 5G system, motivated by the massive diffusion of the Internet of Things (IoT). This article aims to provide a tutorial on MEC technology and an overview of the MEC framework and architecture recently defined by the European Telecommunications Standards Institute (ETSI) MEC Industry Specification Group (ISG) standardization organization. We provide some examples of MEC deployment, with special reference to IoT cases, since IoT is recognized as a main driver for 5G. Finally, we discuss the main benefits and challenges for MEC moving toward 5G.

System and method for multimodal short-cuts to digital services

Abstract: A method and system for facilitating user access to services through a wireless device of a user, involves recommending to a user a subset of services from a plurality of services available to the user in which each recommended service of the subset has at least one voice short-cut associated therewith, and selecting a service to be accessed through the user's wireless device from the subset of services according to a voice command by the user corresponding to the voice short-cut of the service.

Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications

Abstract: This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha–beta–gamma (ABG) model, the close-in (CI) free-space reference distance model, and the CI model with a frequency-weighted path loss exponent (CIF). Each of these models has been recently studied for use in standards bodies such as 3rd Generation Partnership Project (3GPP) and for use in the design of fifth-generation wireless systems in urban macrocell, urban microcell, and indoor office and shopping mall scenarios. Here, we compare the accuracy and sensitivity of these models using measured data from 30 propagation measurement data sets from 2 to 73 GHz over distances ranging from 4 to 1238 m. A series of sensitivity analyses of the three models shows that the four-parameter ABG model underpredicts path loss when relatively close to the transmitter, and overpredicts path loss far from the transmitter, and that the physically based two-parameter CI model and three-parameter CIF model offer computational simplicity, have very similar goodness of fit (i.e., the shadow fading standard deviation), exhibit more stable model parameter behavior across frequencies and distances, and yield smaller prediction error in sensitivity tests across distances and frequencies, when compared to the four-parameter ABG model. Results show the CI model with a 1-m reference distance is suitable for outdoor environments, while the CIF model is more appropriate for indoor modeling. The CI and CIF models are easily implemented in existing 3GPP models by making a very subtle modification—by replacing a floating non-physically based constant with a frequency-dependent constant that represents free-space path loss in the first meter of propagation. This paper shows this subtle change does not change the mathematical form of existing ITU/3GPP models and offers much easier analysis, intuitive appeal, better model parameter stability, and better accuracy in sensitivity tests over a vast range of microwave and mmWave frequencies, scenarios, and distances, while using a simpler model with fewer parameters.

5G 3GPP-Like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments

Abstract: For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.

Propagation Path Loss Models for 5G Urban Micro- and Macro-Cellular Scenarios

Abstract: This paper presents and compares two candidate large-scale propagation path loss models, the alpha-beta-gamma (ABG) model and the close-in (CI) free space reference distance model, for the design of fifth generation (5G) wireless communication systems in urban micro- and macro-cellular scenarios. Comparisons are made using the data obtained from 20 propagation measurement campaigns or ray- tracing studies from 2 GHz to 73.5 GHz over distances ranging from 5 m to 1429 m. The results show that the one-parameter CI model has a very similar goodness of fit (i.e., the shadow fading standard deviation) in both line-of-sight and non-line-of-sight environments, while offering substantial simplicity and more stable behavior across frequencies and distances, as compared to the three-parameter ABG model. Additionally, the CI model needs only one very subtle and simple modification to the existing 3GPP floating-intercept path loss model (replacing a constant with a close-in free space reference value) in order to provide greater simulation accuracy, more simplicity, better repeatability across experiments, and higher stability across a vast range of frequencies.

Mobile network architecture evolution toward 5G

Abstract: As a chain is as strong as its weakest element, so are the efficiency, flexibility, and robustness of a mobile network, which relies on a range of different functional elements and mechanisms. Indeed, the mobile network architecture needs particular attention when discussing the evolution of 3GPP EPS because it is the architecture that integrates the many different future technologies into one mobile network. This article discusses 3GPP EPS mobile network evolution as a whole, analyzing specific architecture properties that are critical in future 3GPP EPS releases. In particular, this article discusses the evolution toward a "network of functions," network slicing, and software-defined mobile network control, management, and orchestration. Furthermore, the roadmap for the future evolution of 3GPP EPS and its technology components is detailed and relevant standards defining organizations are listed.

Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications

Abstract: This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha-beta-gamma (ABG) model, the close-in (CI) free space reference distance model, and the CI model with a frequency-weighted path loss exponent (CIF). Each of these models have been recently studied for use in standards bodies such as 3GPP, and for use in the design of fifth generation (5G) wireless systems in urban macrocell, urban microcell, and indoor office and shopping mall scenarios. Here we compare the accuracy and sensitivity of these models using measured data from 30 propagation measurement datasets from 2 GHz to 73 GHz over distances ranging from 4 m to 1238 m. A series of sensitivity analyses of the three models show that the physically-based two-parameter CI model and three-parameter CIF model offer computational simplicity, have very similar goodness of fit (i.e., the shadow fading standard deviation), exhibit more stable model parameter behavior across frequencies and distances, and yield smaller prediction error in sensitivity testing across distances and frequencies, when compared to the four-parameter ABG model. Results show the CI model with a 1 m close-in reference distance is suitable for outdoor environments, while the CIF model is more appropriate for indoor modeling. The CI and CIF models are easily implemented in existing 3GPP models by making a very subtle modification -- by replacing a floating non-physically based constant with a frequency-dependent constant that represents free space path loss in the first meter of propagation.

HEVC-compliant Tile-based Streaming of Panoramic Video for Virtual Reality Applications

Abstract: Delivering wide-angle and high-resolution spherical panoramic video content entails a high streaming bitrate. This imposes challenges when panorama clips are consumed in virtual reality (VR) head-mounted displays (HMD). The reason is that the HMDs typically require high spatial and temporal fidelity contents and strict low-latency in order to guarantee the user's sense of presence while using them. In order to alleviate the problem, we propose to store two versions of the same video content at different resolutions, each divided into multiple tiles using the High Efficiency Video Coding (HEVC) standard. According to the user's present viewport, a set of tiles is transmitted in the highest captured resolution, while the remaining parts are transmitted from the low-resolution version of the same content. In order to enable randomly choosing different combinations, the tile sets are encoded to be independently decodable. We further study the trade-off in the choice of tiling scheme and its impact on compression and streaming bitrate performances. The results indicate streaming bitrate saving from 30% to 40%, depending on the selected tiling scheme, when compared to streaming the entire video content.

Physical-layer security in evolving optical networks

Abstract: We are witnessing the evolution of optical networks toward highly heterogeneous, flexible networks with a widening area of application. As the bandwidth and reliability performance requirements of mission-critical applications tighten, and the amount of carried data grows, issues related to optical network security are becoming increasingly important. Optical networks are vulnerable to several types of attacks at the physical layer, typically aimed at disrupting the service or gaining unauthorized access to carried data. Such security breaches can induce financial losses to clients or loss of privacy, or cause network-wide service disruption, possibly leading to huge data and revenue losses. Awareness of system weaknesses and possible attack methods is a prerequisite for designing effective network security solutions. As optical networks evolve, new and changing vulnerabilities must be identified and dealt with efficiently. To this end, this article provides a comprehensive overview of potential physical-layer attack scenarios in current and future optical networks. It then proposes a general security framework, outlining possible strategies for dealing with such attacks, meant to aid in the development of efficient provisioning, monitoring, protection, and restoration schemes in the context of optical-layer security.

Channel Coding for Ultra-Reliable Low-Latency Communication in 5G Systems

Abstract: This paper investigates block error rate (BLER) performance and computational complexity of candidate channel coding schemes for ultra-reliable low latency communication (URLLC) in 5G. The considered candidates are the same as those identified in 3GPP: turbo, LDPC, polar, and convolutional codes. Details of code constructions and decoding algorithms are provided with computational complexity analysis. Code construction parameters, number of iterations, and list sizes are selected to provide a fair comparison among candidate coding schemes. Simulation results on BLER are shown for several code rates and small-to-moderate block sizes. The results reveal that polar and LDPC codes outperform turbo codes for short block sizes of 40 bits, while the opposite is true for medium block sizes of 200 bits. None of the schemes is a clear winner at all considered block sizes and coding rates. Other aspects like implementation complexity, latency, and flexibility will also be important when deciding the URLLC coding scheme.

Survey of Strategies for Switching Off Base Stations in Heterogeneous Networks for Greener 5G Systems

Abstract: For heterogeneous network, which has been viewed as one pioneering technology for making cellular networks be evolved into 5G systems, reducing energy consumption by dynamically switching off base stations (BSs) has attracted increasing attention recently. With aiming at optimization on energy saving only or another energy-related performance tradeoffs, several BS switch-off strategies have been proposed from different design perspectives, such as random, distance-aware, load-aware, and auction-based strategies. Furthermore, work has been done to consider joint design for BS switch-off strategy and another strategies, such as user association, resource allocation, and physical-layer interference cancellation strategies. Finally, there have been research results about this topic in emerging cloud radio access networks. In this paper, we take an overview on these technologies and present the state of the art on each aspect. Some challenges that need to be solved in this research filed for future work are also described.

Viewport-Adaptive Encoding and Streaming of 360-Degree Video for Virtual Reality Applications

Abstract: Virtual reality applications use 360-degree videos and head mount displays (HMDs) with stereoscopic capabilities to provide full immersion experience. In these applications it is also common to use 4K resolution or higher per view for 360-degree videos. Consequently, this leads to technical challenges in handling the bandwidth requirements while keeping the system latency to the minimal. When the content is viewed with a HMD, a subset of the entire 360-degree video is displayed at a single point of time. To improve the resolution and picture quality of the displayed content, viewport based coding is desirable. In this regard, we investigated various viewport dependent projection schemes including the existing variants of Pyramidal projection. In this regard we propose the multi-resolution versions of Equirectangular and Cubemap projections. Additionally, we developed a methodology for comparing the rate-distortion performance of these projections. Based on the simulation results, it was observed that multi-resolution projections of Equirectangle and Cubemap outperform other projection schemes, significantly.

Indoor 5G 3GPP-like channel models for office and shopping mall environments

Abstract: Future mobile communications systems are likely to be very different to those of today with new service innovations driven by increasing data traffic demand, increasing processing power of smart devices and new innovative applications. To meet these service demands the telecommunications industry is converging on a common set of 5G requirements which includes network speeds as high as 10 Gbps, cell edge rate greater than 100 Mbps, and latency of less than 1 msec. To reach these 5G requirements the industry is looking at new spectrum bands in the range up to 100 GHz where there is spectrum availability for wide bandwidth channels. For the development of new 5G systems to operate in bands up to 100 GHz there is a need for accurate radio propagation models which are not addressed by existing channel models developed for bands below 6 GHz. This paper presents a preliminary overview of the 5G channel models for bands up to 100 GHz in indoor offices and shopping malls, derived from extensive measurements across a multitude of bands. These studies have found some extensibility of the existing 3GPP models (e.g. 3GPP TR36.873) to the higher frequency bands up to 100 GHz. The measurements indicate that the smaller wavelengths introduce an increased sensitivity of the propagation models to the scale of the environment and show some frequency dependence of the path loss as well as increased occurrence of blockage. Further, the penetration loss is highly dependent on the material and tends to increase with frequency. The small-scale characteristics of the channel such as delay spread and angular spread and the multipath richness is somewhat similar over the frequency range, which is encouraging for extending the existing 3GPP models to the wider frequency range. Further work will be carried out to complete these models, but this paper presents the first steps for an initial basis for the model development.

Cognitive Cellular Networks: A Q-Learning Framework for Self-Organizing Networks

Abstract: Self-organizing networks (SON) aim at simplifying network management (NM) and optimizing network capital and operational expenditure through automation. Most SON functions (SFs) are rule-based control structures, which evaluate metrics and decide actions based on a set of rules. These rigid structures are, however, very complex to design since rules must be derived for each SF in each possible scenario. In practice, rules only support generic behavior, which cannot respond to the specific scenarios in each network or cell. Moreover, SON coordination becomes very complicated with such varied control structures. In this paper, we propose to advance SON toward cognitive cellular networks (CCN) by adding cognition that enables the SFs to independently learn the required optimal configurations. We propose a generalized Q-learning framework for the CCN functions and show how the framework fits to a general SF control loop. We then apply this framework to two functions on mobility robustness optimization (MRO) and mobility load balancing (MLB). Our results show that the MRO function learns to optimize handover performance while the MLB function learns to distribute instantaneous load among cells.

Randomised clinical trial: low‐FODMAP rye bread vs. regular rye bread to relieve the symptoms of irritable bowel syndrome

Abstract: SummaryBackground Grains are high in FODMAPs (Fermentable Oligo-, Di-, Monosaccharides And Polyols) and often considered as triggers of IBS symptoms. Aim To evaluate if rye bread low in FODMAPs would be better tolerated than regular rye bread in subjects with IBS. Methods The study was conducted as a randomised double blind controlled cross-over study (n = 87). Participants were supplied with both regular rye bread and low-FODMAP rye bread for 4 weeks. Symptoms were measured with a symptom severity scoring system (IBS-SSS) and visual analogue scale (VAS) assessments of individual symptoms. Quality of life was monitored. Colonic fermentation was measured by the breath hydrogen test and dietary intake by food diaries. Results Dietary fibre intake increased during both study periods compared to baseline. Many signs of IBS i.e. flatulence, abdominal pain, cramps and stomach rumbling were milder on the low-FODMAP rye bread (P-values: 0.04; 0.049; 0.01 and 0.001). The mean of VAS measurements was favourable towards LF bread [−3 (95% CI): −6 to −1, P = 0.02] but no differences were detected in IBS-SSS or quality of life. The AUC of breath hydrogen values was significantly lower during the low-FODMAP bread period (median 52.9 vs. 72.6; P = 0.01). Conclusions Low-FODMAP rye bread helps IBS patients to control their symptoms and reduces gastrointestinal gas accumulation. However, replacing regular rye bread by low-FODMAP bread without concomitant broader dietary changes does not improve quality of life or IBS-SSS. Nonetheless, inclusion of low-FODMAP rye bread in diet might be one way that IBS patients could increase their fibre intake.

Implementation and Validation of an IEEE 802.11ah Module for ns-3

Abstract: IEEE 802.11ah or HaLow is a new Wi-Fi standard for sub-1Ghz communications, aiming to address the major challenges of the Internet of Things: connectivity among a large number of power-constrained stations deployed over a wide area. Existing research on the performance evaluation of 802.11ah is generally based on analytical models, which does not accurately represent real network dynamics and is hard to adjust to different network conditions. To address this hiatus, we implemented the 802.11ah physical and MAC layer in the ns-3 network simulator, which, compared to analytical models, more closely reflects actual protocol behavior and can more easily be adapted to evaluate a broad range of network and traffic conditions. In this paper, we present the details of our implementation, including a sub-1Ghz physical layer model and several novel MAC layer features. Moreover, simulations based on the implemented model are conducted to evaluate performance of the novel features of IEEE 802.11ah.

Sequence Prediction With Sparse Distributed Hyperdimensional Coding Applied to the Analysis of Mobile Phone Use Patterns

Abstract: Modeling and prediction of temporal sequences is central to many signal processing and machine learning applications. Prediction based on sequence history is typically performed using parametric models, such as fixed-order Markov chains ( $n$ -grams), approximations of high-order Markov processes, such as mixed-order Markov models or mixtures of lagged bigram models, or with other machine learning techniques. This paper presents a method for sequence prediction based on sparse hyperdimensional coding of the sequence structure and describes how higher order temporal structures can be utilized in sparse coding in a balanced manner. The method is purely incremental, allowing real-time online learning and prediction with limited computational resources. Experiments with prediction of mobile phone use patterns, including the prediction of the next launched application, the next GPS location of the user, and the next artist played with the phone media player, reveal that the proposed method is able to capture the relevant variable-order structure from the sequences. In comparison with the $n$ -grams and the mixed-order Markov models, the sparse hyperdimensional predictor clearly outperforms its peers in terms of unweighted average recall and achieves an equal level of weighted average recall as the mixed-order Markov chain but without the batch training of the mixed-order model.

Video streaming method

Abstract: The invention relates to a video streaming method, comprising receiving a panoramic video; receiving head-tracking data; determining a current field of view based on the head-tracking data. The resolution of the panoramic video may be adjusted based on the current field of view, wherein resolution of the current field of view is a first resolution and the resolution outside of the current field of view is a second resolution. The panoramic video with adjusted resolution may be provided, wherein the first resolution is higher than the second resolution.

All-Digital Calibration of Timing Skews for TIADCs Using the Polyphase Decomposition

Abstract: This brief proposes a new all-digital calibration technique suppressing the timing mismatch effect in time-interleaved analog-to-digital converters (TIADCs) for input at any Nyquist band (NB) using the equivalent polyphase structure of the TIADC. The correction technique is simple and does not require the adaptive digital synthesis filters. The timing mismatch is estimated based on an adaptive stochastic gradient descent technique, which is a promising solution for TIADCs operating at a very fast sampling rate. The digital circuit of the proposed calibration algorithm is designed and synthesized using a 28-nm fully depleted Silicon on insulator (FD-SOI) CMOS technology for the 11-b 60-dB SNR TIADC clocked at 2.7 GHz with the input in the first four NBs. The designed circuit occupies the area of 0.05 mm2 and dissipates the total power of 41 mW.

Superfluidity: a flexible functional architecture for 5G networks

Abstract: We propose the innovative architecture of Superfluidity, a Horizon 2020 project, co-funded by the European Union. Superfluidity targets 5G networks, by addressing key network operator challenges with a multi-pronged approach, based on the concept of a flexible, highly adaptive, superfluid network. Superfluidity supports rapid service deployment and migration in a heterogeneous network environment, regardless of the underlying hardware. The overall proposal offers advanced capabilities in terms of service deployment and interoperability, while guaranteeing high-performance levels end-to-end. Copyright © 2016 John Wiley & Sons, Ltd.

A Prediction Study of Path Loss Models from 2-73.5 GHz in an Urban-Macro Environment

Abstract: It is becoming clear that 5G wireless systems will encompass frequencies from around 500 MHz all the way to around 100 GHz. To adequately assess the performance of 5G systems in these different bands, path loss (PL) models will need to be developed across this wide frequency range. The PL mod-els can roughly be broken into two categories, ones that have some anchor in physics, and ones that curve- match only over the data set without any physical anchor. In this paper we use both real-world measurements from 2 to 28 GHz and ray-tracing studies from 2 to 73.5 GHz, both in an urban-macro environ-ment, to assess the prediction performance of the two PL model-ing techniques. In other words, we look at how the two different PL modeling techniques perform when the PL model is applied to a prediction set which is different in distance, frequency, or environment from a measurement set where the parameters of the respective models are determined. We show that a PL model with a physical anchor point can be a better predictor of PL per- formance in the prediction sets while also providing a parameter-ization which is more stable over a substantial number of differ-ent measurement sets.

Localization of LTE measurement records with missing information

Abstract: As cellular networks like 4G LTE networks get more and more sophisticated, mobiles also measure and send enormous amount of mobile measurement data (in TBs/week/metropolitan) during every call and session. The mobile measurement records are saved in data center for further analysis and mining, however, these measurement records are not geo-tagged because the measurement procedures are implemented in mobile LTE stack. Geo-tagging (or localizing) the stored measurement record is a fundamental building block towards network analytics and troubleshooting since the measurement records contain rich information on call quality, latency, throughput, signal quality, error codes etc. In this work, our goal is to localize these mobile measurement records. Precisely, we answer the following question: what was the location of the mobile when it sent a given measurement record? We design and implement novel machine learning based algorithms to infer whether a mobile was outdoor and if so, it infers the latitude-longitude associated with the measurement record. The key technical challenge comes from the fact that measurement records do not contain sufficient information required for triangulation or RF fingerprinting based techniques to work by themselves. Experiments performed with real data sets from an operational 4G network in a major metropolitan show that, the median accuracy of our proposed solution is around 20 m for outdoor mobiles and outdoor classification accuracy is more than 98%.

Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features

Abstract: The essence of image-based localization lies in matching 2D key points in the query image and 3D points in the database. State-of-the-art methods mostly employ sophisticated key point detectors and feature descriptors, e.g., Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT), to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast. The naive usage of binary features, however, does not lend itself to significant speedup of localization, since existing indexing approaches, such as hierarchical clustering trees and locality sensitive hashing, are not efficient enough in indexing binary features and matching binary features turns out to be much slower than matching SIFT features. To overcome this, we propose a much more efficient indexing approach for approximate nearest neighbor search of binary features. This approach resorts to randomized trees that are constructed in a supervised training process by exploiting the label information derived from that multiple features correspond to a common 3D point. In the tree construction process, node tests are selected in a way such that trees have uniform leaf sizes and low error rates, which are two desired properties for efficient approximate nearest neighbor search. To further improve the search efficiency, a probabilistic priority search strategy is adopted. Apart from the label information, this strategy also uses non-binary pixel intensity differences available in descriptor extraction. By using the proposed indexing approach, matching binary features is no longer much slower but slightly faster than matching SIFT features. Consequently, the overall localization speed is significantly improved due to the much faster key point detection and descriptor extraction. It is empirically demonstrated that the localization speed is improved by an order of magnitude as compared with state-of-the-art methods, while comparable registration rate and localization accuracy are still maintained.

mSieve: differential behavioral privacy in time series of mobile sensor data

Abstract: Differential privacy concepts have been successfully used to protect anonymity of individuals in population-scale analysis. Sharing of mobile sensor data, especially physiological data, raise different privacy challenges, that of protecting private behaviors that can be revealed from time series of sensor data. Existing privacy mechanisms rely on noise addition and data perturbation. But the accuracy requirement on inferences drawn from physiological data, together with well-established limits within which these data values occur, render traditional privacy mechanisms inapplicable. In this work, we define a new behavioral privacy metric based on differential privacy and propose a novel data substitution mechanism to protect behavioral privacy. We evaluate the efficacy of our scheme using 660 hours of ECG, respiration, and activity data collected from 43 participants and demonstrate that it is possible to retain meaningful utility, in terms of inference accuracy (90%), while simultaneously preserving the privacy of sensitive behaviors.

Graphene screen-printed radio-frequency identification devices on flexible substrates

Abstract: Despite the great promise of printed flexible electronics from2D crystals, and especially graphene, few scalable applica-tions have been reported so far that can be termed roll-to-rollcompatible. Here we combine screen printed graphene withphotonic annealing to realize radio-frequency identificationdevices with a reading range of up to 4 meters. Most notablyour approach leads to fatigue resistant devices showing lessthan 1% deterioration of electrical properties after 1000 bend-ing cycles. The bending fatigue resistance demonstrated on avariety of technologically relevant plastic and paper sub-strates renders the material highly suitable for various print-able wearable devices, where repeatable dynamic bendingstress is expected during usage. All applied printing and post-processing methods are compatible with roll-to-roll manufac-turing and temperature sensitive flexible substrates providinga platform for the scalable manufacturing of mechanicallystable and environmentally friendly graphene printed electronics.

A Multipath Extraction-Based CSI Acquisition Method for FDD Cellular Networks With Massive Antenna Arrays

Abstract: Unlocking the true potential of massive antenna arrays for cellular networks operating in frequency division duplex (FDD) mode is a challenging task as inadequate channel state information (CSI) by conventional limited-size UE feedback forces crude beamforming in downlink. This paper develops a new technique for the CSI acquisition problem of FDD massive multiple-input multiple-output (MIMO) networks based on estimating the reciprocal characteristics of the multipath channel directly from uplink, while merely the nonreciprocal properties of the dominant propagation paths are estimated and fed back from UE. The main benefits of this technique over compressive-sensing methods include allowing single-beam pilot transmission during downlink training and relaxed UE-side computational complexity, as the full knowledge of CSI vector is no longer required at UE. The design can be accomplished by measuring the direction-of-arrival information and average powers of the dominant paths from uplink, indicating the target dominant paths to corresponding UEs, estimating and subsequently signaling the random phase information of the target paths from each UE, and reconstructing the aggregate CSI vector at eNB. The numerical evaluation results demonstrate that the proposed mechanism offers promising potential for reliable FDD operation in next generation cellular networks equipped with massive antenna arrays.

Virtual realities and education

Abstract: The purpose of this paper is to highlight the state-of-the-art of virtual reality, augmented reality mixed reality technologies, and their applications in formal education. We also present a select ...

A Scalable and Flexible Radio Access Network Architecture for Fifth Generation Mobile Networks

Abstract: The fifth generation of mobile networks is expected to become the key enabling technology for new services and businesses in the Internet of Things realm, including automotive and industry communications. At the same time, the demand for the “bread and butter” services of mobile broadband will continue to increase, driving the need for ubiquitous data capacity everywhere. Compared to 3GPP LTE, fifth generation radio access networks need to support much more diverse requirements with a wide range of deployment options for infrastructure and spectrum availability. Based on this insight, this article discusses a flexible and scalable radio access network architecture design.