Showing papers by "Nokia published in 2018"

Graphene-based integrated photonics for next-generation datacom and telecom

Abstract: Graphene is an ideal material for optoelectronic applications. Its photonic properties give several advantages and complementarities over Si photonics. For example, graphene enables both electro-absorption and electro-refraction modulation with an electro-optical index change exceeding 10−3. It can be used for optical add–drop multiplexing with voltage control, eliminating the current dissipation used for the thermal detuning of microresonators, and for thermoelectric-based ultrafast optical detectors that generate a voltage without transimpedance amplifiers. Here, we present our vision for graphene-based integrated photonics. We review graphene-based transceivers and compare them with existing technologies. Strategies for improving power consumption, manufacturability and wafer-scale integration are addressed. We outline a roadmap of the technological requirements to meet the demands of the datacom and telecom markets. We show that graphene-based integrated photonics could enable ultrahigh spatial bandwidth density, low power consumption for board connectivity and connectivity between data centres, access networks and metropolitan, core, regional and long-haul optical communications.

Collaborative Energy and Information Transfer in Green Wireless Sensor Networks for Smart Cities

Abstract: Smart city is able to make the city source and infrastructure more efficiently utilized, which improves the quality of life for citizens. In this framework, wireless sensor networks (WSNs) play an important role to collect, process, and analyze the corresponding information. However, the massive deployment of WSNs consumes a significant energy consumption, which has raised the growing demand for green WSNs for smart cities. Exploiting the recent advance in collaborative energy and information transfer to power the WSNs and transmit the data has been considered a promising approach to realize the green WSNs for smart cities. We propose an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrate the challenging issues in this design. To achieve a green system design, the sensor nodes in WSNs harvest the energy simultaneously with the information decoding (ID) from the received radio frequency signals. Specifically, the energy-constrained sensor nodes partition the received signals into two independent groups to perform energy harvesting (EH) and ID. The sensor nodes then use the harvested energy to amplify and forward the information signals. We study the joint optimization of subcarrier grouping, subcarrier pairing, and power allocation such that the transmission rate performance is maximized with the EH constraint. The joint optimization problem is solved via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results tested with the real WSNs system data indicate that the performance of our proposed protocol can be significantly improved.

Chargrid: Towards Understanding 2D Documents.

Abstract: We introduce a novel type of text representation that preserves the 2D layout of a document. This is achieved by encoding each document page as a two-dimensional grid of characters. Based on this representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images.

Clustering and Unsupervised Anomaly Detection with l 2 Normalized Deep Auto-Encoder Representations

Abstract: Clustering is essential to many tasks in pattern recognition and computer vision. With the advent of deep learning, there is an increasing interest in learning deep unsupervised representations for clustering analysis. Many works on this domain rely on variants of auto-encoders and use the encoder outputs as representations/features for clustering. In this paper, we show that an l 2 normalization constraint on these representations during auto-encoder training, makes the representations more separable and compact in the Euclidean space after training. This greatly improves the clustering accuracy when k-means clustering is employed on the representations. We also propose a clustering based unsupervised anomaly detection method using l 2 normalized deep auto-encoder representations. We show the effect of l 2 normalization on anomaly detection accuracy. We further show that the proposed anomaly detection method greatly improves accuracy compared to previously proposed deep methods such as reconstruction error based anomaly detection.

Cost-Optimized Submarine Cables Using Massive Spatial Parallelism

Abstract: We study the techno-economics of submarine systems constrained by a fixed electrical power supply. We show significant cost savings for high-capacity submarine systems using massive space-division multiplexing (SDM), even without assuming any savings from SDM-specific subsystem integration. Systems with about 100 parallel optical paths, e.g., $\sim$ 50 fiber pairs are shown to provide minimum cost/bit, operating at reduced spectral efficiencies and deep within the linear regime. While advanced nonlinearity-optimized fibers and digital nonlinearity compensation schemes provide little to no gain in such systems, SDM integration of amplifiers and transponders is shown to be a source for significant additional cost savings. We further examine the permissible cost premium for multicore fibers in such massively parallel systems and revisit various design tradeoffs for optical amplifiers, showing that a reduced noise figure can be traded for better power conversion efficiency. We also evaluate potential gains from increasing the available electrical supply power and discuss reliability aspects of massively parallel submarine systems.

Trans-Atlantic Field Trial Using High Spectral Efficiency Probabilistically Shaped 64-QAM and Single-Carrier Real-Time 250-Gb/s 16-QAM

Abstract: We report the transmission of probabilistically shaped (PS) 64-ary quadrature amplitude modulation (QAM) at 7.46 b/s/Hz over a 5523-km in-service trans-Atlantic fiber-optic cable that consists of 65–89-km spans of Erbium-doped fiber amplifier only amplified fiber. Using a looped-back system configuration, we achieve 5.68 b/s/Hz over a trans-Pacific-equivalent distance of 11 046 km. Net spectral efficiencies are increased by 18% and 80% by using PS, at 5523 km and 11 046 km, respectively, compared to uniform square QAM. Throughout our experiments, we pay particular attention that our claims are backed by implementable forward error correction schemes. In addition, we demonstrate real-time coherent transmission of single-carrier 200 and 250-Gb/s uniform 8-QAM and 16-QAM at 4 b/s/Hz over the 5523-km cable.

Chargrid: Towards Understanding 2D Documents

Abstract: We introduce a novel type of text representation that preserves the 2D layout of a document. This is achieved by encoding each document page as a two-dimensional grid of characters. Based on this representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images.

Advanced Industrial Wireless Sensor Networks and Intelligent IoT

Abstract: Examines the market for wireless sensor networks in the era and expansion of the Internet of Things Over the past decade, the fast expansion of the Internet of Things (IoT) paradigm and wireless communication technologies has raised many scientific and engineering challenges that call for ingenious research efforts from both academia and industry The IoT paradigm now covers several technologies beyond RFID and wireless sensor networks (WSNs) In fact, the number of potential application fields has already exceeded expectations According to Cisco IBSG, more than 50 billion devices are expected to be connected to the Internet by 2020, with around 20 percent from the industry sector Therefore, integrating the IoT concept and industrial WSNs (IWSNs) is an attractive choice for industrial processes, which may optimize operational efficiency, automation, maintenance, and rationalization Moreover, IoT ensures large-scale interconnection between machines, computers, and people, enabling intelligent industrial operations This emergent technological evolution has led to what has become the Industrial IoT (IIoT) IIoT will bring promising opportunities, along with new challenges

Comparison of photoplethysmogram measured from wrist and finger and the effect of measurement location on pulse arrival time.

Abstract: Objective The aim of this paper was to compare photoplethysmogram (PPG) signals measured from the wrist and finger and to evaluate if wrist PPG signal could be used to calculate pulse arrival time (PAT), the time delay between electrocardiogram (ECG) R peak and a feature (e.g. peak, foot, first derivative peak) in the PPG signal. Further, the correlation between pulse wave velocity (rePWV, defined as PWV from ECG R peak to extremity) and systolic blood pressure was studied. Approach Thirty subjects were measured at rest by a trained research nurse. For reference measurement, chest ECG and finger PPG were measured using commercial sensors. Wrist PPG and arm ECG were measured with a custom-made setup, where the PPG sensor was located at the back surface of the forearm. Main results Reference finger and wrist PPG signals were found to differ in shape and also in amplitude. The PPG foot or first derivative peak detection methods seemed to be the most suitable methods for wrist PAT calculation. The Pearson correlation coefficient between blood pressure and rePWV was found to be 0.44 for the reference finger measurement and 0.37 for the wrist measurement. Significance Wrist PPG signal is widely used in optical heart rate monitors. Based on the results obtained in this study, wrist PPG signal may be used also for PAT calculation. The use of PAT for blood pressure estimation still has challenges, but PAT as such could be used as an interesting indicator of vascular health.

A Coarse-to-Fine Algorithm for Matching and Registration in 3D Cross-Source Point Clouds

Abstract: We propose an efficient method to deal with the matching and registration problem found in cross-source point clouds captured by different types of sensors. This task is especially challenging due to the presence of density variation, scale difference, a large proportion of noise and outliers, missing data, and viewpoint variation. The proposed method has two stages: in the coarse matching stage, we use the ensemble of shape functions descriptor to select potential K regions from the candidate point clouds for the target. In the fine stage, we propose a scale embedded generative Gaussian mixture models registration method to refine the results from the coarse matching stage. Following the fine stage, both the best region and accurate camera pose relationships between the candidates and target are found. We conduct experiments in which we apply the method to two applications: one is 3D object detection and localization in street-view outdoor (LiDAR/VSFM) cross-source point clouds and the other is 3D scene matching and registration in indoor (KinectFusion/VSFM) cross-source point clouds. The experiment results show that the proposed method performs well when compared with the existing methods. It also shows that the proposed method is robust under various sensing techniques, such as LiDAR, Kinect, and RGB camera.

Boosting 5G Through Ethernet: How Evolved Fronthaul Can Take Next-Generation Mobile to the Next Level

Abstract: Current approaches to the fronthaul for centralized, or cloud, radio access networks (C-RANs) need to be revised to meet the requirements of next-generation mobile networks. There are two major challenges. First, fronthaul signals need to be transported over public fixed-access networks, such as passive optical networks (PONs), typically sharing them with other services. Second, higher data rates must be handled because of larger radio bandwidths and a greater use of multiantenna techniques, such as massive multiple input, multiple output (MIMO). Using Ethernet as a new transport protocol for the fronthaul allows statistical multiplexing and enables convergence between fixed and mobile services.

Object Detection in Equirectangular Panorama

Abstract: We introduce a high-resolution equirectangular panorama (aka 360-degree, virtual reality, VR) dataset for object detection and propose a multi-projection variant of the YOLO detector. The main challenges with equirectangular panorama images are i) the lack of annotated training data, ii) high-resolution imagery and iii) severe geometric distortions of objects near the panorama projection poles. In this work, we solve the challenges by I) using training examples available in the “conventional datasets” (ImageNet and COCO), II) employing only low resolution images that require only moderate GPU computing power and memory, and III) our multi-projection YOLO handles projection distortions by making multiple stereographic sub-projections. In our experiments, YOLO outperforms the other state-of-the-art detector, Faster R-CNN, and our multi-projection YOLO achieves the best accuracy with low-resolution input.

Creating value through blockchain powered resource configurations: Analysis of 5G network slice brokering case

Abstract: The exponential growth of wireless services with diversity of devices and applications has inspired the research community to come up with novel concepts to improve the efficiency of resource use. It is cost efficient and practical to be able to evaluate and analyze expected value of use cases before actual implementation of the solution. This paper presents blockchain network slice brokering use case value analysis and results in the industrial automation application scenario. The use case value was assessed applying resource configuration framework against blockchain and smart contracts characteristics and capabilities. According to the findings, expected value of blockchain network slice brokering use case was proven and in general feasibility of blockchain technology for novel resource configurations use cases for various network functions was stated. The use case enables industrial automation processes and related manufacturing equipment to autonomously and dynamically acquire the slice needed for most efficient operations. The resource configuration framework provides a dynamic framework for analyzing and developing the blockchain enabled novel use cases and business models.

Business Models for Local 5G Micro Operators

Abstract: 5G will change the mobile communication business ecosystem by introducing location specific high-quality wireless networks that can by operated by different stakeholders. This development will change the traditional business models and ecosystem roles, as well as open the market for new local mobile network operators. These operators, such as recently introduced micro operators, can target specific customers in different vertical sectors with closed 5G networks, serve mobile network operator’s (MNO) customers in high-demand areas on behalf of the MNO as a neutral host with open 5G networks, or mix different types of customers and offerings through various hybrid business models. This paper discusses business model options for local 5G micro operators, addressing also the different network deployment options. Three generic 5G business models and respective value ecosystems are presented: Vertical business model and ecosystem, Horizontal business model and ecosystem, and Oblique business model and ecosystem. Finally, the scalability, adaptability and sustainability of the business models and ecosystems are examined.

3GPP LTE-WLAN Aggregation Technologies: Functionalities and Performance Comparison

Abstract: The use of license-exempt bands offers a promising opportunity to additionally enhance operator networks to meet the future traffic demand. The recent specification efforts in 3GPP have resulted in two major aggregation features that enable LTE networks to benefit from unlicensed spectrum via WLAN. In this article, we provide a thorough overview of these features known as, LTE-WLAN aggregation (LWA) and LTE-WLAN radio level integration with IP security tunnel (LWIP). The article presents and motivates the design choices of protocol architectures, procedures, mobility, and security. It also proposes flow control algorithms suitable for both technologies, which aim at the best usage of licensed and unlicensed spectrum. Simulation results show the performance unveiling the potential gains of these features in different load conditions, also in a comparative manner, showing that LWA substantially outperforms LWIP.

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography.

Abstract: The feasibility of data based machine learning applied to ultrasound tomography is studied to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, a high-order discontinuous Galerkin method is considered, while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, the material porosity and tortuosity is estimated, while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirm the feasibility and accuracy of this approach.

The Essential Guide to Realizing 5G-Connected UAVs with Massive MIMO.

Abstract: What will it take for drones -- and the whole associated ecosystem -- to take off? Arguably, infallible command and control (C&C) channels for safe and autonomous flying, and high-throughput links for multi-purpose live video streaming. And indeed, meeting these aspirations may entail a full cellular support, provided through 5G-and-beyond hardware and software upgrades by both mobile operators and manufacturers of these unmanned aerial vehicles (UAVs). In this article, we vouch for massive MIMO as the key building block to realize 5G-connected UAVs. Through the sheer evidence of 3GPP-compliant simulations, we demonstrate how massive MIMO can be enhanced by complementary network-based and UAV-based solutions, resulting in consistent UAV C&C support, large UAV uplink data rates, and harmonious coexistence with legacy ground users.

Mobility Challenges for Unmanned Aerial Vehicles Connected to Cellular LTE Networks

Abstract: Mobility performance is at the heart of each cellular technology, including omni-present LTE networks It becomes even more significant when such system is supposed to reliably support state-of-the-art devices, such as Unmanned Aerial Vehicles (UAVs), emerging at rapid pace in our surroundings Contemporary network deployments, addressing mainly the needs of terrestrial users, were not optimized to serve UAVs moving below and above the typical cellular antenna heights Thus, it is worth checking how they can handle these novel and distinctive services, also with respect to mobility robustness This paper elaborates on the challenges ahead which must be tackled to make existing LTE networks sufficiently capable of accommodating Aerial Vehicles We present simulation results in various environments, obtained for different user equipment's (UE's) heights and velocities Furthermore, we describe and analyze the identified issues Finally, we also suggest potential optimizations and future research goals

A Longitudinal Study of Predictors for Adolescent Electronic Cigarette Experimentation and Comparison with Conventional Smoking

Abstract: Little is known of the predictors of electronic cigarette (e-cigarette) use among adolescents, even though the use is increasing We studied here the predictors for e-cigarette experimentation (tried and tried more than twice) and compared them with predictors for conventional smoking A baseline school survey was conducted in the Helsinki metropolitan area, Finland, in 2011 for seventh graders (12 to 13-year-olds) Response rate was 73% The same students were followed up in 2014 (9th grade, 15 to 16-year-olds), N = 5742 Generalized linear mixed models controlling for school clustering were used In the follow-up, 433% of boys and 256% of girls had tried e-cigarettes and 219% and 81% correspondingly more than twice The strongest predictors for both genders were conventional smoking, drunkenness and energy drink use Furthermore, poor academic achievement predicted e-cigarette experimentation for both genders, and for boys, participation in team sports was a predictor The predictors for experimenting and for experimenting more than twice were very similar, except for boys' participation in team sports They were also similar compared to the predictors of conventional smoking but the associations were weaker To conclude, smoking and other addictive behaviors predict adolescents' experimentation with e-cigarettes Family's socioeconomic background had little significance

On the development of IoT systems

Abstract: A typical IoT system consists of four distinct architectural elements: devices, gateways, cloud and apps. All these elements require special skills in their development. In order to write safe, secure IoT systems, developers must be trained and experienced in four different areas of software development: embedded, cloud, web and mobile. In addition, given the distributed nature of IoT systems, distributed programming skills play a critical role. In this paper we examine the challenges in IoT system development, and summarize our observations and experiences on the necessity and co-presence of different types of software development skills in the design of IoT systems.

Prognostic Factors for Survival and Relapse in ANCA-Associated Vasculitis with Renal Involvement: A Clinical Long-Term Follow-Up Study

Abstract: Aim. We describe the clinical pattern of ANCA-associated vasculitis (AAV) and assess long-term prognostic factors of patients and renal survival and relapse. Methods. Data from 85 patients with renal biopsy-proven AAV at a single center with up to 20-year [median 16.2 years (95% CI 14.9-17.7)] follow-up were retrospectively collected. Results. Overall, 55% of the patients had microscopic polyangiitis (MPA) and 45% had granulomatosis with polyangiitis (GPA). The histopathological classes were focal in 35%, crescentic in 26%, mixed in 20%, and sclerotic glomerulonephritis in 19% of the patients. As induction treatment, a combination of cyclophosphamide and corticosteroids was given to 82%, while a combination of azathioprine and corticosteroids was maintenance therapy in 79%. The twenty-year patient survival was 45%. In a multivariable analysis, age ≥58 years [hazard ratio (HR) 7.64, 95% CI 3.44-16.95] and myeloperoxidase (MPO) ANCA (HR 2.12, 95% CI 1.08-4.17) were associated with shorter patient survival time. Renal survival was 68% overall: 88% in focal, 71% in crescentic, 56% in mixed, and 37% in sclerotic class (p=0.01). Female sex (HR 0.26, 95% CI 0.10-0.73) was a predictor of improved renal survival, whereas GFR <30 ml/min and MPO-ANCA were associated with worse renal survival (HR 4.10, 95% CI 1.35-12.49 and HR 3.10, 95% CI 1.21-7.95, respectively). Relapse-free survival at 20 years was 10%. MPA was associated with a lower risk for relapse (HR 0.48, 95% CI 0.28–0.82). Conclusion. We confirmed the improved patient and renal survival in AAV patients with glomerulonephritis, while relapse remained the primary challenge. Histopathological classification may be relevant for survival.

A Taxonomy of IoT Client Architectures

Abstract: This article defines a taxonomy of software architecture options, derived from industry projects, for Internet of Things (IoT) devices, from the most limited sensing devices to high-end devices featuring fully fledged OSs and developer frameworks. A plethora of architecture options exists for IoT devices, offering very different levels of software development capabilities. These capabilities can significantly affect IoT systems’ end-to-end architecture and topology.

Ultrawideband Wavelength-Tunable Hybrid External-Cavity Lasers

Abstract: We demonstrate two novel hybrid lasers with the same III–V ultrawideband gain medium. An hybrid III–V/Si laser and its III–V/SiO2 counterpart exhibit high fiber-coupled output power without booster semiconductor optical amplifier (up to +13 dBm for III–V/Si and +10 dBm for III–V/SiO2) and a record tuning range (respectively, 95 nm and 66 nm) over the C and L bands with side mode suppression ratio greater than 35 dB.

Bankruptcy game based resource allocation algorithm for 5G Cloud-RAN slicing

Abstract: Recently, network slicing which addresses the deployment of multiple logical networks as independent business operations on a common physical infrastructure has attracted more and more attentions. Despite the advances of network slicing, there are still many open issues regarding the isolation of the wireless slices and resources allocation. The paper focus on spectrum resource allocation for Cloud-RAN slices. We develop a bankruptcy game based algorithm to allocate resource for the Cloud-RAN slices. Cloud and slices are modeled to the bankrupt company and debtors in the game respectively, where Shapley value is adopted to obtain a stable solution. Simulation results show that the bankruptcy game based algorithm significantly improve resource utilization and guarantee the fairness of allocation.

Low Latency Edge Rendering Scheme for Interactive 360 Degree Virtual Reality Gaming

Abstract: This paper describes the core functionality and a proof-of-concept demonstration setup for remote 360 degree stereo virtual reality (VR) gaming. In this end-to-end scheme, the execution of a VR game is off-loaded from an end user device to a cloud edge server in which the executed game is rendered based on user's field of view (FoV) and control actions. Headset and controller feedback is transmitted over the network to the server from which the rendered views of the game are streamed to a user in real-time as encoded HEVC video frames. This approach saves energy and computation load of the end terminals by making use of the latest advancements in network connection speed and quality. In the showcased demonstration, a VR game is run in Unity on a laptop powered by i7 7820HK processor and GTX 1070 GPU. The 360 degree spherical view of the game is rendered and converted to a rectangular frame using equirectangular projection (ERP). The ERP video is sliced vertically and only the FoV is encoded with Kvazaar HEVC encoder in real time and sent over the network in UDP packets. Another laptop is used for playback with a HTC Vive VR headset. Our system can reach an end-to-end latency of 30 ms and bit rate of 20 Mbps for stereo 1080p30 format.

BAD: Blockchain Anomaly Detection.

Abstract: Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. Over the years, different approaches have been designed, all focused on lowering the false positive rate. However, no proposal has addressed attacks targeting blockchain-based systems. In this paper we present BAD: the first Blockchain Anomaly Detection solution. BAD leverages blockchain meta-data, named forks, in order to collect potentially malicious activities in the network/system. BAD enjoys the following features: (i) it is distributed (thus avoiding any central point of failure), (ii) it is tamper-proof (making not possible for a malicious software to remove or to alter its own traces), (iii) it is trusted (any behavioral data is collected and verified by the majority of the network) and (iv) it is private (avoiding any third party to collect/analyze/store sensitive information). Our proposal is validated via both experimental results and theoretical complexity analysis, that highlight the quality and viability of our Blockchain Anomaly Detection solution.

Rate Adaptation, Scheduling, and Mode Selection in D2D Systems With Partial Channel Knowledge

Abstract: Device-to-device (D2D) communication enables simultaneous data transmissions by cellular users (CU) and D2D user pairs, but at the expense of additional interference between them. The literature on resource allocation in D2D systems often assumes that the base station (BS) has complete channel state information (CSI) about all the links between all the users in a cell. However, acquiring the CSI of cross links between the CUs and the D2D receivers is a critical bottleneck because the number of cross links is the product of the number of CUs and D2D pairs. We study a novel partial CSI model in which the overhead of feeding back the CSI of the cross links is much lower. For a cell with one D2D pair and multiple CUs, we propose a novel throughput-optimal joint mode selection, user scheduling, and rate adaptation policy that exploits information about the statistics of the cross links and incorporates inter-cell interference. We derive closed-form expressions for the feedback-conditioned goodput for the underlay mode, which drives this optimal policy. We also present extensions that incorporate user fairness, quantized CSI, and multiple D2D pairs and multiple subchannels.

Secure de-centralized domain name system

Abstract: A computer-implemented method for secure de-centralized domain name system, the method comprising: recording a domain registration transaction to a blockchain, the domain registration transaction comprising a domain name, a domain primary key corresponding to a domain public key and domain certificate information for a server node; recording a domain security transaction, comprising the domain public key, to the blockchain to generate a domain name record comprising the domain name, an associated IP address, the domain public key and the domain certificate information, wherein the domain security transaction being signed using the domain primary key; transmitting, by a client node, a domain name request to a domain name node; receiving, by the client node, a domain name response from the domain name node, the domain name response comprising the domain public key, the domain certificate information and the associated IP address retrieved from the domain name record of the blockchain; and initiating a secure communication between the client node and the server node using at least one of the domain public key and the domain certificate information.

An apparatus, a method and a computer program for omnidirectional video

Abstract: There are disclosed various methods, apparatuses and computer program products for video encoding and decoding. In some embodiments a single-time coded picture set is generated comprising first two or more coded pictures representing at least one input picture of a first time instance. The first two or more coded pictures represent regions of the at least one input picture with different resolutions so that a subset of the regions that cover a viewport has higher resolution, wherein the viewport represents a displayed portion of decoded regions of the single-time coded picture set.

Analysis of Spectrum Valuation Approaches: The Viewpoint of Local 5G Networks in Shared Spectrum Bands

Abstract: Radio spectrum is a scarce natural resource, whose efficient management calls for a thorough understanding of its value. Quite a big number of approaches have emerged for spectrum valuation based on different elements, some with such potentially high uncertainty as future profits, total cost of ownership and societal benefits. Spectrum valuation will be important for the upcoming spectrum decisions by the regulators to deploy 5G networks but will face a new situation, where the use of higher carrier frequencies inherently limits network operations to local areas. This paper analyses the existing spectrum valuation approaches and identifies the key elements to consider, when defining and assessing the value of spectrum especially in the context of future local 5G networks. An important aspect is that the growing pressure to open the mobile market for location specific 5G networks has resulted in new sharing-based models for spectrum access, to allow the emergence of entrant local 5G operators to serve different verticals. We will therefore characterize the identified spectrum valuation elements in the new context of new local 5G networks operating in shared spectrum bands. Our approach considers spectrum valuation for 5G from the perspectives of the different stakeholder roles including regulators, mobile network operators (MNOs) and entrant local 5G operators.