Periodic Reporting for period 2 - eWINE (elastic WIreless Networking Experimentation)
Okres sprawozdawczy: 2017-01-01 do 2018-04-30
The project aims to solve connectivity issues for wireless devices at mass events or situations for technologies such as cellular (2G–3G–4G), WiFi technologies, Bluetooth and others. The configuration of these networks typically needs intense manual work. As a result, networks are often not very efficient in terms of energy and also relatively static, unable to adapt should the preplanned capacity be exceeded. In addition, the performance of the planned network in non-licensed bands yields inferior quality when cross-technology interference occurs.
eWINE will have a major social impact for society through its development of the next generation wireless products and applications for a broad range of applications domains, from public safety to the home environment by the development and ad hoc deployment of networks with smart and dynamic configuration capabilities that support public safety at large ad hoc events, such as sports events, city events and world congresses. In regards to the environment, eWINE's impact is as follows: much higher efficiency in terms of energy consumption and electromagnetic exposure, further optimizations and intelligent configuations through the introduction of planning and learning strategies into the cognitive loop and reduction of energy consumption while increasing comfort in the home environment through intelligent and flexible control systems.
The overall objectives are to enable on demand end to end basic wireless connectivity capable of delivering (i) elastic connectivity services from an Over-The-Top (OTT) service provider to fixed and mobile wireless devices within dense network scenarios, (ii) enable elastic resource sharing in dense deployments of heterogeneous and small cell networks (HetSNets) in a dynamic way to provide Quality of Service to the many wireless services running on the wireless devices, (iii) develop and evaluate an open and reconfigurable physical layer that is able to intelligently adapt radio parameters to the current network and application context and (iv) lastly, educate the research community, industrial professionals as well as the general public to increase awareness of problems that occur in dense and dynamic wireless networks and possible solutions to these, thus promoting the uptake of the project results by providing an Intelligence Toolbox (i.e. an open source repository of the software components developed in the project and tested in the showcases).
eWINE will have a major social impact for society through its development of the next generation wireless products and applications for a broad range of applications domains, from public safety to the home environment by the development and ad hoc deployment of networks with smart and dynamic configuration capabilities that support public safety at large ad hoc events, such as sports events, city events and world congresses. In regards to the environment, eWINE's impact is as follows: much higher efficiency in terms of energy consumption and electromagnetic exposure, further optimizations and intelligent configuations through the introduction of planning and learning strategies into the cognitive loop and reduction of energy consumption while increasing comfort in the home environment through intelligent and flexible control systems.
The overall objectives are to enable on demand end to end basic wireless connectivity capable of delivering (i) elastic connectivity services from an Over-The-Top (OTT) service provider to fixed and mobile wireless devices within dense network scenarios, (ii) enable elastic resource sharing in dense deployments of heterogeneous and small cell networks (HetSNets) in a dynamic way to provide Quality of Service to the many wireless services running on the wireless devices, (iii) develop and evaluate an open and reconfigurable physical layer that is able to intelligently adapt radio parameters to the current network and application context and (iv) lastly, educate the research community, industrial professionals as well as the general public to increase awareness of problems that occur in dense and dynamic wireless networks and possible solutions to these, thus promoting the uptake of the project results by providing an Intelligence Toolbox (i.e. an open source repository of the software components developed in the project and tested in the showcases).
The work is progressing on schedule: all scheduled deliverables have been produced and the respective milestones have been met. From the beginning of the project, work has progressed on the development of the Showcases which target a specific HetSNets challenge at different scales, from the PHY layer to over the top communication over heterogeneous technologies. All partners have provided detailed functional and technical specifications for Showcase 1, 2 and 3. Some examples include: 1) Standardized localization interface; 2) Intuitive flow-based drag-and-drop for wireless experimentation and 3) Flexible FPGA PHY implementation. Partners also worked on the Demonstrators. Some examples include: 1) Intuitive flow-based drag-and-drop for wireless experimentation; 2) Scampi-WiSHFUL integration for Device-to-device (D2D) communication and 3) SubGHz dense network optimisation. In Context provisioning and sensing algorithms design and provisioning modules, the Demonstrator and software components for drag and drop wireless experimentation were created for Showcase 2, D2D communication in LTE networks and algorithm design of collecting context information for D2D communication were performed complimenting Showcase 2, outdoor localization using data from Log-a-Tec testbed and indoor localization using UWB measurements and LOS/NLOS classification were worked on as well as location forecast using clustering algorithms and Mobility Markov Chain for Showcase 1. Work was also performed on spectrum sensor development for ultra-narrowband transmissions and context data collection for ultra- narrowband communications for Showcase 2.
In the area of Intelligent Algorithm Design, Development, Implementation, partners worked on the creation of the demonstrator about SUMO optimization for the wireless audio conferencing scenario and the creation of prediction algorithms and datasets for the intelligence repository for Showcase 2; worked on the optimization algorithm design on power-aware mode selection between D2D communication mode and infrastructure mode in LTE networks and the implementation of the D2D Physical channels with compliance to 3GPP LTE standard in Software-defined-radio testbed, tested and prepared the demonstrator for Showcase 1, worked on the Flexible FPGA PHY layer implementation (GFDM), waveform parameter optimization, and efficient beam-alignment algorithm for mmWave links which were included in Showcase 3, contributed to the early work on channel selection as a component for Showcase 2, worked on the Manycore optimizations of the OFDM modem (WiFi-like waveform) Tx. Multithreading, Thread Pool and timing performance measurements (profiling) to show the results for Showcase 3.
In the area of Intelligent Algorithm Design, Development, Implementation, partners worked on the creation of the demonstrator about SUMO optimization for the wireless audio conferencing scenario and the creation of prediction algorithms and datasets for the intelligence repository for Showcase 2; worked on the optimization algorithm design on power-aware mode selection between D2D communication mode and infrastructure mode in LTE networks and the implementation of the D2D Physical channels with compliance to 3GPP LTE standard in Software-defined-radio testbed, tested and prepared the demonstrator for Showcase 1, worked on the Flexible FPGA PHY layer implementation (GFDM), waveform parameter optimization, and efficient beam-alignment algorithm for mmWave links which were included in Showcase 3, contributed to the early work on channel selection as a component for Showcase 2, worked on the Manycore optimizations of the OFDM modem (WiFi-like waveform) Tx. Multithreading, Thread Pool and timing performance measurements (profiling) to show the results for Showcase 3.
Progress beyond the state of the art has been achieved by working on novel algorithms and solutions. Some examples include novel interference classifications techniques, the first reconfigurable Software-defined Radio implementation on LTE Device-to-device communication that is compliant with 3GPP LTE standardization, a framework for infrastructure assisted IEEE 802.11 D2D communication, high level localization service architecture acting as a middle-ware for diverse sources of location information, the first real-time PHY FPGA of the flexible GFDM waveform generation framework implemented, contributions to the Next Generation Hardware by extending already existing CREW and FLEX facilities with a hardware enabler supporting flexible waveform generation, definition of an information structure for context provisioning, context acquisition and processing with the link quality estimation, development of low-power wide area networks (LP-WANs),successful dissemination efforts by identifying multichannel-approaches, providing the first subscription service for open-source LTE protocol stacks (aLTErnative), defined a way to implement SIGFOX protocol on the VESNA platform, designed and implemented a framework for intelligence driven D2D over-the-top content sharing incorporating the Scampi platform.
The socio-economic impact and wider implications are seen by producing a far more efficient operation in dense heterogeneous environments with a energy efficiency which positively impacts the environment, costs which can be further reduced when utilizing WiFi D2D, increased capacity and improved battery consumption in end-user terminals, a simplified integration of applications with services that can provide location information which reduces the costs and efforts of enabling location-awareness in wireless networks, QoS improvement and more energy efficient devices and electrical power consumption, reduction of the installation complexity, increase in the global network capacity, and improving search and rescue capabilities.
The socio-economic impact and wider implications are seen by producing a far more efficient operation in dense heterogeneous environments with a energy efficiency which positively impacts the environment, costs which can be further reduced when utilizing WiFi D2D, increased capacity and improved battery consumption in end-user terminals, a simplified integration of applications with services that can provide location information which reduces the costs and efforts of enabling location-awareness in wireless networks, QoS improvement and more energy efficient devices and electrical power consumption, reduction of the installation complexity, increase in the global network capacity, and improving search and rescue capabilities.