About our Testbeds
Sigint's R&D platforms have been developed in house and include channel modeling and wireless simulators, radio emulators and network testbeds and cover most of the layers of the OSI model. The platforms are used for internal research purposes and in numerous European and National R&D projects. Examples of their usage include but not limited to channel modeling, wireless planning, localization, QoS/QoE, routing, mobility and green radio networks.
​
Additionally, for testing and experimentation purposes, Sigint owns and operates small scale 802.16, 802.15.4 and 802.11 a/b/g/n networks. These networks can be easily reconfigured and redeployed elsewhere depending on the requirements.
Wireless Modeling
TruNET Wireless
TruNet, is a 3D wireless network planning simulator based on the 3DTruEM physics EM engine developed by Sigint. The success story of TruNET resulted into the establishment of spin of company Fractal Networks, which currently owns the IPR rights of the commercialized version. The simulator's standard output provides magnitude and phase information, Power Delay Profiles (PDP), Angle of Arrival (AoA), Angle of Departure (AoD) information, SNR, SNIR and C/I information. The simulator's core functionality is further extended through a number of communication modules and connectors.
Examples of these modules and connectors include MIMO modules, NS2/Matlab connectors for supporting higher layer simulations, WiFi/WiMAX modules, localisation modules that allow the creation of fingerprinting databases and their performance evaluation, Jammer modules, Emulator Modules, Geolocation database Modules for TV White Spaces network design etc. TruNet's main design environment includes an intuitive CAD and antenna designer which allows the user to quickly design and configure the wireless network under investigation.
Key Features
Simplified 3D Design Environment
Intuitive 3D design environment and associate functionality accelerates and simplifies the design and analysis of any
wireless network.
Simplified 3D Wireless Network Design
Unique 3D EM engine allows the estimation of signal parameters at multiple layers in a singe simulation run thus accelerating the simulation process time.
Ultrawideband
studies
An UWB radio addon module allows the design and analysis of UWB radio networks.
Importing Terrain and complex structures
Ability to easily import and handle terrain data, urban building overlays and complex structures.
MIMO Antenna
Deployment
Further to the standard antenna designer, TruNET Wireless provides the ability to design and deploy complex MIMO antenna structures to allow the analysis of modern MIMO communication systems.
Creation and deployment of ‘Transceiver’ elements
Unique design abilities allow the construction of ‘transceiver’ elements that can be used to simulate ad-hoc / mesh networks or standard infrastructure.
-
Wireless network design
-
EMF studies (Radio)*
-
Localisation*
-
UWB*
-
MAC/System Simulation**
Positioning Platforms
Sigint is developing an innovative experimental Positioning as a Service (PaaS) platform able to provide decentralized, high accuracy 3D indoor positioning Services (< 0.5m) for personnel and asset tracking.
PaaS platform will be a fully-fledged localisation system, compliant with 3GPP standards for industry 4.0 and industry 5.0, incorporating various new technologies. Such a service can be potentially available for utilization in several types of management and monitoring platforms such as Crisis Management, Command and Control (C2) Security/Monitoring platforms in multi-floor or underground critical infrastructures such as oil and gas refineries, mines, chemical factories, hospitals etc. The service can also be of great interest for indoor navigation in large industrial complexes and/or marketing and promotional platforms in commercial buildings such as shopping malls or recreational theme parks etc.
PaaS platform utilizes 5G NR and 6G infrastructure, fuse information retrieved from heterogeneous wireless technologies such as 5G NR, Wi-Fi 6, Wi-Fi 7, IoT or other WSN, BLE or other types of sensors and process data using ML/AI positioning algorithms to provide high accuracy 3D positioning.
Pass Platform Features
A fully-fledged localization system, compliant with 3GPP standards for industry 4.0 and industry 5.0. This will incorporate various technologies such as BLE, Wi-Fi and WSN. In this system the team will develop and deploy the following back end and front-end applications:
​​
Back-end:
-
A client collector for gathering RSSi from various new technologies (e.g., 5G NR, Wi-Fi 6, Wi-Fi-7, BLE).
-
A master collector for aggregating the client data.
-
A Big Data Repository for hosting the localisation history.
-
Machine Learning Engine localise assets and first responder team members.
-
Live positioning API for serving the real-time location to the front-end applications.
​
Front-end:
-
Assets tracking application for a precise localisation and monitoring valuable assets.
-
First Responder application for tracking rescue members inside complex military building.
Sophisticated deep learning-based (ML/AI) indoor localization algorithms capable to fuse information from heterogeneous popular wireless technologies and rapidly – not resource/energy intensively -provide improved 3D positioning accuracy (<0.5m) for personnel tracking.
​
a. Network Edge: The network edge hosts assets that need tracking such as First Responders, employees, visitors, movable equipment, parts, or any other expensive goods in a Critical Infrastructure, Complex or warehouse. The slice acts as a signal transmitter for the positioning service in the knowledge plane.
b. Network Access: the network access slice consists of a set of networks such as Wireless Sensors, BLE, Wi-Fi Networks. These Wireless networks are expected to range between 1GHz and 5 GHz. These technologies can either be used together or separately during the radio planning process of the positioning. It is suggested a combination of several heterogeneous technologies to get better accuracy.
​
c. Network Core: This tier is used as a gateway to send signal data to the knowledge plane components.
​
d. Knowledge: The knowledge slice consists of three main components:
i) Machine Learning/AI engine that provides the modelling for localisation.
​
ii) A live positioning API service to provide both real-time and historical location tracking.
iii)A Big Data component to store both historical data and the positioning model parameters.
These three applications will be built as a backend system to provide localisation for any building through a RESTful
e. API.Application: The application slice hosts two main applications:
i) An asset tracking software to track valuable goods and generate alerts in case or any removal from the designated location.
​
ii) A First Responder software for tracking first responders team members while they carry out critical missions inside complex buildings.
The real-time location is provided to both applications through the RESTful API served by the live positioning API in the knowledge slice.