Periodic Reporting for period 1 - 5GSmartFact (Industrial Doctorate Training Network on Future Wireless Connected and Automated Industry enabled by 5G)
Reporting period: 2021-03-01 to 2023-02-28
5GSmartFact is an MSCA-ITN project funded by the EU whose objective is to study, develop, optimize and assess the deployment of 5G networks that target the IIoT requirements (in terms of availability, ultra-low latency, reliability, amount of supported devices, localization accuracy and energy efficiency) in factory environments, and exploit them to integrate factory applications (especially those related to robot-control and robot navigation) which might lead to a complete redesign of robot architectures and hence to a leap forward in the industry automation .
5GSmartFact events, schools and courses are aimed to train the 14 project’s Early Stage Researchers (ESRs) and are open to other PhD students.
5GSmartFact events, schools and courses are aimed to train the 14 project’s Early Stage Researchers (ESRs) and are open to other PhD students.
WP2 deals with research on evolution of 5G systems for accommodating the demanding needs of the factory of the future. 5GSmartFact presented the background and state of the art on wireless technologies for industry 4.0 highlighting the main challenges and limitations of the current solutions and techniques, justifying how future research is needed to address them. The ESRs involved in WP2 are ESR4, ESR5, ESR6 and ESR9.
Deliverable D2.1 “State of the art and challenges for IIoT channel modeling, radio planning and massive MIMO and IRE technologies” is available at the project website.
WP3 deals with Network-specific design for communication and computation in IIoT. The involved researchers are ESRs 2, 3, 8, 10 and 11. Deliverable D3.1 “State of the art on 5G industrial networks and slicing for IIoT“.
WP4 deals with 5G-aware industrial applications. The project examined a collection of scientific works on the use of the 5G technologies within the robotics domain and presented the state-of-the-art, describing different approaches adopted to overcome the constrained resources inherent to robotic platforms, in particular mobile ones. D4.1 “Survey on the state on the use of 5G for robot applications in remote distributed edge computation platforms” is available at the project website. ESRs contributing to WP4 are ESR1,ESR4 ESR12, ESR13 and ESR14.
Regarding WP5 Training, five courses were offered to ESRs, open to other researchers, dealing with Mathematical tools; Foundations on Robotics; Radio Planning and Network Simulation; Open access; and Scientific Writing & English communication skills.
As main result of WP6 dissemination activities, seven 5GSmartFact ESRs’ scientific conference papers were accepted and four articles have been submitted. 5GSmartFact has published two newsletters. 5GSmartFact participated in H2020 Booster completing Module A.
Deliverable D2.1 “State of the art and challenges for IIoT channel modeling, radio planning and massive MIMO and IRE technologies” is available at the project website.
WP3 deals with Network-specific design for communication and computation in IIoT. The involved researchers are ESRs 2, 3, 8, 10 and 11. Deliverable D3.1 “State of the art on 5G industrial networks and slicing for IIoT“.
WP4 deals with 5G-aware industrial applications. The project examined a collection of scientific works on the use of the 5G technologies within the robotics domain and presented the state-of-the-art, describing different approaches adopted to overcome the constrained resources inherent to robotic platforms, in particular mobile ones. D4.1 “Survey on the state on the use of 5G for robot applications in remote distributed edge computation platforms” is available at the project website. ESRs contributing to WP4 are ESR1,ESR4 ESR12, ESR13 and ESR14.
Regarding WP5 Training, five courses were offered to ESRs, open to other researchers, dealing with Mathematical tools; Foundations on Robotics; Radio Planning and Network Simulation; Open access; and Scientific Writing & English communication skills.
As main result of WP6 dissemination activities, seven 5GSmartFact ESRs’ scientific conference papers were accepted and four articles have been submitted. 5GSmartFact has published two newsletters. 5GSmartFact participated in H2020 Booster completing Module A.
Progress and expected results related to the described WPs are the following:
WP2
● Enhanced and validated ray-based channel model, compliant with dynamic environments and new radio technologies such as RIS and possibly distributed MIMO.
● Estimation of the number of spatial Degrees of Freedom and computation of their corresponding orthogonal modes in surface-based communication.
● Location Aided user association and cluster formation for cell Free Massive MIMO: Exploiting the positional and path specific information to propose novel user-association and dynamic cluster formation.
● Centralised Graph Neural Network Based Power Control Algorithm for Industrial Wireless Subnetworks.
● New energy self-sufficient IoT motes for industrial environments that integrate a processing unit and will leverage VLC for communications and energy harvesting.
WP3
● Wifi 7 (802.11be) multilink simulation using NS3. Which will actually be a scheduling scheme for multilink in a smart factory environment.
● Multy-frequency RIS; idea phase and delivery date for MEC.
● Developing DMA electromagnetic model compatible with MU-MIMO system and use it in ISAC scenario and developing optimization algorithm. Further evaluation of the developed model with machine learning.
WP4
● Real Task and Motion Planning (TAMP) experiments that evaluate the performance of a robotic system comparing edge computing using latency data of 5G and other wireless technologies.
● Conceptualize, develop, and evaluate the performance of a 5G-based indoor positioning system in live production environments.
● cm-level tracking and positioning algorithms that use VLC emitting devices as reference markers in the industrial environment to achieve high precision localization.
● Using a realistic simulated environment, a localization accuracy similar to most GNSS receivers was achieved; it is envisaged to increase the accuracy.
● A preliminary 5G SLAM algorithm has been developed and validated in simulation and is to be extended for multi sensor fusion amongst robotic systems.
WP2
● Enhanced and validated ray-based channel model, compliant with dynamic environments and new radio technologies such as RIS and possibly distributed MIMO.
● Estimation of the number of spatial Degrees of Freedom and computation of their corresponding orthogonal modes in surface-based communication.
● Location Aided user association and cluster formation for cell Free Massive MIMO: Exploiting the positional and path specific information to propose novel user-association and dynamic cluster formation.
● Centralised Graph Neural Network Based Power Control Algorithm for Industrial Wireless Subnetworks.
● New energy self-sufficient IoT motes for industrial environments that integrate a processing unit and will leverage VLC for communications and energy harvesting.
WP3
● Wifi 7 (802.11be) multilink simulation using NS3. Which will actually be a scheduling scheme for multilink in a smart factory environment.
● Multy-frequency RIS; idea phase and delivery date for MEC.
● Developing DMA electromagnetic model compatible with MU-MIMO system and use it in ISAC scenario and developing optimization algorithm. Further evaluation of the developed model with machine learning.
WP4
● Real Task and Motion Planning (TAMP) experiments that evaluate the performance of a robotic system comparing edge computing using latency data of 5G and other wireless technologies.
● Conceptualize, develop, and evaluate the performance of a 5G-based indoor positioning system in live production environments.
● cm-level tracking and positioning algorithms that use VLC emitting devices as reference markers in the industrial environment to achieve high precision localization.
● Using a realistic simulated environment, a localization accuracy similar to most GNSS receivers was achieved; it is envisaged to increase the accuracy.
● A preliminary 5G SLAM algorithm has been developed and validated in simulation and is to be extended for multi sensor fusion amongst robotic systems.