Periodic Reporting for period 1 - RISA-VLC (Reconfigurable Intelligent Surface-Assisted VLC for battery-free IoT devices)
Reporting period: 2023-01-01 to 2024-12-31
The vast development of smart communication devices for the Internet of Things (IoT) is incurring an ever-increasing demand for wireless data services and energy sources. It is expected to witness a growth of connected IoT devices in the next few years, reaching up to tens of billions. Currently, these devices rely on batteries for their operation, but battery recycling is a delicate matter that has a negative environmental impact due to their chemical components. Thus, it is reasonable to think that new wireless communication systems must comply with energy-efficiency standards, then reducing the use of batteries and the carbon footprint produced by them. Because of all this, one of the mission areas in Horizon Europe is climate-neutral and smart cities, aligned with the European Green Deal. Smart cities will leverage the next generation of wireless communication technologies that must contribute to the achievement of United Nations sustainable development goals such as sustainable cities and communities, and responsible consumption and production. The intrinsic duality of visible light communication (VLC), also known as Light Fidelity (LiFi) in its networked version, for simultaneous illumination and data transmission positions it as the perfect technology for leading the future energy efficient wireless indoor communication systems. We envision LiFi for enabling battery-free IoT devices. However, there are two fundamental limitations that impede this vision:
1) The portion of optical power reached by the receiver is a tiny amount of the transmit power.
2) LiFi-based systems strictly depend on a line-of-sight link between LED and receiver.
This project titled ‘Reconfigurable Intelligent Surface - Assisted VLC for battery-free IoT devices (RISA-VLC)’ has the overarching goal of enabling the massive deployment of battery-free IoT devices by increasing communication and harvesting performance. With such purpose, RISA-VLC explores reconfigurable intelligent surfaces (RIS) for VLC with the aim of engineering the channel and augmenting the amount of received optical power in indoor LiFi systems, then eliminating the barriers for the deployment of autonomous IoT devices. This applies to scenarios such as smart home, healthcare, smart farming and Industry 4.0.
RISA-VLC pursues the following objectives:
O1. Identify materials that can perform as RIS at indoor LiFi scenarios and characterize their photometric parameters.
O2. Provide a new mathematical channel model considering the reflective surfaces of O1.
O3. Propose novel signal-processing and communication techniques to exploit the manageable proposed channel.
O4. Establish guidelines for RIS-assisted wireless power transfer at LiFi scenarios, then studying the autonomy of IoT devices considering the amount of harvested power under the new proposed channel.
1) The portion of optical power reached by the receiver is a tiny amount of the transmit power.
2) LiFi-based systems strictly depend on a line-of-sight link between LED and receiver.
This project titled ‘Reconfigurable Intelligent Surface - Assisted VLC for battery-free IoT devices (RISA-VLC)’ has the overarching goal of enabling the massive deployment of battery-free IoT devices by increasing communication and harvesting performance. With such purpose, RISA-VLC explores reconfigurable intelligent surfaces (RIS) for VLC with the aim of engineering the channel and augmenting the amount of received optical power in indoor LiFi systems, then eliminating the barriers for the deployment of autonomous IoT devices. This applies to scenarios such as smart home, healthcare, smart farming and Industry 4.0.
RISA-VLC pursues the following objectives:
O1. Identify materials that can perform as RIS at indoor LiFi scenarios and characterize their photometric parameters.
O2. Provide a new mathematical channel model considering the reflective surfaces of O1.
O3. Propose novel signal-processing and communication techniques to exploit the manageable proposed channel.
O4. Establish guidelines for RIS-assisted wireless power transfer at LiFi scenarios, then studying the autonomy of IoT devices considering the amount of harvested power under the new proposed channel.
First of all, we identified the materials that can perform as RIS: metasurfaces and mirrors. We first evaluated the placement of mirrors to minimize the outage area, which is the key performance metric for IoT devices. This contribution was published in the flagship conference IEEE Globecom 2023.
We extended the first contribution with the introduction of movable mirrors. We studied the specular and diffuse reflections of mirrors and wall elements, and we formulated an optimization problem to minimize the outage probability while minimizing one of the two available resources, the number of mirrors and the total optical power allocated among LEDs. This work was submitted to IEEE Transactions on Wireless Communications and it is currently under review.
The third main contribution was published in IEEE Globecom 2024, where we studied the potential of curved mirrors as static reflective surfaces to provide a broadening specular reflection that increases the signal coverage in mirror-assisted VLC scenarios.
Note that these first three contributions contributed to the achievement of the objectives O1, O2, and O3.
Additionally, in line with the evolving horizon of RISA-VLC’s research, we have performed the following activities:
We studied multi-chip LEDs to increase the data rate and energy harvested. In this context, we derived an exact closed-form procedure for the allocation of power per individual LED chip for any tri-color combination, which is beyond the state of art, and we evaluated communication and illumination metrics for multiple LED combinations. This contribution was published in the IEEE/Optica Journal of Lightwave Technology.
Besides, the usage of RGB LEDs was extended to vehicular scenarios that call for high data rates and low latencies. We proposed a novel transmission scheme referred to as colored blind interference alignment (cBIA), and it was published in IEEE Transactions on Vehicular Technology.
Still working on the transmitter side, we evaluated the communication and energy harvesting performance in dimming mode, which was published in Computer Communications journal. Besides, we studied the performance of low-power metasurfaces installed at the transmitter to modulate sunlight and increase the spectral efficiency. This contribution was published in the conference MedComNet 2023.
Looking at the autonomy performance of the IoT device, we proposed a system architecture for battery-free IoT systems, that was published in IEEE ICNP 2023. Additionally, a demonstration for a battery-free LiFi-based IoT device was presented in ACM Mobicom 2023.
Note that these last works contributed to attaining the objective O4 of RISA-VLC.
Finally, RISA-VLC went beyond the planned activities and studied the performance of LiFi, which is the core technology of the project, together with other widespread RF systems such as WiFi. We have designed a classification model to predict the type of user’s trajectory and assist a reinforcement learning algorithm to make handover decisions that are automatically adapted to new network conditions. This work was published in the IEEE/Optica Journal of Optical Communications and Networking.
We extended the first contribution with the introduction of movable mirrors. We studied the specular and diffuse reflections of mirrors and wall elements, and we formulated an optimization problem to minimize the outage probability while minimizing one of the two available resources, the number of mirrors and the total optical power allocated among LEDs. This work was submitted to IEEE Transactions on Wireless Communications and it is currently under review.
The third main contribution was published in IEEE Globecom 2024, where we studied the potential of curved mirrors as static reflective surfaces to provide a broadening specular reflection that increases the signal coverage in mirror-assisted VLC scenarios.
Note that these first three contributions contributed to the achievement of the objectives O1, O2, and O3.
Additionally, in line with the evolving horizon of RISA-VLC’s research, we have performed the following activities:
We studied multi-chip LEDs to increase the data rate and energy harvested. In this context, we derived an exact closed-form procedure for the allocation of power per individual LED chip for any tri-color combination, which is beyond the state of art, and we evaluated communication and illumination metrics for multiple LED combinations. This contribution was published in the IEEE/Optica Journal of Lightwave Technology.
Besides, the usage of RGB LEDs was extended to vehicular scenarios that call for high data rates and low latencies. We proposed a novel transmission scheme referred to as colored blind interference alignment (cBIA), and it was published in IEEE Transactions on Vehicular Technology.
Still working on the transmitter side, we evaluated the communication and energy harvesting performance in dimming mode, which was published in Computer Communications journal. Besides, we studied the performance of low-power metasurfaces installed at the transmitter to modulate sunlight and increase the spectral efficiency. This contribution was published in the conference MedComNet 2023.
Looking at the autonomy performance of the IoT device, we proposed a system architecture for battery-free IoT systems, that was published in IEEE ICNP 2023. Additionally, a demonstration for a battery-free LiFi-based IoT device was presented in ACM Mobicom 2023.
Note that these last works contributed to attaining the objective O4 of RISA-VLC.
Finally, RISA-VLC went beyond the planned activities and studied the performance of LiFi, which is the core technology of the project, together with other widespread RF systems such as WiFi. We have designed a classification model to predict the type of user’s trajectory and assist a reinforcement learning algorithm to make handover decisions that are automatically adapted to new network conditions. This work was published in the IEEE/Optica Journal of Optical Communications and Networking.
The project is original and challenging as it addresses one fundamental issue for sustainable future communication systems. Considering a realistic LiFi indoor scenario where shadowed zones are usual means a step further for the study of sustainable IoT devices. This project is progressing beyond and proposes the use of novel RIS elements to overcome this issue. We bring together three main pillars for future wireless networks: energy harvesting, optical wireless communication and communication with intelligent surfaces.
Besides the results obtained throughout the project, the plan is to continue this research line in the future by carrying out the following tasks:
1) Propose novel LiFi systems for a zero-power 6G, including analysis of new transmitting/receiving LiFi materials and the implementation of ORIS for an optical wireless channel control.
2) Implement LiFi for a rate-centric 6G wireless RAN, including LiFi-based localization for extending ISAC to an optical wireless domain and further integration of LiFi-RF networks.
3) Enable the use of artificial intelligence in ORIS-assisted LiFi networks to ease and enhance the synchronization and resource allocation, including on-device and on-network machine learning techniques.
Besides the results obtained throughout the project, the plan is to continue this research line in the future by carrying out the following tasks:
1) Propose novel LiFi systems for a zero-power 6G, including analysis of new transmitting/receiving LiFi materials and the implementation of ORIS for an optical wireless channel control.
2) Implement LiFi for a rate-centric 6G wireless RAN, including LiFi-based localization for extending ISAC to an optical wireless domain and further integration of LiFi-RF networks.
3) Enable the use of artificial intelligence in ORIS-assisted LiFi networks to ease and enhance the synchronization and resource allocation, including on-device and on-network machine learning techniques.