Periodic Reporting for period 1 - RISESWCS (Reconfigurable intelligent surfaces-based energy harvesting sustainable wireless communication systems.)
Okres sprawozdawczy: 2023-10-01 do 2025-09-30
The project RISESWCS was dedicated to the study and research of RIS assisted energy harvesting wireless communications. In this project, the key goals were to analyze and optimize the performance of RIS assisted communication systems while also providing energy (wirelessly) to ambient users. This was a very timely topic for research due to the exceedingly large number of low power devices that will be deployed in near future where the need for replacing batteries is difficult to fulfill. Specifically, four key research objectives were devised as given below:
1-Characterize the mathematical relationship between average performance metrics such as data rate, outage probability, and EH efficiency; and the system parameters such as number of RIS elements, total transmit power, and transmission bandwidth. Work package 1 (WP1), carried out at TAU, aims at achieving RO1. RO1 is measurable and verifiable by analytical formulations and relationships obtained for performance metrics and system parameters.
2-Building on the groundwork of RO1, develop optimization algorithms for maximizing the performance under practical limitations on the feedback requirements and subsequently devise low complexity distributed
implementations of the developed optimization algorithms. RO2 will be achieved at TAU through WP2. RO2 is measured and verified by the developed optimization algorithms.
3-Develop a simulation software toolbox for easy verification and demonstration of the newly obtained results. This toolbox will be named RISESWCS and can be exploited both in academia and industry. RO3 will be achieved
through WP3 at TAU. The success of RO3 is measured and verified by the developed toolbox.
4-Provide abstractions of the results of RO1 for encompassing the large-scale network analysis to assess the feasibility of the large networks and to devise cross-layer optimization techniques for efficient use of available
resources. WP4 will be responsible for achieving RO4 and the tasks of WP4 will be partially carried out at TAU and UVIGO. This feasibility study will provide measurable and verifiable performance tradeoffs as function of system
parameters.
1-Characterize the mathematical relationship between average performance metrics such as data rate, outage probability, and EH efficiency; and the system parameters such as number of RIS elements, total transmit power, and transmission bandwidth. Work package 1 (WP1), carried out at TAU, aims at achieving RO1. RO1 is measurable and verifiable by analytical formulations and relationships obtained for performance metrics and system parameters.
2-Building on the groundwork of RO1, develop optimization algorithms for maximizing the performance under practical limitations on the feedback requirements and subsequently devise low complexity distributed
implementations of the developed optimization algorithms. RO2 will be achieved at TAU through WP2. RO2 is measured and verified by the developed optimization algorithms.
3-Develop a simulation software toolbox for easy verification and demonstration of the newly obtained results. This toolbox will be named RISESWCS and can be exploited both in academia and industry. RO3 will be achieved
through WP3 at TAU. The success of RO3 is measured and verified by the developed toolbox.
4-Provide abstractions of the results of RO1 for encompassing the large-scale network analysis to assess the feasibility of the large networks and to devise cross-layer optimization techniques for efficient use of available
resources. WP4 will be responsible for achieving RO4 and the tasks of WP4 will be partially carried out at TAU and UVIGO. This feasibility study will provide measurable and verifiable performance tradeoffs as function of system
parameters.
During this project, the Researcher has been working toward formalizing the relationships between the harvested energy/communication SINR and the active/passive beamformers of BS/RIS within the system. After formalizing these relationships, the Researcher focused on developing optimization problem for minimizing the overall power consumption of the system. As a result of this effort, a low-complexity power allocation algorithm was developed to obtain sub-optimal solution for the active and passive beamformers for the BS and RIS.
Due to the early termination of the project, it was not possible to fully realize the impacts of the project results.