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GRENHAS Report Summary

Project ID: 654123
Funded under: H2020-EU.1.3.2.

Periodic Reporting for period 1 - GRENHAS (Green and Smart Communications with Energy Harvesting: A Signal Processing Approach)

Reporting period: 2015-03-01 to 2017-02-28

Summary of the context and overall objectives of the project

Efficient usage of energy resources is a growing concern in today's communication systems. Solutions that consider energy harvesting (EH), where nodes in a communication system utilize other available energy sources, such as solar, wind power or man made signals, instead of completely relying on a fixed battery or the power from the grid, offer a promising perspective. EH capabilities not only enable efficient usage of energy sources but also offer enhanced mobility and prolonged network life-times. Hence communications systems powered by energy harvesting have a wide range of applications including environmental monitoring, process monitoring, smart homes and smart cities. Understanding the information transfer capabilities of communication systems under EH constraints is an important step for efficiently incorporating EH capabilities into our systems.This work addresses this problem within a framework that prioritizes practical low-complexity solutions. We have adopted an estimation theoretic perspective where the problem is investigated within a practical signal processing framework. We have focused on efficient transmission and resource allocation strategies. Practical receiver structures with linear filtering, low complexity designs such as linear precoders, power allocation methods were important ingredients in our work. Our resulting solutions complement the existing information theoretic solutions, and contribute to moving us one step closer to creating future green and smart communication systems.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

This project is concerned with providing solutions for the challenges encountered while incorporating energy harvesting capabilities into our communication systems. We have focused on the design of efficient signal processing techniques. These include the design of linear precoders and filters at transmitters/receivers, and resource allocation strategies, such as transmission power allocation through transmission time frame. We have considered both the challenges arising from intermittent characteristics of natural sources, and the challenges arising from radio-frequency (RF) power transfer. We have provided optimal power allocation strategies and performance bounds for intermittent energy arrival scenario. We have provided guidelines for beamforming for wireless power transfer scenarios and feasibility of ambient RF energy harvesting. These results have led to two accepted/published journal papers, one journal paper draft, and various conference papers in ICC2017, EUSIPCO2016 (invited), ISIT2016, ISCWS2016 and ISWCS2015. The fellow also gave an invited talk at WCNC 2017 Workshop -Energy Harvesting and Remotely Powered Wireless Communication for the internet of things (IoT). Collaboration with Ericsson was initiated and has resulted in one journal paper. Three Master Thesis projects on energy harvesting systems at Chalmers University of Technology was initiated and supervised by the fellow. Outreach activities are performed at 2016 Gothenburg Science Festival, which include a high school program and a public panel discussion.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

At the moment, the information theoretic studies continue to provide us knowledge on the fundamental limits of energy harvesting systems. On the other hand, practical communication systems that make usage of simplistic energy harvesting ideas have also started to emerge. It is important that the gap between these two realms are bridged and efficient communication strategies are developed and standardized into communication systems. The project contributed to this goal by developing practical signal processing techniques. Practical power allocation scenarios, non-linear energy harvesting models, hardware impairment models were important ingredients of our work. Our resulting solutions complement the existing information theoretic solutions, and contribute to moving us one step closer to creating future green and smart communication systems.

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