TY - GEN
T1 - Absense
T2 - 6th ACM International Conference on Nanoscale Computing and Communication, NANOCOM 2019
AU - Liaskos, C.
AU - Pyrialakos, G.
AU - Pitilakis, A.
AU - Abadal, S.
AU - Tsioliaridou, A.
AU - Tasolamprou, A.
AU - Tsilipakos, O.
AU - Kantartzis, N.
AU - Ioannidis, S.
AU - Alarcon, E.
AU - Cabellos, A.
AU - Kafesaki, M.
AU - Pitsillides, A.
AU - Kossifos, K.
AU - Georgiou, J.
AU - Akyildiz, I. F.
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/9/25
Y1 - 2019/9/25
N2 - Metasurfaces (MS) constitute effective media for manipulating and transforming impinging EM waves. Related studies have explored a series of impactful MS capabilities and applications in areas such as wireless communications, medical imaging and energy harvesting. A key-gap in the existing body of work is that the attributes of the EM waves to-be-controlled (e.g., direction, polarization, phase) are known in advance. The present work proposes a solution to the EM wave sensing problem using the intelligent and networked MS counterparts-the HyperSurfaces (HSFs), without requiring dedicated field sensors. An nano-network embedded within the HSF iterates over the possible MS configurations, finding the one that fully absorbs the impinging EM wave, hence maximizing the energy distribution within the HSF. Using a distributed consensus approach, the nano-network then matches the found configuration to the most probable EM wave traits, via a static lookup table that can be created during the HSF manufacturing. Realistic simulations demonstrate the potential of the novel scheme. Moreover, we show that the proposed workflow is also an ambient EM compiler, i.e., an autonomic HSF that translates high-level EM objectives to corresponding, low-level EM actuation commands.
AB - Metasurfaces (MS) constitute effective media for manipulating and transforming impinging EM waves. Related studies have explored a series of impactful MS capabilities and applications in areas such as wireless communications, medical imaging and energy harvesting. A key-gap in the existing body of work is that the attributes of the EM waves to-be-controlled (e.g., direction, polarization, phase) are known in advance. The present work proposes a solution to the EM wave sensing problem using the intelligent and networked MS counterparts-the HyperSurfaces (HSFs), without requiring dedicated field sensors. An nano-network embedded within the HSF iterates over the possible MS configurations, finding the one that fully absorbs the impinging EM wave, hence maximizing the energy distribution within the HSF. Using a distributed consensus approach, the nano-network then matches the found configuration to the most probable EM wave traits, via a static lookup table that can be created during the HSF manufacturing. Realistic simulations demonstrate the potential of the novel scheme. Moreover, we show that the proposed workflow is also an ambient EM compiler, i.e., an autonomic HSF that translates high-level EM objectives to corresponding, low-level EM actuation commands.
KW - EM sensing
KW - HyperSurfaces
KW - Metasurfaces
KW - Nano-networks
UR - http://www.scopus.com/inward/record.url?scp=85073809942&partnerID=8YFLogxK
U2 - 10.1145/3345312.3345468
DO - 10.1145/3345312.3345468
M3 - Conference contribution
AN - SCOPUS:85073809942
T3 - Proceedings of the 6th ACM International Conference on Nanoscale Computing and Communication, NANOCOM 2019
BT - Proceedings of the 6th ACM International Conference on Nanoscale Computing and Communication, NANOCOM 2019
PB - Association for Computing Machinery, Inc
Y2 - 25 September 2019 through 27 September 2019
ER -