Goal-Oriented Wave Computing for Direction-of-Arrival Estimation
Abstract
Programmable metasurfaces---arrays of elements whose electromagnetic response can be tuned electronically---can process signals as a wave propagates through or reflects off them, a paradigm known as wave-domain analog computing. The design strategy is typically indirect: the surface is fitted to a prescribed linear operator, such as a discrete Fourier transform, and the result is read from output power measurements. Using direction-of-arrival (DoA) estimation as a canonical benchmark, we study what this indirection costs across analog computing topologies, namely reflective reconfigurable intelligent surfaces (RIS), beyond-diagonal RIS (BD-RIS), and stacked intelligent metasurfaces (SIM). We first derive Cramér--Rao bounds for the complete physical estimation chain, including one that accounts for output detectors measuring only intensity. We then formulate a goal-oriented alternative where the surfaces are trained directly through the deployed readout, with no operator target. We observe that goal-oriented training lets sparsely connected or shallower surfaces reach the task performance of their more complex operator-fitted counterparts---despite nearly identical Fisher information---and that the advantage persists under coarsely quantized (1--3 bit) control. From 16 to 64 elements, the depth saving persists, while the connectivity a beyond-diagonal RIS needs grows with the aperture. Under tuner and per-layer loss, the goal-oriented designs keep their advantage.
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BibTeX
@misc{kalfa2026goaloriented,
title = {{Goal-Oriented Wave Computing for Direction-of-Arrival Estimation}},
author = {Mert Kalfa},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.06839},
eprint = {2610.06839},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.06839},
}
RIS
TY - UNPB
TI - Goal-Oriented Wave Computing for Direction-of-Arrival Estimation
AU - Mert Kalfa
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.06839
UR - https://arxiv.org/abs/2610.06839
AB - Programmable metasurfaces---arrays of elements whose electromagnetic response can be tuned electronically---can process signals as a wave propagates through or reflects off them, a paradigm known as wave-domain analog computing. The design strategy is typically indirect: the surface is fitted to a prescribed linear operator, such as a discrete Fourier transform, and the result is read from output power measurements. Using direction-of-arrival (DoA) estimation as a canonical benchmark, we study what this indirection costs across analog computing topologies, namely reflective reconfigurable intelligent surfaces (RIS), beyond-diagonal RIS (BD-RIS), and stacked intelligent metasurfaces (SIM). We first derive Cramér--Rao bounds for the complete physical estimation chain, including one that accounts for output detectors measuring only intensity. We then formulate a goal-oriented alternative where the surfaces are trained directly through the deployed readout, with no operator target. We observe that goal-oriented training lets sparsely connected or shallower surfaces reach the task performance of their more complex operator-fitted counterparts---despite nearly identical Fisher information---and that the advantage persists under coarsely quantized (1--3 bit) control. From 16 to 64 elements, the depth saving persists, while the connectivity a beyond-diagonal RIS needs grows with the aperture. Under tuner and per-layer loss, the goal-oriented designs keep their advantage.
ER -