Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Kalfa, Mert
Institution: Sabancı Üniversitesi
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7220743039