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Intelligent surfaces comprising of cost effective, nearly passive, andreconfigurable unit elements are lately gaining increasing interest due totheir potential in enabling fully programmable wireless environments. They areenvisioned to offer environmental intelligence for diverse communicationobjectives, when coated on various objects of the deployment area of interest.To achieve this overarching goal, the channels where the ReconfigurableIntelligent Surfaces (RISs) are involved need to be in principle estimated.However, this is a challenging task with the currently available hardware RISarchitectures requiring lengthy training periods among the network nodesutilizing RIS-assisted wireless communication. In this paper, we present anovel RIS architecture comprising of any number of passive reflecting elements,a simple controller for their adjustable configuration, and a single RadioFrequency (RF) chain for baseband measurements. Capitalizing on thisarchitecture and assuming sparse wireless channels in the beamspace domain, wepresent an alternating optimization approach for explicit estimation of thechannel gains at the RIS elements attached to the single RF chain.Representative simulation results demonstrate the channel estimation accuracyand achievable end-to-end performance for various training lengths and numbersof reflecting unit elements.

A Hardware Architecture for Reconfigurable Intelligent Surfaces with Minimal Active Elements for Explicit Channel Estimation