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

本文提出了一种新颖的基于上转换器的可重构智能表面 (RIS) 结构，并利用这个结构，在光束空间域假设稀疏无线信道，提出了一种交替优化方法，用于明确估计连接到单个射频链的 RIS 元件的信道增益。