33 research outputs found
A Hardware Architecture for Reconfigurable Intelligent Surfaces with Minimal Active Elements for Explicit Channel Estimation
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.Comment: 5 pages, 2 figures, invited/accepted to IEEE ICASSP 202
Reconfigurable intelligent surface passive beamforming enhancement using unsupervised learning
Reconfigurable intelligent surfaces (RIS) is a wireless technology that has the potential to improve cellular communication systems significantly. This paper considers enhancing the RIS beamforming in a RIS-aided multiuser multi-input multi-output (MIMO) system to enhance user throughput in cellular networks. The study offers an unsupervised/deep neural network (U/DNN) that simultaneously optimizes the intelligent surface beamforming with less complexity to overcome the non-convex sum-rate problem difficulty. The numerical outcomes comparing the suggested approach to the near-optimal iterative semi-definite programming strategy indicate that the proposed method retains most performance (more than 95% of optimal throughput value when the number of antennas is 4 and RIS’s elements are 30) while drastically reducing system computing complexity