Channel Estimation for RIS-Aided mmWave MIMO Systems via Atomic Norm Minimization

Abstract

Channel estimation (CE) plays a key role in reconfigurable intelligent surface (RIS)-aided multipleinput multiple-output (MIMO) communication systems, while it poses a challenging task due to the passive nature of RIS and the cascaded channel structures. In this paper, a partially decoupled atomic norm minimization (PDANM) framework is proposed for CE of RIS-aided MIMO systems, which exploits the three-dimensional angular sparsity of the channel. In particular, PDANM partially decouples the differential angles at the RIS from other angles at the base station and user equipment, reducing the computational complexity compared with existing methods. A reweighted PDANM (RPDANM) algorithm is proposed to further improve CE accuracy, which iteratively refines CE through a specifically designed reweighing strategy. Building upon RPDANM, we propose an iterative approach named RPDANM with adaptive phase control (RPDANM-APC), which adaptively adjusts the RIS phases based on previously estimated channel parameters to facilitate CE, achieving superior CE accuracy while reducing training overhead. Numerical simulations demonstrate the superiority of our proposed approaches in terms of running time, CE accuracy, and training overhead. In particular, the RPDANM-APC approach can achieve higher CE accuracy than existing methods within less than 40 percent training overhead while reducing the running time by tens of times.

Leveraging Location Information for RIS-Aided mmWave MIMO Communications

“…where T c is the duration of coherence time, T t is the duration for training, w and Ω are the designed MS combiner and RIS phase control matrix based on the parameter estimates [4].…”

“…The estimation of φ R,M can be obtained by root finding approach based on Toep(û) withû from ( 13) [4]. Similarly, we can estimate the angular difference θ diff in the same manner based on Y T .…”

“…where θ diff asin(sin(θ R,M ) − sin(φ B,R ) • 1), where 1 is an all-one vector. This is equivalent to rank-L R,M (rank-deficient) mmWave MIMO channel estimation, and decoupled ANM can also be applied [4].…”

“…They can be seamlessly incorporated into existing systems and used for maintaining the connectivity when the direct line-of-sight (LoS) path between the base station (BS) and mobile station (MS) encounters blockage, which is commonly seen in mmWave MIMO systems [3]. Similar to point-to-point mmWave MIMO systems, efficient channel state information (CSI) acquisition is challenging [4]- [6]. However, the prior information on the channel parameters, e.g., angles of departure (AoDs) and angles of arrival (AoAs), derived from an out-of-bound location information system (e.g., global positioning system (GPS) for outdoor environment and ultra wideband (UWB) for indoor environment) on the MS and environmental objects, can help [7], [8].…”

“…In practice, an RIS may have hundreds of elements, but during the channel estimation phase, some of them may need to be turned off (i.e., in an absorption mode) in order to achieve an optimal tradeoff between estimation performance and computation complexity 4. Throughout the paper, we assume a single RF chain at the MS, so Tt = P T .…”