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 RISassisted 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.
Millimeter Wave (mmWave) massive Multiple Input Multiple Output (MIMO) systems realizing directive beamforming require reliable estimation of the wireless propagation channel. However, mmWave channels are characterized by high variability that severely challenges their recovery over short training periods. Current channel estimation techniques exploit either the channel sparsity in the beamspace domain or its low rank property in the antenna domain, nevertheless, they still require large numbers of training symbols for satisfactory performance. In this paper, we present a novel channel estimation algorithm that jointly exploits the latter two properties of mmWave channels to provide more accurate recovery, especially for shorter training intervals. The proposed iterative algorithm is based on the Alternating Direction Method of Multipliers (ADMM) and provides the global optimum solution to the considered convex mmWave channel estimation problem with fast convergence properties.
This paper proposes a novel architecture with a framework that dynamically activates the optimal number of radio frequency (RF) chains used to implement hybrid beamforming in a millimeter wave (mmWave) multiple-input and multiple-output (MIMO) system. We use fractional programming to solve an energy efficiency maximization problem and exploit the Dinkelbach method (DM) based framework to optimize the number of active RF chains and data streams. This solution is updated dynamically based on the current channel conditions, where the analog/digital (A/D) hybrid precoder and combiner matrices at the transmitter and the receiver, respectively, are designed using a codebook-based fast approximation solution called gradient pursuit (GP). The GP algorithm shows less run time and complexity while compared to the state of the art orthogonal matching pursuit (OMP) solution. The energy and spectral efficiency performance of the proposed framework is compared with the existing state of the art solutions such as the brute force (BF), the digital beamformer and the analog beamformer. The codebook-free approaches to design the precoders and combiners such as alternating direction method of multipliers (ADMM) and singular value decomposition (SVD) based solution are also shown to be incorporated into the proposed framework to achieve better energy efficiency performance.
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