Reconfigurable Intelligent Surfaces: Channel Estimation and Applications in Future Wireless Networks

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Reconfigurable Intelligent Surfaces (RISs) are planar structures comprising passive, low-cost reflecting elements that efficiently manipulate the electromagnetic (EM) propagation environment through phase and amplitude adjustments, enhancing wireless system performance. RIS passive beamforming eliminates the need for complex signal processing or RF chains, offering benefits like improved signal-to-interference noise ratio (SINR), enhanced link quality-of-service (QoS), and reduced energy consumption. However, the limited signal processing capability of RIS-assisted wireless networks presents design challenges. This dissertation comprises two main parts, addressing channel estimation schemes for RIS-assisted MIMO systems and practical RIS implementation to optimize MIMO Radar and communication coexistence systems, respectively. In the first part, we introduce an innovative algorithm for estimating the composite channel, separate RIS-based channels, and the direct channel within the RIS-assisted system. By dividing the RIS surface into smaller sub-RIS units and controlling the phase shifts, we successfully estimate the overall channel. Additionally, we propose a straightforward passive pilot sequence scheduling scheme to jointly adjust phase shift coefficients. Next, we present a two-stage channel estimation approach for RIS-assisted mmWave communication systems. The first stage identifies active users and efficiently estimates their channel parameters using sparsity. The second stage utilizes partial Channel State Information (CSI) and spatial-temporal correlation to estimate inactive users' channel coefficients, adopting a spatial-temporal-spectral (STS) framework based on convolutional neural networks (CNNs). The second part addresses enhancing a Multiple-Input Multiple-Output (MIMO) Radar and Communication Coexistence (RCC) system using RIS deployment. To mitigate interference in a Multi-User (MU)-MIMO RCC setup, we propose a strategic RIS deployment scheme, leveraging their passive beamforming capabilities. This dissertation provides insights into RIS-assisted MIMO channel estimation and practical RIS implementation for wireless system design, advancing RIS-assisted wireless communication across various applications.

Reconfigurable intelligent surfaces, Channel estimation, MIMO, Signal Processing, RIS, IRS, ISAC, Radar and communication coexistence, integrated sensing and communication (ISAC), machine learning, CNN
Portions of this document appear in: E. Shtaiwi, H. Zhang, S. Vishwanath, M. Youssef, A. Abdelhadi and Z. Han, ”Channel Estimation Approach for RIS Assisted MIMO Systems,” IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 2, pp. 452-465, June 2021; and in: Shtaiwi, Eyad, Hongliang Zhang, Ahmed Abdelhadi, A. Lee Swindlehurst, Zhu Han, and H. Vincent Poor. "Sum-rate Maximization for RIS-assisted Integrated Sensing and Communication Systems with Manifold Optimization." IEEE Transactions on Communications (2023); and in: E. Shtaiwi, A. Hussein, A. Khawar, A. Alkhateeb, A. Abdelhadi, and Z. Han, ”Implementation of Real-Time Adversarial Attacks on DNN-based Modulation Classifier,” International Conference on Computing, Networking, and Communications 2023, Honolulu, HI, USA, Feb. 2023; and in: E. Shtaiwi, A. E. Ouadrhiri, M. Moradikia, S. Sultana, A. Abdelhadi and Z. Han, ”Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks,” IEEE Global Communications Conference (GLOBECOM), Rio de Janeiro, Brazil, Dec. 2022; and in: E. Shtaiwi, H. Zhang, A. Abdelhadi and Z. Han, ”Sum-rate Maximization for RIS-assisted Radar and Communication Coexistence System,” IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, Dec. 2021; and in: E. Shtaiwi, H. Zhang, A. Abdelhadi and Z. Han, ”RIS-Assisted mmWave Channel Estimation Using Convolutional Neural Networks,” IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Nanjing, China, Mar. 2021.