Advanced Techniques for Channel Modeling, Estimation, and Resource Allocation Optimization in 5G/6G Wireless Communication Systems - Guerra, David William Marques (2024)
Several emerging technologies have being proposed to meet the growing demand for highspeed, reliable, and high-quality communications for the next-generation (6G) mobile systems. The Extra-Large Multiple-Input Multiple-Output (XL-MIMO) system, a MIMO communication system with a very large number of antennas, is presented as a promising solution, as well as Reconfigurable Intelligent Surfaces (RIS), which are structures composed of reflecting elements capable of altering the phase and amplitude of the reflected signal, thus improving communication performance. Building on the potential of these techniques, this thesis explores the characteristics, challenges, and solutions associated with implementing these schemes in mobile communication systems. The first part proposes a double-scattering XL-MIMO channel modeling, considering specific concepts of this scenario and evaluating the impact of the spatial and temporal evolution of the dynamic environment. We observe that the birth and death processes of scatterers and clusters considerably impact system performance. In the second part, a channel estimator is proposed for RIS-assisted systems using signal compression techniques. More specifically, an efficient estimation of sparse channel gains using a modified redundant dictionary to reconstruct the BS-RIS/UE-RIS links in RIS-aided communication is proposed. This is achieved through a compressed sensing (CS) based method called Matching Pursuit with Phase Rotation (MP-PR), which uses a few active elements on the RIS panel. This procedure aims to optimize the efficiency and accuracy of channel estimation, representing one of the main challenges for effectively integrating RIS in beyond 5G (B5G) wireless networks. The third part of this work proposes an energy-efficient uplink power control for RIS-aided IoT systems. To address the battery limitations of IoT devices, the approach employs Riemannian manifolds and a convex power allocation solution to improve energy efficiency (EE) by reducing IoT transmit power in RIS-aided uplink Massive MIMO (M-MIMO) systems. Numerical results indicate significant improvements in the total power consumption of the devices compared to existing techniques. Additionally, an alternative approach using statistical channel state information (CSI) demonstrates comparable performance with reduced complexity