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Abstract

Millimeter wave (mmWave) bands have been introduced as a new spectrum for fifth-generation (5G) cellular networks to increase network capacity and data rate. However, significant path loss in mmWave bands hinders their successful deployment. To cope with this, mmWave hybrid beamforming (HBF) technique has been proposed. Despite its benefits, adopting mmWave HBF presents the challenge of high computational complexity. Leveraging the sparse characteristics of mmWave channels, in our first contribution, we propose iterative approaches with low computational complexity to derive hybrid beamforming matrices. Moreover, 5G networks face the problem of delivering services with diverse quality-of-service (QoS) requirements, commonly referred to as the coexistence problem. State-of-the-art approaches for handling this problem lack joint optimization of the performance for both enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users. To address this gap, our second contribution proposes a hybrid puncturing and superposition scheme that utilizes multi-objective optimization to maximize both the data rate of eMBB users and the admission rate of URLLC users. Beyond fifth-generation cellular networks aim to surpass the capabilities of 5G systems by delivering higher data rates, enhanced reliability, and reduced latency, all while improving energy efficiency. To support this improvement, emerging technologies, including intelligent reflecting surfaces (IRSs) and rate-splitting multiple access (RSMA) will be integrated into future wireless systems. In this thesis, we tackle the challenges associated with implementing the aforementioned enabling technologies to improve the QoS of eMBB and URLLC users. In our third contribution, we explore the advantages of a dual active-passive IRS design to enhance the energy efficiency of the eMBB use case. As our fourth contribution, we employ a simultaneously transmitting and reflecting (STAR)-IRS to further extend the service coverage of URLLC users on both sides of an IRS. We design an optimized codebook for pre-designing phase shifts of STAR-IRSs to maximize the number of admitted URLLC users. Finally, in our last contribution, we utilize an active IRS to improve the performance of RSMA technique. Specifically, we study the resource allocation design to minimize the power consumption of the base station and the active IRS under the QoS constraints for the URLLC users.