Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/33068
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dc.contributor.advisorAl-Raweshidy, H-
dc.contributor.advisorItagaki, T-
dc.contributor.authorAhmed AL-Joudi, Aya Khalid-
dc.date.accessioned2026-03-30T11:29:52Z-
dc.date.available2026-03-30T11:29:52Z-
dc.date.issued2026-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/33068-
dc.descriptionThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University Londonen_US
dc.description.abstractThis thesis presents a novel approach to design and implement a new channel coding method combined with a Adaptive RIS (ARIS) to enhance Terahertz (THz) communication in 6G networks. The research addresses the crucial requirements of 6G communication, including ultra-fast data transmission, minimal delay, extensive connectivity, and optimal energy usage. The innovative channel coding approaches, Polar Convolutional Serial Code (PCSC) and Polar Convolutional Parallel Code (PCPC), are specifically designed to enhance the reliability and data transfer rate of wireless communication systems operating at THz frequencies. Their performance is rigorously evaluated in congested network conditions, a common scenario in 6G applications, in conjunction with Non Orthogonal Multiple Access (NOMA) strategies. A key achievement in this research is the integration of ARIS into the commu-nication system, leading to the development of a ARIS Decision Making Algorithm (ARIS-DMA). This technology optimises signal strength and coverage by dynamically adjusting surface reflection and transmission properties based on the user’s location and network conditions. The ARIS-DMA effectively reduces power loss and latency, providing comprehensive coverage and a 70% signal power loss reduction, instilling confidence of users about the progress in the field. In addition, the thesis investigates the application of Deep Learning (DL) methods for decoding PCPC. It suggests a Deep Q Network (DQN) based Deep Q Network ARISDMA (DQN-ARISDMA) to improve beamforming and increase spectral efficiency. The findings exhibit significant enhancements in data transmission speeds, utilisation of the frequency spectrum, and the ability of the system to respond promptly, all of which are vital for time-sensitive applications in 6G networks. The outcomes of this study contribute significantly to the development of communication systems that can meet the rigorous standards of future 6G networks while also being scalable, energy-efficient, and reliable. This advancement creates opportunities for progress in areas such as smart cities, autonomous vehicles, and augmented/virtual reality experiences, demonstrating the practical implications of our research.en_US
dc.publisherBrunel University Londonen_US
dc.relation.urihttps://bura.brunel.ac.uk/handle/2438/33068/1/FulltextThesis.pdf-
dc.subjectMetaSurafceen_US
dc.subjectMachine Learningen_US
dc.subjectBeyond 5G Communicationen_US
dc.subjectHolographic Intelligent Surfaceen_US
dc.subjectNext Wireless Generationen_US
dc.titleDesign and development of an AI enhanced channel coding technique with adaptive reconfigurable intelligent surface for terahertz 6G communicationen_US
dc.typeThesisen_US
Appears in Collections:Electronic and Electrical Engineering
Department of Electronic and Electrical Engineering Theses

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