Pham Ngoc Luat creator Attaphongse Taparugssanagorn Chairperson Kamol Kaemarungsi Co-chairperson Poompat Saengudomlert Examination Committee Chaklam Silpasuwanchai Examination Committee His Majesty the King{u2019}s Scholarships (Thailand) Scholarship donor text series theses th Pathum Thani, Thailand Asian Institute of Technology 2024 continuing eng

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133 leaves : ill.+ 1 online resource This research explores the integration of communication and radar systems in Joint Communication and Sensing (JCAS) beyond 5G (6G) using Deep Learning (DL). By sharing resources and minimizing interference, communication and radar systems can func tion simultaneously. The study focuses on designing and optimizing JCAS signals and sys tems, addressing spectral efficiency, transmission power, and antenna use. It investigates waveform candidates and tests system performance over both theoretical and realistic chan nels in various scenarios. A proposed Multiple-Input Multiple-Output (MIMO)-JCAS Base Station (BS) processes downlink communication and echo signals using interference can cellation. Additionally, a Deep Neural Network (DNN) method for channel estimation and signal detection is introduced, showing superior performance in Bit Error Rate (BER) against Signal-to-Noise Ratio (SNR) compared to traditional methods. However, the study excludes other layers beyond the physical layer, hardware implementation, uplink sensing, resource allocation, and signal parameter estimation. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Telecommunications Thesis (Ph.D.) - Asian Institute of Technology, 2024 Integrated sensing and communications 5G mobile communication systems Deep learning (Machine learning) Asian Institute of Technology. http://203.159.5.9/ait-thesis/detail.php?q=B23926 http://203.159.5.9/ait-thesis/detail.php?q=B23926 260407 20260818094136.0