AI SearchDownlink joint communication and sensing beyond 5G (6G) systems using deep learning
Call Number: AIT Diss. no.TC-24-01 Material type:
SeriesSeries: Asian Institute of Technology. Dissertation ; no. TC-24-01Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2024Description: 133 leaves : ill.+ 1 online resourceSubject(s): Online resources: Dissertation note: Thesis (Ph.D.) - Asian Institute of Technology, 2024 Summary: 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.
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Asian Institute of Technology Library Archives | AIT Diss. no.TC-24-01 (Browse shelf(Opens below)) | 1 | Not for loan |
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
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.
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