@phdthesis{,
    author = {Zhu, Yazhou},
    title = {Distributed Resource Optimization for NOMA Transmission in Beamforming SATCOM},
    editor = {},
    booktitle = {},
    series = {},
    journal = {},
    address = {},
    publisher = {},
    edition = {},
    year = {2023},
    isbn = {},
    volume = {},
    number =  {},
    pages = {},
    url = {},
    doi = {},
    keywords = {Multigateway multibeam satellite, distributed resource optimization, nonorthogonal multiple access, beamforming, flexible payload power resource allocation, weighted sum-rate maximization, per-feed available power constraints.},
    abstract = {To increase the throughput and user connectivity of existing satellite communication (SATCOM) systems, this work studies the application of nonorthogonal multiple access (NOMA) transmission in beamforming (BF) based forward links, where multiple users are simultaneously served by the same beam. To effectively implement nonorthogonal SATCOM, the feeder link limitations and multibeam satellite payload constraints must be considered for BF design and power allocation (PA) optimization. 
To address these challenges, distributed resource optimization strategies are investigated for BF and flexible payload power resource allocation in multigateway (multi-GW) nonorthogonal SATCOM systems. Specifically, a per-feed available power-constrained BF strategy via maximization of the worst-user signal-to-leakage-and-noise ratio (SLNR) is explored with local channel state information (CSI) for a distributed operation of GWs. As an upper-bound performance benchmark, a centralized multilayer BF strategy is processed in a central unit with full global CSI and data sharing. Moreover, a weighted sum-rate maximization-based (WSRM-based) payload power resource optimization strategy is locally applied at each GW to efficiently use payload power resources for higher performance increment, depending on the actual traffic demand. The nonconvex WSRM problem is further solved by the weighted sum-MSE minimization-based (WMMSE-based) and deep neural network-based (DNN-based) methods. Finally, an efficient user scheduling is designed to enable the operator to capture a substantial system-throughput gain. Accurate mathematical modeling for a realistic SATCOM scenario is conducted and applied to design the resource optimization strategies. The results over the realistic simulation environment show the efficiency of our strategies.},
    note = {},
    
    school = {Universität der Bundeswehr München},
    
}