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- W4387129475 abstract "Efficient renewable resource integration is a key pillar of the evolving smart grid vision to shift from fossil fuels. Microgrids (MGs), empowered by distributed energy resources (DERs), are a promising avenue toward this goal. In MG communications, robust radio resource management is essential, requiring mature wireless technologies that accommodate growing network size and diverse services. This work demonstrates the capabilities of coverage enhancement, which is a feature of the narrow-band internet of things to support the performance of future massive MG networks. We formulate a problem to maximize the weighted sum-rate of DERs with different quality of service classes. Our objective is to optimally allocate power levels to the users and perform frequency scheduling. A three-step approach is proposed to solve the highly nonconvex problem, where difference-of-convex tools are invoked to address the power control subproblem. Next, a low-complexity distributed heuristic is used for sub-carrier scheduling, leveraging data-driven methods and mixed-integer nonlinear programming for network compression and coordination. The base stations’ coverage enhancement radii are optimized using a distributed multi-armed-bandit-based algorithm. Furthermore, numerical simulations reveal that our integrated solution not only outperforms benchmarks based on a genetic algorithm and round-robin scheduling but also performs comparably to solutions using pure mixed-integer nonlinear programming for optimal scheduling." @default.
- W4387129475 created "2023-09-29" @default.
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- W4387129475 date "2023-01-01" @default.
- W4387129475 modified "2023-10-18" @default.
- W4387129475 title "Joint Optimization of Radio Resources and Coverage Enhancement in Massive Microgrid Networks" @default.
- W4387129475 doi "https://doi.org/10.1109/tnsm.2023.3320517" @default.
- W4387129475 hasPublicationYear "2023" @default.
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