Joint User Grouping and Power Allocation for MISO Systems: Learning to Schedule. Yuan, Y., Vu, T. X., Lei, L., Chatzinotas, S., & Ottersten, B. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 1-5, Sep., 2019.
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Paper doi abstract bibtex
Paper doi abstract bibtex In this paper, we address ajoint user scheduling and power allocation problem from a machine-learning perspective in order to efficiently minimize data delivery time for multiple-input single-output (MISO) systems. The joint optimization problem is formulated as a mixed-integer and non-linear programming problem, such that the data requests can be delivered by minimum delay, and the power consumption can meet practical requirements. For solving the problem to the global optimum, we provide a solution to decouple the scheduling and power optimization. Due to the problem's inherent hardness, the optimal solution requires exponential complexity and time in computations. To enable an efficient and competitive solution, we propose a learning-based approach to reduce data delivery time and solution's computational delay, where a deep neural network is trained to learn and decide how to optimize user scheduling. In numerical study, the developed optimal solution can be used for performance benchmarking and generating training data for the proposed learning approach. The results demonstrate the developed learning based approach is able to significantly improve the computation efficiency while achieves a near optimal performance.
@InProceedings{8902514,
author = {Y. Yuan and T. X. Vu and L. Lei and S. Chatzinotas and B. Ottersten},
booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
title = {Joint User Grouping and Power Allocation for MISO Systems: Learning to Schedule},
year = {2019},
pages = {1-5},
abstract = {In this paper, we address ajoint user scheduling and power allocation problem from a machine-learning perspective in order to efficiently minimize data delivery time for multiple-input single-output (MISO) systems. The joint optimization problem is formulated as a mixed-integer and non-linear programming problem, such that the data requests can be delivered by minimum delay, and the power consumption can meet practical requirements. For solving the problem to the global optimum, we provide a solution to decouple the scheduling and power optimization. Due to the problem's inherent hardness, the optimal solution requires exponential complexity and time in computations. To enable an efficient and competitive solution, we propose a learning-based approach to reduce data delivery time and solution's computational delay, where a deep neural network is trained to learn and decide how to optimize user scheduling. In numerical study, the developed optimal solution can be used for performance benchmarking and generating training data for the proposed learning approach. The results demonstrate the developed learning based approach is able to significantly improve the computation efficiency while achieves a near optimal performance.},
keywords = {integer programming;learning (artificial intelligence);neural nets;nonlinear programming;scheduling;joint user grouping;MISO systems;user scheduling;power allocation problem;machine-learning perspective;data delivery time;multiple-input single-output systems;joint optimization problem;mixed-integer;nonlinear programming problem;data requests;power consumption;power optimization;inherent hardness;exponential complexity;learning-based approach;computational delay;developed optimal solution;performance benchmarking;training data;learning based approach;computation efficiency;Resource management;Processor scheduling;Power control;Optimal scheduling;MISO communication;Interference;Time minimization;machine learning;power allocation;user scheduling.},
doi = {10.23919/EUSIPCO.2019.8902514},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533780.pdf},
}Downloads: 0
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