Efficient Minimum-Energy Scheduling with Machine-Learning Based Predictions for Multiuser MISO Systems

• We address an energy-efficient scheduling problem for practical multiple-input single-output (MISO) systems with stringent execution-time requirements. Optimal user-group scheduling is adopted to enable timely and energy-efficient data transmission, such that all the users' demand can be delivered within a limited time. The high computational complexity in optimal iterative algorithms limits their applications in real-time network operations. In this paper, we rethink the conventional optimization algorithms, and embed machine-learning based predictions in the optimization process, aiming at improving the computational efficiency and meeting the stringent execution-time limits in practice, while retaining competitive energy-saving performance for the MISO system. Numerical results demonstrate that the proposed method, i.e., optimization with machine- learning predictions (OMLP), is able to provide a time-efficient and high-quality solution for the considered scheduling problem. Towards online scheduling in real-time communications, OMLP is of high computational efficiency compared to conventional optimal iterative algorithms. OMLP guarantees the optimality as long as the machine- learning based predictions are accurate.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)

Keyword

computational complexity;energy conservation;iterative methods;learning (artificial intelligence);MISO communication;multi-access systems;optimisation;telecommunication computing;telecommunication power management;telecommunication scheduling;efficient minimum-energy scheduling;multiuser MISO systems;energy-efficient scheduling problem;multiple-input single-output systems;stringent execution-time requirements;optimal user-group scheduling;energy-efficient data transmission;high computational complexity;real-time network operations;optimization process;competitive energy-saving performance;MISO system;OMLP;high-quality solution;real-time communications;high computational efficiency;conventional optimal iterative algorithms;optimization algorithms;scheduling problem;optimization with machine learning predictions;Processor scheduling;Optimal scheduling;Scheduling;Machine learning;Data communication;Real-time systems

Publication and Content Type

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