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Multi-IRS Aided Mobile Edge Computing for High Reliability and Low Latency Services
Harnessing the IRS's potential in enhancing the performance of edge computation offloading, in this paper, we study the optimized use of a system of multi-IRS along with the design of the offloading (to an edge with multi MECs) and resource allocation parameters for the purpose of minimizing the devices' energy consumption considering 5G services with stringent latency and reliability requirements.
Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey
Furthermore, we provide an overview of the ML techniques that have been considered in this context.
A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT
To this end, we propose a novel deep learning-based framework for compressing and quantizing the observations of correlated sensors.
PRVNet: A Novel Partially-Regularized Variational Autoencoders for Massive MIMO CSI Feedback
In a multiple-input multiple-output frequency-division duplexing (MIMO-FDD) system, the user equipment (UE) sends the downlink channel state information (CSI) to the base station to report link status.
