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Found 5 papers, 2 papers with code
QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning
Fifth-generation (5G) New Radio (NR) cellular networks support a wide range of new services, many of which require an application-specific quality of service (QoS), e. g. in terms of a guaranteed minimum bit-rate or a maximum tolerable delay.
Reinforcement Learning for Efficient and Tuning-Free Link Adaptation
In this context, we propose a latent learning model for link adaptation that exploits the correlation between data transmission parameters.
Thompson Sampling for Linearly Constrained Bandits
We address multi-armed bandits (MAB) where the objective is to maximize the cumulative reward under a probabilistic linear constraint.
Constrained Thompson Sampling for Wireless Link Optimization
We model rate selection as a stochastic multi-armed bandit (MAB) problem, where a finite set of transmission rates are modeled as independent bandit arms.
SpotNet - Learned iterations for cell detection in image-based immunoassays
Accurate cell detection and counting in the image-based ELISpot and FluoroSpot immunoassays is a challenging task.
