6 citations · 10 across the 5 of their papers we have counts for
6 papers
QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning
Jakob Stigenberg, Vidit Saxena, Soma Tayamon +1
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 o…
Reinforcement Learning for Efficient and Tuning-Free Link Adaptation
Vidit Saxena, Hugo Tullberg, Joakim Jaldén
Wireless links adapt the data transmission parameters to the dynamic channel state -- this is called link adaptation. Classical link adaptation relies on tuning parameters that are…
Thompson Sampling for Linearly Constrained Bandits
Vidit Saxena, Joseph E. Gonzalez, Joakim Jaldén
We address multi-armed bandits (MAB) where the objective is to maximize the cumulative reward under a probabilistic linear constraint. For a few real-world instances of this proble…
Constrained Thompson Sampling for Wireless Link Optimization
Vidit Saxena, Joseph E. Gonzalez, Ion Stoica +2
Wireless communication systems operate in complex time-varying environments. Therefore, selecting the optimal configuration parameters in these systems is a challenging problem. Fo…
SpotNet - Learned iterations for cell detection in image-based immunoassays
Pol del Aguila Pla, Vidit Saxena, Joakim Jaldén
Accurate cell detection and counting in the image-based ELISpot and FluoroSpot immunoassays is a challenging task. Recently proposed methodology matches human accuracy by leveragin…
Deep Learning for Frame Error Probability Prediction in BICM-OFDM Systems
Vidit Saxena, Joakim Jaldén, Mats Bengtsson +1
In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corr…