Arash Asadi

TL;DR: In this article, a taxonomy based on the D2D communicating spectrum and review the available literature extensively under the proposed taxonomy is provided, which provides new insights to the over-explored and underexplored areas which lead to identify open research problems of D2DM communication in cellular networks.

TL;DR: A taxonomy for opportunistic schedulers is provided, which is based on scheduling design's objectives, to unveil two major issues: (i) the research in opportunistic is mature enough to jump from pure theory to implementation, and (ii) there are still under-explored and interesting research areas in opportunism scheduling.

TL;DR: This work proposes an online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems as a contextual multi-armed bandit problem and proposes a lightweight context-aware onlinelearning algorithm, namely fast machine learning (FML), with proven performance bound and guaranteed convergence.

TL;DR: This work proposes the first online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems as a contextual multi-armed bandit problem and proposes a lightweight context-aware onlinelearning algorithm, namely FML, with proven performance bound and guaranteed convergence.