P
Pavel Mach
Researcher at Czech Technical University in Prague
Publications - 178
Citations - 5075
Pavel Mach is an academic researcher from Czech Technical University in Prague. The author has contributed to research in topics: Adhesive & Handover. The author has an hindex of 19, co-authored 171 publications receiving 3834 citations.
Papers
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Journal ArticleDOI
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
Pavel Mach,Zdenek Becvar +1 more
TL;DR: This paper describes major use cases and reference scenarios where the mobile edge computing (MEC) is applicable and surveys existing concepts integrating MEC functionalities to the mobile networks and discusses current advancement in standardization of the MEC.
Journal ArticleDOI
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
Pavel Mach,Zdenek Becvar +1 more
TL;DR: In this paper, the authors present a survey of the research on computation offloading in mobile edge computing (MEC), focusing on user-oriented use cases and reference scenarios where the MEC is applicable.
Journal ArticleDOI
In-Band Device-to-Device Communication in OFDMA Cellular Networks: A Survey and Challenges
TL;DR: A thorough overview of the state of the art focusing on D2D communication, especially within 3GPP LTE/LTE-A, and highlights areas not satisfactorily addressed so far and outlines major challenges for future work regarding efficient integration of D1D in cellular networks.
Proceedings ArticleDOI
Adaptive Hysteresis Margin for Handover in Femtocell Networks
Zdenek Becvar,Pavel Mach +1 more
TL;DR: The purpose of this paper is to propose mechanism with minimum requirements on conventional network and user’s equipment and with a simple implementation of actual level of hysteresis margin according to the position of the user in a cell for elimination of redundant handovers.
Journal ArticleDOI
Joint Positioning of Flying Base Stations and Association of Users: Evolutionary-Based Approach
TL;DR: This paper proposes an algorithm that associates users with the most suitable SBS/FlyBS and finds optimal positions of all FlyBSs and investigates the performance of two proposed approaches for the joint association and positioning based on the genetic algorithm (GA) and particle swarm optimization (PSO).