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Liljana Gavrilovska
Researcher at Saints Cyril and Methodius University of Skopje
Publications - 205
Citations - 2330
Liljana Gavrilovska is an academic researcher from Saints Cyril and Methodius University of Skopje. The author has contributed to research in topics: Cognitive radio & Wireless network. The author has an hindex of 23, co-authored 202 publications receiving 2103 citations. Previous affiliations of Liljana Gavrilovska include Microsoft.
Papers
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Journal ArticleDOI
Analysis of the Decoupled Access for Downlink and Uplink in Wireless Heterogeneous Networks
TL;DR: This work derives the association probability for decoupled DL/UL access using the framework of stochastic geometry and analyzes the impact that this type of association has on the average throughput in the system.
Journal ArticleDOI
Analysis of the Decoupled Access for Downlink and Uplink in Wireless Heterogeneous Networks
TL;DR: In this paper, the authors derived the association probability for DL/UL access in femto and macro BSs and analyzed the impact that this type of association has on the average throughput in the system.
Journal ArticleDOI
Learning and Reasoning in Cognitive Radio Networks
TL;DR: Insight is provided into the mechanisms for obtaining and inferring knowledge that clearly set apart the cognitive radio networks from other wireless solutions.
Journal ArticleDOI
Medium Access Control Protocols in Cognitive Radio Networks: Overview and General Classification
TL;DR: The survey offers extensive overview on the state-of-the-art advances in C- MAC protocol engineering by reviewing existing and up-to-date technical solutions, identifies their basic characteristics and maps them into the C-MAC cycle, regardless of the operational scenario and settings.
Proceedings ArticleDOI
Reliability of a radio environment Map: Case of spatial interpolation techniques
TL;DR: Several spatial interpolation techniques based on Inverse Distance Weighting are analyzed and compared in terms of reliability bounds of the interpolation errors for an indoor environment and performance evaluation shows that they can provide a robust and reliable RIF estimation within the entire REM concept.