Statistical resource allocation for ultra-reliable wireless networks
Thesis event information
Date and time of the thesis defence
Topic of the dissertation
Statistical resource allocation for ultra-reliable wireless networks
Doctoral candidate
Master of Science (Technology) Dian Echevarría Pérez
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, CWC- Radio Technology
Subject of study
Wireless Communication
Opponent
Professor Sinem Coleri, Koç University
Custos
Associate Professor Hirley Alves, University of Oulu
Smarter resource management for reliable wireless connections
Future wireless networks must support diverse services, including ultra-reliable
low-latency communications (URLLC), enhanced-mobile broadband (eMBB), and
massive machine-type communications (mMTC). The stringent reliability targets of
URLLC introduce major challenges for resource allocation, link adaptation, and overall
system design, which become even more demanding when URLLC coexists with
other service classes such as eMBB. This thesis develops optimization and reliability
enhancement methods that combine advanced statistical modeling, multi-antenna
processing, and location-aware network management.
First, the thesis studies beamforming with multiple antennas for URLLC and eMBB
coexistence in shared time-frequency resources. Because acquiring instantaneous
channel state information (i-CSI) for URLLC devices is often impractical due to
overhead and timing constraints, a history-based method is proposed that fuses past
URLLC channel measurements with i-CSI from eMBB users. In parallel, robust
resource allocation policies are designed for URLLC under imperfect i-CSI integrating
statistical tools such as extreme value theory (EVT) to characterize the tail behavior
of metrics such as the signal-to-interference-plus-noise ratio (SINR) while meeting
demanding reliability criteria. Next, spatial and localization information is exploited
through statistical radio maps to predict link quality and allocate resources at unmeasured locations. By integrating EVT with spatial radio maps, predictive reliability guarantees are achieved.
Additionally, the thesis explores how location-aware radio maps and EVT can enhance end-to-end (E2E) ultra-reliable communication (URC), with a focus on advanced handover (HO) strategies. Novel algorithms are proposed that trigger HO decisions using predictive spatial information and adaptive time-power control, reducing energy consumption while preserving reliable connectivity during mobility. The proposed methods in this thesis show potential to advance ultra-reliable wireless networking as a practical path for meeting the service demands of the fifth generation
(5G) and beyond wireless systems.
low-latency communications (URLLC), enhanced-mobile broadband (eMBB), and
massive machine-type communications (mMTC). The stringent reliability targets of
URLLC introduce major challenges for resource allocation, link adaptation, and overall
system design, which become even more demanding when URLLC coexists with
other service classes such as eMBB. This thesis develops optimization and reliability
enhancement methods that combine advanced statistical modeling, multi-antenna
processing, and location-aware network management.
First, the thesis studies beamforming with multiple antennas for URLLC and eMBB
coexistence in shared time-frequency resources. Because acquiring instantaneous
channel state information (i-CSI) for URLLC devices is often impractical due to
overhead and timing constraints, a history-based method is proposed that fuses past
URLLC channel measurements with i-CSI from eMBB users. In parallel, robust
resource allocation policies are designed for URLLC under imperfect i-CSI integrating
statistical tools such as extreme value theory (EVT) to characterize the tail behavior
of metrics such as the signal-to-interference-plus-noise ratio (SINR) while meeting
demanding reliability criteria. Next, spatial and localization information is exploited
through statistical radio maps to predict link quality and allocate resources at unmeasured locations. By integrating EVT with spatial radio maps, predictive reliability guarantees are achieved.
Additionally, the thesis explores how location-aware radio maps and EVT can enhance end-to-end (E2E) ultra-reliable communication (URC), with a focus on advanced handover (HO) strategies. Novel algorithms are proposed that trigger HO decisions using predictive spatial information and adaptive time-power control, reducing energy consumption while preserving reliable connectivity during mobility. The proposed methods in this thesis show potential to advance ultra-reliable wireless networking as a practical path for meeting the service demands of the fifth generation
(5G) and beyond wireless systems.
Last updated: 17.10.2025