Energy efficiency and statistical QoS provisioning under Markovian arrivals for power-limited MTC networks
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
Lecture Hall IT115, Linnanmaa Campus
Topic of the dissertation
Energy efficiency and statistical QoS provisioning under Markovian arrivals for power-limited MTC networks
Doctoral candidate
Master of Science (Technology) Fahad Rahim Qasmi
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Centre for Wireless Communication
Subject of study
Communications Engineering
Opponent
Professor Kimmo Juhani Kansanen, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
Custos
Professor Hirley Alves, University of Oulu
Energy-efficient and reliable communication for Internet of Things devices
The emergence of 6G and beyond is expected to enable massive Internet of Things (IoT) deployments across diverse industrial verticals, introducing significant constraints in terms of throughput, latency, reliability, and energy efficiency. Since these devices typically operate under limited power resources and generate sporadic and bursty traffic while simultaneously requiring stringent Quality of Service (QoS) guarantees, meeting these demands remains a critical challenge. Motivated by these challenges, energy-efficient wireless communication strategies are required for battery constrained IoT systems which prolong device lifetime and ensure sustainable network operation.
In this context, the thesis first investigates the throughput of wireless transmissions for delay sensitive data under both constant and random arrival rates. In particular, Markovian arrival models are considered to characterise the arrival rates subject to statistical queueing constraints. Second, we proposed energy-efficient wireless communication under realistic traffic and channel conditions. A resource allocation framework is proposed that exploits traffic arrival characteristics and buffer state information to enhance energy efficiency while ensuring sustainable operation for energy constrained IoT systems. Finally, a comprehensive performance evaluation of energy efficiency is carried out under different channel models, including Nakagami-𝑚 and ℱ-composite fading channels, which capture both small-scale and large-scale fading effects.
In addition, the study employs an effective capacity framework to investigate the sustainable throughput of wireless transmissions. Markovian traffic models are incorporated to capture the random characteristics of IoT traffic. The Effective Energy Efficiency (EEE) is evaluated by considering Power Allocation (PA), Rate Allocation (RA), and jointly considering PA and RA under both constant and random traffic arrivals. Furthermore, a power consumption model based on the probability of a Non-Empty Buffer (NBP) is incorporated into the EEE framework to account for traffic-aware transmission activity under random arrivals.
The results reveal important trade-offs among energy efficiency, transmission power, rate, delay, and channel conditions, and demonstrate that joint optimisation of power and rate significantly improves system performance.
In this context, the thesis first investigates the throughput of wireless transmissions for delay sensitive data under both constant and random arrival rates. In particular, Markovian arrival models are considered to characterise the arrival rates subject to statistical queueing constraints. Second, we proposed energy-efficient wireless communication under realistic traffic and channel conditions. A resource allocation framework is proposed that exploits traffic arrival characteristics and buffer state information to enhance energy efficiency while ensuring sustainable operation for energy constrained IoT systems. Finally, a comprehensive performance evaluation of energy efficiency is carried out under different channel models, including Nakagami-𝑚 and ℱ-composite fading channels, which capture both small-scale and large-scale fading effects.
In addition, the study employs an effective capacity framework to investigate the sustainable throughput of wireless transmissions. Markovian traffic models are incorporated to capture the random characteristics of IoT traffic. The Effective Energy Efficiency (EEE) is evaluated by considering Power Allocation (PA), Rate Allocation (RA), and jointly considering PA and RA under both constant and random traffic arrivals. Furthermore, a power consumption model based on the probability of a Non-Empty Buffer (NBP) is incorporated into the EEE framework to account for traffic-aware transmission activity under random arrivals.
The results reveal important trade-offs among energy efficiency, transmission power, rate, delay, and channel conditions, and demonstrate that joint optimisation of power and rate significantly improves system performance.
Created 31.10.2026 | Updated 2.11.2026