M. Kalntis
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6 records found
1
In these environments where traffic patterns change rapidly, signal qualities fluctuate unpredictably and cost/availability of resources is uncertain, it becomes apparent that static control rules and legacy mechanisms built on heuristics are poorly suited. In this context, the evolution of mobile network architectures, particularly the emergence of open Radio Access Network (RAN), represents a necessary and enabling change. The O-RAN Alliance, for example, is a global initiative aimed at softwarizing and standardizing RANs to improve their performance, reduce costs, and lower the entry barrier for a broader vendor ecosystem. It enables scalable, datadriven control loops that can be implemented centrally by intelligent controllers and enforced at different time scales, namely, near-real-time (near-RT) and non-real-time (non-RT). In this way, it becomes possible to embed online learning solutions in the RAN itself, where data are collected and used for effective and robust learning.
This dissertation responds to these challenges by developing online (meta-) learning algorithms for two coupled control layers in O-RAN: (i) mobility management (via user-cell association and traditional/conditional handovers) and (ii) resource allocation (via threshold, non-RT policies) for virtualized base stations. Online learning provides a principled way to make sequential decisions under uncertainty, and online meta-learning enables the system to combine various (online) learners, each tailored for different environments, achieving both effectiveness, which translates to high performance under all conditions, as well as robustness, which ensures this high performance without knowing precisely the conditions. All proposed methods deliver operation guarantees under all conditions (from stationary to even adversarial dynamics), as well as practical gains on country-scale operator data and O-RAN-compatible testbeds.
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In these environments where traffic patterns change rapidly, signal qualities fluctuate unpredictably and cost/availability of resources is uncertain, it becomes apparent that static control rules and legacy mechanisms built on heuristics are poorly suited. In this context, the evolution of mobile network architectures, particularly the emergence of open Radio Access Network (RAN), represents a necessary and enabling change. The O-RAN Alliance, for example, is a global initiative aimed at softwarizing and standardizing RANs to improve their performance, reduce costs, and lower the entry barrier for a broader vendor ecosystem. It enables scalable, datadriven control loops that can be implemented centrally by intelligent controllers and enforced at different time scales, namely, near-real-time (near-RT) and non-real-time (non-RT). In this way, it becomes possible to embed online learning solutions in the RAN itself, where data are collected and used for effective and robust learning.
This dissertation responds to these challenges by developing online (meta-) learning algorithms for two coupled control layers in O-RAN: (i) mobility management (via user-cell association and traditional/conditional handovers) and (ii) resource allocation (via threshold, non-RT policies) for virtualized base stations. Online learning provides a principled way to make sequential decisions under uncertainty, and online meta-learning enables the system to combine various (online) learners, each tailored for different environments, achieving both effectiveness, which translates to high performance under all conditions, as well as robustness, which ensures this high performance without knowing precisely the conditions. All proposed methods deliver operation guarantees under all conditions (from stationary to even adversarial dynamics), as well as practical gains on country-scale operator data and O-RAN-compatible testbeds.
Handovers (HOs) are the cornerstone of modern cellular networks for enabling seamless connectivity to a vast and diverse number of mobile users. However, as mobile networks become more complex with more diverse users and smaller cells, traditional HOs face significant challenges, such as prolonged delays and increased failures. To mitigate these issues, 3GPP introduced conditional handovers (CHOs), a new type of HO that enables the preparation (i.e., resource allocation) of multiple cells for a single user to increase the chance of HO success and decrease the delays in the procedure. Despite its advantages, CHO introduces new challenges that must be addressed, including efficient resource allocation and managing signaling/communication overhead from frequent cell preparations and releases. This paper presents a novel framework aligned with the O-RAN paradigm that leverages meta-learning for CHO optimization, providing robust dynamic regret guarantees and demonstrating at least 180% superior performance than other 3GPP benchmarks in volatile signal conditions.
Open RAN systems, with their virtualized base stations (vBSs), offer increased flexibility and reduced costs, vendor diversity, and interoperability. However, optimizing the allocation of radio resources in such systems raises new challenges due to the volatile vBSs operation, and the dynamic network conditions and user demands they are called to support. Leveraging the novel O-RAN multi-tier control architecture, we propose a new set of resource allocation threshold policies with the aim of balancing the vBSs' performance and energy consumption in a robust and provably optimal fashion. To that end, we introduce an online learning algorithm that operates under minimal assumptions and without requiring knowledge of the environment, hence being suitable even for "challenging"environments with non-stationary or adversarial demands and conditions. We also develop a meta-learning scheme that utilizes other available algorithmic schemes, e.g., tailored for more "easy"environments, by choosing dynamically the best-performing algorithm; thus enhancing the system's effectiveness. We prove that the proposed solutions achieve sub-linear regret (zero optimality gap), and characterize their dependence on the main system parameters. The performance of the algorithms is evaluated with real-world data from a testbed, in stationary and adversarial conditions, indicating energy savings of up to 64.5% compared with several state-of-the-art benchmarks.
Through the Telco Lens
A Countrywide Empirical Study of Cellular Handovers
Cellular networks rely on handovers (HOs) as a fundamental element to enable seamless connectivity for mobile users. A comprehensive analysis of HOs can be achieved through data from Mobile Network Operators (MNOs); however, the vast majority of studies employ data from measurement campaigns within confined areas and with limited end-user devices, thereby providing only a partial view of HOs. This paper presents the first countrywide analysis of HO performance, from the perspective of a top-tier MNO in a European country. We collect traffic from approximately 40M users for 4 weeks and study the impact of the radio access technologies (RATs), device types, and manufacturers on HOs across the country. We characterize the geo-temporal dynamics of horizontal (intra-RAT) and vertical (inter-RATs) HOs, at the district level and at millisecond granularity, and leverage open datasets from the country's official census office to associate our findings with the population. We further delve into the frequency, duration, and causes of HO failures, and model them using statistical tools. Our study offers unique insights into mobility management, highlighting the heterogeneity of the network and devices, and their effect on HOs.