https://hdl.handle.net/20.500.12761/1 2026-04-14T11:15:32Z 2026-04-14T11:15:32Z Karmegam, Arivarasan Kiffer, Lucianna Fernández Anta, Antonio https://hdl.handle.net/20.500.12761/2023 2026-04-14T00:00:20Z 2026-06-01T00:00:00Z

Exploiting Multi-Core Parallelism in Blockchain Validation and Construction Karmegam, Arivarasan; Kiffer, Lucianna; Fernández Anta, Antonio Blockchain validators can reduce block processing time by exploiting multi-core CPUs, but deterministic execution must preserve a given total order while respecting transaction conflicts and per-block runtime limits. This paper systematically examines how validators can exploit multi-core parallelism during both block construction and execution without violating blockchain semantics. We formalize two validator-side optimization problems: (i) executing an already ordered block on p cores to minimize makespan while ensuring equivalence to sequential execution; and (ii) selecting and scheduling a subset of mempool transactions under a runtime limit B to maximize validator reward. For both, we develop exact Mixed-Integer Linear Programming (MILP) formulations that capture conflict, order, and capacity constraints, and propose fast deterministic heuristics that scale to realistic workloads. Using Ethereum mainnet traces and including a Solana-inspired declared-access baseline (Sol) for ordered-block scheduling and a simple reward-greedy baseline (RG) for block construction, we empirically quantify the trade-offs between optimality and runtime. MILPs quickly become intractable as heterogeneity or core count increases, whereas our heuristics run in milliseconds and achieve near-optimal quality. For ordered-block execution, heuristic makespans are typically within a few percent of the MILP solutions (and can even surpass the MILP incumbent when the solver times out), yielding up to 1.5 speedup with p=2 and 2.3 speedup with p=8 over sequential execution, despite tight ordering constraints. For block construction, the heuristic achieves 99--100% of the MILP optimum reward on homogeneous workloads, and 74--100% of an LP-relaxation upper bound on heterogeneous workloads, where exact optimization often times out. The resulting block-construction throughput scales close to linearly with p, reaching up to 7.9 speedup with p=8 in our experiments. These results demonstrate that lightweight, conflict-aware scheduling and selection can unlock substantial parallelism in blockchain validation, bridging the gap between sequential execution and the true potential of multi-core hardware.

2026-06-01T00:00:00Z Mozhganfar, Mojtaba Khodarahmi, Masoumeh Lorenzi, Daniele Dolati, Mahdi Tashtarian, Farzad Khonsari, Ahmad Timmerer, Christian https://hdl.handle.net/20.500.12761/2022 2026-04-14T00:00:19Z 2026-05-01T00:00:00Z

QoE Modeling in Volumetric Video Streaming: A Short Survey Mozhganfar, Mojtaba; Khodarahmi, Masoumeh; Lorenzi, Daniele; Dolati, Mahdi; Tashtarian, Farzad; Khonsari, Ahmad; Timmerer, Christian Volumetric video streaming enables six degrees of freedom (6DoF) interaction, allowing users to navigate freely within immersive three-dimensional (3D) environments. Despite notable advancements, volumetric video remains an emerging field, presenting ongoing challenges and vast opportunities in content capture, compression, transmission, decompression, rendering, and display. As user expectations grow, delivering high Quality of Experience (QoE) in these systems becomes increasingly critical due to the complexity of volumetric content and the demands of interactive streaming. This paper reviews recent progress in QoE for volumetric streaming, beginning with an overview of QoE evaluation of volumetric video streaming studies, including subjective assessments tailored to 6DoF content. The core focus of this work is on objective QoE modeling, where we analyze existing models based on their input factors and methodological strategies. Finally, we discuss the key challenges and promising research directions for building perceptually accurate and adaptable QoE models that can support the future of immersive volumetric media.

2026-05-01T00:00:00Z Fezeu, Rostand A. K. Coelho de Freitas, Lilian Ramadan, Eman Carpenter, Jason Fiandrino, Claudio Widmer, Joerg Zhang, Zhi-Li https://hdl.handle.net/20.500.12761/2021 2026-04-14T00:00:16Z 2026-04-01T00:00:00Z

Spectrum & RAN Sharing: A Measurement-based Case Study of Commercial 5G Networks in Spain Fezeu, Rostand A. K.; Coelho de Freitas, Lilian; Ramadan, Eman; Carpenter, Jason; Fiandrino, Claudio; Widmer, Joerg; Zhang, Zhi-Li Radio Access Network (RAN) sharing, which often also includes spectrum sharing, is a strategic cooperative agreement among two or more mobile operators in which one operator may use another’s RAN infrastructure to provide mobile services to its users. By mutually sharing physical sites, radio elements, licensed spectrum, and other parts of the RAN infrastructure, participating operators can significantly reduce the capital (and operational) expenditure in deploying and operating cellular networks, while accelerating coverage expansion– thereby addressing the spectrum scarcity and infrastructure cost challenges in the 5G era and beyond. While the economic benefits of RAN sharing are well understood, the impact of such resource pooling on user-perceived performance remains underexplored, especially in real-world commercial deployments. We present, to the best of our knowledge, the first empirical measurement study of commercial 5G spectrum and RAN sharing. Our measurement study is unique in that, beyond identifying real-world instances of shared 5G spectrum and RAN deployment “in the wild”, we also analyze users’ perceived performance and its implication on Quality of Experience (QoE). Our study provides critical insights into resource management (i.e., pooling) and spectrum efficiency, offering a blueprint (and implications) for network evolution in 5G, 6G, and beyond.

2026-04-01T00:00:00Z Namvar, Reza Gallego Delgado, José Banchs, Albert Chatzieleftheriou, Livia Elena Ayala-Romero, Jose A. Garcia-Saavedra, Andres Fiore, Marco https://hdl.handle.net/20.500.12761/2020 2026-04-14T00:00:12Z 2026-06-15T00:00:00Z

Exploring the Viability of Automated Heuristic Design for 5G LDPC Decoding Namvar, Reza; Gallego Delgado, José; Banchs, Albert; Chatzieleftheriou, Livia Elena; Ayala-Romero, Jose A.; Garcia-Saavedra, Andres; Fiore, Marco Automated Heuristic Design (AHD) leverages Large Language Models (LLMs) and task-specific evaluation to search among existing algorithmic components and generate novel ones. This paper studies AHD for 5G New Radio (NR) Low-Density Parity Check (LDPC) decoding by evolving the Check Node Update (CNU) function used in iterative Belief Propagation (BP). To this end, we implement a flexible AHD framework capable of accommodating different evolution policies and assess their performance (i.e., the decoding accuracy achieved by the discovered heuristic) and computational complexity (i.e., the number of evaluated candidate heuristics) in the target 5G NR task. We experiment with two evolution policies: LLM- Based Evolution (LBE), which performs population-based par- allel mutation and selection, and Prompt-guided LLM-Based Evolution (PLBE), which augments evolution with structured prompt operators. Under a fixed time budget per experiment, we find that AHD prompted by specifying the context of the task (i.e., LDPC decoding) consistently converges toward state- of-the-art performance and is robust to the specific evaluation approach employed. Instead, context-agnostic prompting and/or exploratory parent sampling tend to stagnate at substantially lower scores. The best discovered CNU heuristic is structurally close to functions employed in production 5G networks and marginally outperforms such functions on the specific Transport Block (TB) batch used for AHD. However, the additional gain disappears under independently drawn TBs. Ultimately, our study highlights the promise of AHD for 5G tasks but also the need for careful validation when interpreting in-loop gains.

2026-06-15T00:00:00Z