https://hdl.handle.net/20.500.12761/1 Wed, 15 Apr 2026 11:05:40 GMT 2026-04-15T11:05:40Z https://hdl.handle.net/20.500.12761/2025 Estimating Target Doppler in Unsynchronized Multistatic ISAC Deployments with Mobile Nodes Bhalli, Zaman; Rossi, Michele; Widmer, Joerg; Canil, Marco Integrated Sensing And Communication (ISAC) is recognized as a key enabler for future 6th Generation (6G) networks, combining communication capabilities with pervasive sensing. In such systems, the estimation of the Doppler shift plays a crucial role for target characterization. However, typical real-world ISAC scenarios largely involve bistatic or multistatic configurations and mobile ISAC nodes. Under these conditions, Doppler estimation becomes particularly challenging, as clock asynchrony between the Transmitter (TX) and the Receivers (RXs), combined with their mobility, introduces additional Doppler components and phase offsets that distortor disrupt the target-induced frequency shift. Existing works have considered these challenges separately or relied on external reference reflectors. In this paper, we present the first method to estimate the Doppler frequency of a target with mobile and asynchronous ISAC nodes in a multistatic configuration, considering the case of a mobile TX and multiple static RXs, and without leveraging any external reflector. By leveraging the invariance of the phase offsets across multipath components and exploiting geometrical relationships, we show that the problem is solvable if at least 4 RXs are present. We evaluate the proposed solution through numerical simulations in various scenarios, showing that it is a valid approach for estimating target Doppler shifts in unsynchronized multistatic ISAC deployments with mobile nodes. Mon, 01 Jun 2026 00:00:00 GMT https://hdl.handle.net/20.500.12761/2025 2026-06-01T00:00:00Z https://hdl.handle.net/20.500.12761/2024 SemanticDFL: Similarity-Aware Pull-based Personalized Decentralized Federated Learning Dogani, Javad; Khastkhodaei, Mostafa; Khunjush, Farshad; Laoutaris, Nikolaos Personalized decentralized federated learning (PDFL) seeks to tailor models to heterogeneous clients without a central coordinator, yet gossip-style mixing on large graphs dilutes minority signals and assumes any-to-any connectivity. We present SemanticDFL, a fully decentralized, pull-based personalization layer that organizes peers into a hierarchical semantic overlay network (SON). Each client publishes a compact top-P model signature; proximity-bounded discovery forms zones that are clustered using affinity propagation and stewarded by replica-backed super-peers that route bounded-fanout similarity queries. Clients then pull only the K most similar models for personalized aggregation, concentrating communication and computation where they matter most. We prove a lower bound that links spectral mixing and data heterogeneity to an irreducible mis-aggregation penalty for graph-oblivious, push-based overlays, thereby motivating the proposed similarity-aware pull method. A prototype and large-scale evaluation on FMNIST, Tiny ImageNet, Google Speech Commands, and 20 Newsgroups under Dirichlet and pathological splits (50--400 peers on the EU SLICES testbed) show that SemanticDFL improves final accuracy by 3--12% over strong decentralized personalized baselines, reaches target accuracy with 2.5x fewer rounds than FedAvg, and requires 1.3X fewer rounds than the best DPFL alternative. It adds only .7--12.6% per-round overhead across all settings while maintaining Recall@K 0.88--1.00, positioning similarity-aware pull over semantic overlays as a scalable path to high-quality personalization in decentralized FL. Mon, 01 Jun 2026 00:00:00 GMT https://hdl.handle.net/20.500.12761/2024 2026-06-01T00:00:00Z https://hdl.handle.net/20.500.12761/2023 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. Mon, 01 Jun 2026 00:00:00 GMT https://hdl.handle.net/20.500.12761/2023 2026-06-01T00:00:00Z https://hdl.handle.net/20.500.12761/2022 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. Fri, 01 May 2026 00:00:00 GMT https://hdl.handle.net/20.500.12761/2022 2026-05-01T00:00:00Z