arXiv subjects

João Paulo Esper

Publications and source records attributed to João Paulo Esper.

4 recordsLinked to original sources

arXiv2026

Enabling Real-Time Programmability for RAN Functions: A Wasm-Based Approach for Isolated and Deterministic dApps

The Open Radio Access Network Alliance (O-RAN) architecture enables third-party applications to optimize Radio Access Networks (RANs) at multiple timescales. However, the real-time distributed applications (known as dApps) that demand low latency, high performance, and resource isolation remain underexplored. Existing approaches propose collocating a new RAN Intelligent Controller (RIC) at the edge, or deploying dApps in bare metal along with RAN functions. The former approach increases network complexity and requires additional edge computing resources, and the latter raises serious security concerns due to the lack of native mechanisms to isolate dApps and RAN functions. Meanwhile, WebAssembly (Wasm) has emerged as a fast, lightweight technology for robust execution of external, untrusted code. In this work, we propose a new approach to executing dApps using Wasm to isolate applications in real-time in O-RAN. Results from a proof-of-concept implementation show that our lightweight and robust approach can provide predictable, deterministic performance, resource isolation, and low latency, supporting real-time control loops.

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arXiv2026

Toward Scalable VR-Cloud Gaming: An Attention-aware Adaptive Resource Allocation Framework for 6G Networks

Virtual Reality Cloud Gaming (VR-CG) represents a demanding class of immersive applications, requiring high bandwidth, ultra-low latency, and intelligent resource management to ensure optimal user experience. In this paper, we propose a scalable and QoE-aware multi-stage optimization framework for resource allocation in VR-CG over 6G networks. Our solution decomposes the joint resource allocation problem into three interdependent stages: (i) user association and communication resource allocation; (ii) VR-CG game engine placement with adaptive multipath routing; and (iii) attention-aware scheduling and wireless resource allocation based on motion-to-photon latency. For each stage, we design specialized heuristic algorithms that achieve near-optimal performance while significantly reducing computational time. We introduce a novel user-centric QoE model based on visual attention to virtual objects, guiding adaptive resolution and frame rate selection. A dataset-driven evaluation demonstrates that, when compared against state-of-the-art approaches, our framework improves QoE by up to 50\%, reduces communication resource usage by 75\%, and achieves up to 35\% cost savings, while maintaining an average optimality gap of 5\%. Our proposed heuristics solve large-scale scenarios in under 0.1 seconds, highlighting their potential for real-time deployment in next-generation mobile networks.

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arXiv2025

+Tour: Recommending personalized itineraries for smart tourism

Next-generation touristic services will rely on the advanced mobile networks' high bandwidth and low latency and the Multi-access Edge Computing (MEC) paradigm to provide fully immersive mobile experiences. As an integral part of travel planning systems, recommendation algorithms devise personalized tour itineraries for individual users considering the popularity of a city's Points of Interest (POIs) as well as the tourist preferences and constraints. However, in the context of next-generation touristic services, recommendation algorithms should also consider the applications (e.g., social network, mobile video streaming, mobile augmented reality) the tourist will consume in the POIs and the quality in which the MEC infrastructure will deliver such applications. In this paper, we address the joint problem of recommending personalized tour itineraries for tourists and efficiently allocating MEC resources for advanced touristic applications. We formulate an optimization problem that maximizes the itinerary of individual tourists while optimizing the resource allocation at the network edge. We then propose an exact algorithm that quickly solves the problem optimally, considering instances of realistic size. Using a real-world location-based photo-sharing database, we conduct and present an exploratory analysis to understand preferences and users' visiting patterns. Using this understanding, we propose a methodology to identify user interest in applications. Finally, we evaluate our algorithm using this dataset. Results show that our algorithm outperforms a modified version of a state-of-the-art solution for personalized tour itinerary recommendation, demonstrating gains up to 11% for resource allocation efficiency and 40% for user experience. In addition, our algorithm performs similarly to the modified state-of-the-art solution regarding traditional itinerary recommendation metrics.

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arXiv2023

Impact of User Privacy and Mobility on Edge Offloading

Offloading high-demanding applications to the edge provides better quality of experience (QoE) for users with limited hardware devices. However, to maintain a competitive QoE, infrastructure, and service providers must adapt to users' different mobility patterns, which can be challenging, especially for location-based services (LBS). Another issue that needs to be tackled is the increasing demand for user privacy protection. With less (accurate) information regarding user location, preferences, and usage patterns, forecasting the performance of offloading mechanisms becomes even more challenging. This work discusses the impacts of users' privacy and mobility when offloading to the edge. Different privacy and mobility scenarios are simulated and discussed to shed light on the trade-offs (e.g., privacy protection at the cost of increased latency) among privacy protection, mobility, and offloading performance.

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