CORTEXA
← Browse
arxivcs.NI2026-07-20

Cost-Aware Uplink MPQUIC Scheduling via Multi-Objective Bayesian Optimization

Thanh Trung Nguyen, Thanh Le, Phi Le Nguyen, Kien Nguyen

Multipath QUIC (MPQUIC) enables simultaneous uplink transmission over heterogeneous access networks such as Wi-Fi and LTE, improving reliability and performance. However, aggressive LTE utilization increases operational cost, creating an inherent trade-off between upload delay and cellular usage. Existing MPQUIC schedulers typically optimize a single performance objective and operate at fixed points within this trade-off space, without explicitly supporting cost-aware operation. This paper formulates uplink MPQUIC scheduling as a multi-objective optimization problem that jointly considers maximum upload completion time and total LTE usage. We propose a Bayesian Optimization-based framework that treats the MPQUIC system as a black box and systematically explores probabilistic path selection configurations to uncover Pareto-efficient operating points. Rather than committing to a predefined scheduling policy, the framework exposes a spectrum of delay--cost trade-offs without modifying protocol internals. Experiments conducted using the Mininet-WiFi emulator show that the proposed approach characterizes a wide delay--cost region and identifies configurations that achieve substantial LTE savings (up to 80%) with controlled increases in upload time. The results further indicate that, under higher contention levels, systematic multi-objective exploration provides increased flexibility compared to fixed-policy schedulers in cost-aware heterogeneous uplink deployments.

View free PDFSource page

Related papers

arxivcs.NIeess.SP2026-07-01

Robust Base Station Placement in Agricultural IoT via Bayesian Optimization

Gourav Prateek Sharma, Durgesh Singh, James Gross

Precision-agriculture networks based on private 5G NR should ensure reliable connectivity for IoT sensor nodes throughout the crop growing season, yet the propagation environment changes dramatically as vegetation grows and matures. We formulate $K$-base-station~(BS) placement as…

View free PDFSource page
arxivcs.NI2026-07-19

DAN-Scheduler: Deterministic Three-Stage Co-Optimization of Scheduling, Memory Layout, and Pipeline Overlap for General-Purpose NPUs

Runhao Liu, Peng Zheng

Neural Processing Units (NPUs) are increasingly deployed for high-throughput, memory-constrained inference, yet their hierarchical on-chip memories and heterogeneous compute and data-movement engines tightly couple execution order, memory placement, and pipeline overlap. Existing…

View free PDFSource page
arxivcs.NIcs.AI2026-07-05

Agentic-V2X: Small Language Model Agents for Deadline-Aware V2X Scheduling in 5G/6G Networks

Gerasimos Papanikolaou-Ntais, Alexandros Kaloxylos, Athanasios Kanavos

Large Language Models (LLMs) are proposed as control interfaces for next-generation networks, but their latency, hallucinations, and lack of control guarantees make them unsuitable for near-real-time packet schedulers, especially in dynamic V2X environments. This paper introduces…

View free PDFSource page
arxivcs.NIcs.CV2026-06-30

Exploiting Overlapping Fields of View for Redundancy-Aware Uplink Transmission in Vehicular 6G

Hamidreza Mazandarani, Masoud Shokrnezhad, Tarik Taleb, Onur Günlü

Emerging uplink-dominant 6G use cases, such as cooperative vehicular streaming, require efficient transmission of high-volume visual data over limited wireless resources. While semantic communications can reduce traffic by prioritizing task-relevant content, most existing approac…

View free PDFSource page
arxivcs.NI2026-06-29

CALO: Constraint-Aware Learning Optimization for Joint Resource Allocation in Double-Active RIS-Assisted Wireless Networks

Alaa S. Arabiyat, Mohammad J. Abdel-Rahman

Double-active reconfigurable intelligent surface (RIS)-assisted wireless systems can improve coverage and achievable rate in blockage-dominated environments. Still, their joint resource allocation is challenging due to the coupling among RIS placement, amplification power allocat…

View free PDFSource page
arxivcs.AIcs.LGcs.NI2026-07-15

AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks

Giambattista Amati, Federica Mangiatordi, Simone Angelini, Emiliano Pallotti, Pierpaolo Salvo

Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments. However, limited Road-Side Unit (RSU) density, frequent non-line-of-sight conditions, and highly dynamic vehicular topologies often prevent many Connected and Automated Ve…

View free PDFSource page