Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.08618

Demo: Closed-Loop Sionna-Isaac Sim Co-Simulation Framework for Wireless-Aware Robot Navigation over ROS 2

Yi Shen Lim·Seungeun Oh·Jihong Park
View original source ↗Read source document ↗Export metadata ↓

Abstract

A robot that offloads its control loop to the network carries the receiver with it, so link quality is decided by where it goes. Simulating this requires both a physics engine and a site-specific propagation model at once; to our knowledge no simulator natively unifies both, with existing couplings of the two limited to offline analyses. We demonstrate a real-time co-simulation framework coupling NVIDIA Isaac Sim and NVIDIA Sionna over ROS 2 that closes the perception-action-communication (PAC) loop between them. Sionna ray-traces the base-station-to-robot channel over the exact geometry Isaac Sim simulates on, rather than modeling it stochastically, and feeds channel states back into the control loop in real time. Ray-traced on GPU, the coverage map is refreshed in ~16ms (~60 Hz), fast enough for real-time control. To showcase the framework's utility, we implement a wireless-aware navigation application in an OpenStreetMap(OSM)-derived SUTD campus twin with two Nova Carter robots: the closed-loop planner eliminates communication outage at only +7.4% traversal time over the shortest-path baseline (which spends 7.9 s of its 81.2 s run disconnected).

Explore related subjects

cs.NIcs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yi Shen Lim, Seungeun Oh, Jihong Park. 2026-10-06. Demo: Closed-Loop Sionna-Isaac Sim Co-Simulation Framework for Wireless-Aware Robot Navigation over ROS 2. https://arxiv.org/abs/2610.08618

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

IPEK: Event-Criticality-Aware Evidential Trust Management against Strategic Insider Attacks in Vehicular Ad Hoc Networks

Event-based trust management in vehicular ad hoc networks is vulnerable to strategic insider attackers who report trivial events correctly to build reputation, falsify reports on safety-critical events, and corrupt the trust feedback they provide about other vehicles. This paper proposes IPEK, an event-criticality-aware evidential trust management framework. IPEK combines an asymmetric local trust that scales rewards and penalties with event severity and location criticality, a reporter-credibility test that discounts feedback deviating systematically from the consensus of witnesses, and a Dempster-Shafer global trust that fuses feedback with Yager's rule, historical combination and risk accentuation. Closed-form properties quantify how quickly trust is lost and regained and show why Yager's rule keeps the global state revisable. IPEK is evaluated in simulation against a trust cascading-based dissemination scheme and the beta reputation system under eight attacks, with severity-, location-, on-off- and continuously triggered attackers falsifying their reports alone or also their feedback, at attacker ratios from 10% to 40%. Threshold-free separation measures are complemented by closed-loop revocation with frozen thresholds. With up to 25% attackers, IPEK achieves a significantly higher F1-score than both baselines under all attacks, with false positive rates of 0.9-15.5% against 7.8-49.7% and 26.0-41.4%, and it remains robust up to 40% attackers when only reports are falsified. Under fully colluding attackers that also corrupt feedback, all methods break down beyond about one third of attackers; this envelope and a longer detection delay are reported as limitations.

cs.NI↗
arXiv2026

SparKV: Overhead-Aware KV Cache Loading for Efficient On-Device LLM Inference

Efficient inference for on-device Large Language Models (LLMs) remains challenging due to limited hardware resources and the high cost of the prefill stage, which processes the full input context to construct Key-Value (KV) caches. We present SparKV, an adaptive KV loading framework that combines cloud-based KV streaming with on-device computation. SparKV models the cost of individual KV chunks and decides whether each chunk should be streamed or computed locally, while overlapping the two execution paths to reduce latency. To handle fluctuations in wireless connectivity and edge resource availability, SparKV further refines offline-generated schedules at runtime to rebalance communication and computation costs. Experiments across diverse datasets, LLMs, and edge devices show that SparKV reduces Time-to-First-Token by 1.3$x-5.1x with negligible impact on response quality, while lowering per-request energy consumption by 1.5x to 3.3x, demonstrating its robustness and practicality for real-world on-device deployment.

cs.NI↗
arXiv2026

Hardware-Software Co-Design for Energy-Efficient Neural Recording in Wireless Implantable BCIs

Implantable Brain-Computer Interfaces (iBCIs) are increasingly pivotal in clinical and daily applications. However, wireless iBCIs face severe constraints in power consumption and data throughput. To mitigate these bottlenecks, we propose a wireless iBCI headstage featuring adaptive ADC sampling and spike detection. Distinguishing our design from traditional application-layer compression, we employ a server-driven architecture that achieves source-level efficiency. Specifically, the server learns an optimal, electrode-specific sample rate vector to dynamically reconfigure the ADC hardware. This strategy reduces data volume directly at the acquisition layer (ADC and amplifier) rather than relying on computationally intensive post-digitization processing. Extensive experiments across diverse subjects and arrays demonstrate a power reduction of up to 40 mW and a 3.2x decrease in FPGA resource utilization, all while maintaining or exceeding decoding accuracy in both motor and visual tasks. This design offers a highly practical solution for long-term in-vivo recording.

cs.NI↗