The advent of sixth-generation (6G) mobile networks forecasts the widespread deployment of edge artificial intelligence (AI), where AI inference tasks are offloaded from resource-constrained edge devices to edge servers. This paradigm promises low latency and high-efficiency processing, yet faces critical challenges in meeting the stringent latency requirements of emerging applications such as autonomous driving and real-time robotics. Traditional ultra-reliable and low-latency communication (URLLC) paradigms fall short in the context of edge inference, where the high-dimensional nature of extracted features introduces a fundamental trade-off between reliability and latency. In this paper, we exploit the inherent robustness of AI models to channel distortions to design an ultra-low-latency edge inference framework that jointly optimizes computation and communication resources. We focus on both multi-snapshot (sequential sensing) and multi-view (distributed sensing) scenarios. For each, we derive upper bounds on inference accuracy as a function of the number of snapshots/views and their average bit error rate (BER). These bounds guide the formulation of optimization problems that minimize total system latency while satisfying accuracy constraints. The joint optimization is decomposed into subproblems involving snapshot/view selection, transmit power control, and allocation of computation and communication time. To solve this efficiently, we develop a low-complexity iterative algorithm. Experimental results on synthetic and real-world datasets validate our approach, demonstrating significant latency reductions while maintaining inference performance. Our findings provide a foundation for robust and efficient edge AI design in future 6G networks.
Zhi-Feng Wang, Qunsong Zeng, Ren-Zhi Yuan et al.· IEEE Transactions on Wireles...· 0 citations
Efficient exploration and target identification in unstructured environments are critical challenges in UAV automation. While foundation models like Segment Anything Model 3 (SAM 3) offer powerful open-vocabulary perception, their high computational cost and inference latency hinder real-time deployment on onboard hardware. Traditional geometric methods, conversely, ensure safety but lack semantic awareness. This paper presents the Semantic-Foundation UAV Exploration with Latency-awareness (S-FUEL) framework, which bridges this gap via an asynchronous semantic-geometric fusion strategy. We formulate the exploration task as a dual-layer optimization problem. First, to handle the low-frequency semantic updates, we introduce a Latency-Aware Semantic Utility Function, which asynchronously updates global goals based on SAM 3’s open-vocabulary detections without blocking the high-frequency local planner. Second, to ensure safety against dynamic obstacles despite sparse detections, we propose a Predictive Semantic Repulsion Field. This module leverages SAM 3’s video tracking memory to forecast obstacle trajectories and incorporate spatiotemporal priors into the local map, enabling the UAV to proactively avoid dynamic threats between semantic frames. Experimental results demonstrate that S-FUEL reduces “time-to-target” by about 42% compared with the FUEL geometric baseline and remains 10.6% faster than the strongest adapted semantic baseline. At the evaluated 50 Hz/2 Hz planning–semantic rate pair, the bounded predictor yields a 2% nominal collision rate; the rate rises to at most 4% under the tested weight perturbations, so the method reduces rather than eliminates dynamic-obstacle risk. Note to Practitioners—This study addresses a pressing practical challenge in autonomous UAV operations: enabling real-time, target-oriented exploration using computationally heavy foundation models on resource-constrained onboard hardware. In real-world search and rescue or industrial inspection missions, a fundamental mismatch exists between the high-speed requirements of flight control and the significant latency of advanced vision models like SAM 3. This latency often creates “blind intervals” where the UAV is unaware of obstacle movements between semantic updates. To overcome this, we propose the S-FUEL framework, which decouples semantic reasoning from motion planning through an asynchronous dual-thread architecture. Our method allows the UAV to utilize high-level semantic guidance for efficient searching without compromising local safety or control stability. The proposed strategy is particularly applicable to autonomous systems operating in dynamic, unstructured environments where rapid target identification and high-frequency collision avoidance are both essential.
Si-Tian Peng, Rui Wang· IEEE Transactions on Automat...· 0 citations
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