Age of Information Optimization

Últimos artículos

• Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN)

  Yahao Ding, Jiaxiang Wang, Zhouxiang Zhao, Zhaohui Yang, Mingzhe Chen, Mohammad Shikh-Bahaei · 5 de octubre de 2026

  Mobile embodied AI networks (MEAN) enable embodied agents to perceive, reason, communicate, and act in wireless environments. In such networks, agent mobility can improve channel conditions, while semantic compression can reduce transmission payloads. However, movement consumes energy, and stronger …

• Asynchronous LLM Post-Training: Group-Mass Capping and Convergence Analysis

  Qijia He, Ruinan Jin, Jun Luo, Shaofeng Zou, Yingbin Liang · 2 de octubre de 2026

  Asynchronous reinforcement learning (RL) improves the efficiency of large language model post-training but introduces stale rollouts generated by earlier policies. Theoretical understanding of how this staleness affects convergence and how to mitigate its impact remains limited. We derive a converge…

• PROSE: A Theory of Optimal Stopping with Perishable Evidence for Peer Selection in Intermittently Connected Decentralised Learning

  Christos Anagnostopoulos · 22 de septiembre de 2026

  Decentralised federated learning removes the aggregation server but makes collaboration dependent on transient peer availability. In mobile and intermittently connected systems, evaluating a promising peer consumes contact time and may cause the exchange opportunity itself to vanish, so that the evi…

• Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning

  Yuxuan Sun, Yuxuan Bai, Tan Chen, Sheng Zhou, Zhisheng Niu · 16 de septiembre de 2026

  Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it important for Internet of Things applications such as intelligent transportation, industrial monitoring, and unmanned systems. Under spatio-temporal data di…

• World Models Under Asynchronous Sensor Observations

  Akash Anand, Abhay Anand, Yash Vishe · 9 de septiembre de 2026

  Learned world models typically assume that observations arrive synchronously, an abstraction inherited from simulators that return a complete state vector at each environment step. Physical sensing instead operates at heterogeneous rates, leaving most observation channels stale at any given instant.…

• SafeStep: An Interactive Demonstration of Semantic Communication for Pedestrian Safety Monitoring

  Christian McDowell, Andrea Panebianco, Jeremiah Yang, Sirin Chakraborty, Samuel Chamoun, Travis Ross, Yin Sun · 31 de agosto de 2026

  In this paper, we develop SafeStep, an interactive browser-based semantic communication platform for live pedestrian safety monitoring. SafeStep extracts pedestrian information from four live traffic-camera feeds, transmits it through a semantic communication transceiver over an Additive White Gauss…

• Resilient Decentralized Wireless Federated Learning via Gradient Tracking with AdamW

  Nguyen Van Thieu, Ti Ti Nguyen, Ons Aouedi, Vu Nguyen Ha, Symeon Chatzinotas · 27 de agosto de 2026

  Wireless Internet-of-Things (IoT) edge networks require decentralized learning (DecL) methods that can operate reliably under both heterogeneous local data and communication-constrained wireless links. However, existing decentralized optimization schemes often incur substantial communication overhea…

• FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selection

  Nguyen Van Thieu, Ti Ti Nguyen, Ons Aouedi, Zerihun Huruy, Vu Nguyen Ha, Symeon Chatzinotas · 27 de agosto de 2026

  Reliable access selection in dynamic and heterogeneous indoor-outdoor environments is challenging because instantaneous radio measurements alone cannot capture future QoS degradation caused by mobility, blockage, traffic load, and resource competition. This paper proposes FedQoS, a federated QoS-ris…

• Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation

  Adam Fisch, Shubhendu Trivedi, Fantine Huot, William W. Cohen, Michael Kaisers, Mirella Lapata, Kate Larson, Jacob Eisenstein · 25 de agosto de 2026

  Heterogeneous AI systems composed of multiple models, architectures, harnesses, or inference-time settings can improve quality and efficiency by routing queries to the specialist who can answer most effectively at the lowest cost. Routing requires estimating each specialist's expected return, but th…

• A Lyapunov Drift-Plus-Penalty Method Tailored for Reinforcement Learning with Queue Stability

  Wenhan Xu, Jiashuo Jiang, Lei Deng, Danny Hin-Kwok Tsang · 14 de agosto de 2026

  With the proliferation of Internet of Things (IoT) devices, the demand for addressing complex optimization challenges has intensified. The Lyapunov Drift-Plus-Penalty algorithm is a widely adopted approach for ensuring queue stability, and some research has preliminarily explored its integration wit…

• Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning

  Wentao Zhang, Wentao Mo · 4 de agosto de 2026

  Safety-critical IoT systems such as industrial closed-loop control, V2X coordination, and remote teleoperation require every sensor's peak Age of Information (peak AoI, also abbreviated PAoI) to stay below a hard per-slot deadline, not merely an average bound. Existing approaches meet this requireme…

• Partially-Observable Transmission Control for UAV-Enabled Federated Learning in IoT Networks

  Masoud Ghazikor, Zhou Ni, Morteza Hashemi · 4 de agosto de 2026

  Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable. In this paper, we develop a packet-level transmi…

• Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints

  Yeonseo Jeong, Wonhyeok Ko, Sungweon Hong, Songnam Hong · 4 de agosto de 2026

  Maximizing throughput under proportional fairness in dense wireless networks requires jointly managing user association, scheduling, base station (BS) activation, and handover control under hard finite-horizon energy and handover budgets, which induces a fundamental tension between BS-side energy ma…

• TALSC: Timeliness-Aware Large-Small VLM Collaboration for Infrastructure-Assisted Autonomous Driving

  Mengmeng Zhu, Yuxuan Sun, Wei Chen, Bo Ai · 4 de agosto de 2026

  The deployment of Vision-Language Models (VLMs) in autonomous driving (AD) systems is constrained by on-board computing power, restricting vehicles to small VLMs (SVLMs) with limited perception and reasoning capabilities. Infrastructure-assisted AD alleviates this resource constraint by enabling col…

• Learning When to Automate: Queue Control in Human-AI Service Systems

  Giovanni Montanari, Marco Scarsini, Vianney Perchet · 8 de julio de 2026

  We study a human-AI service system in which tasks arrive sequentially and are processed through a two-stage architecture: an automated chatbot followed, when necessary, by a human agent. We consider $T$ sequentially arriving tasks, each belonging to one of $K$ heterogeneous types. For each task the …

• Contrastive Predictive Coding with Compression for Enhanced Channel State Feedback in Wireless Networks

  Ahmed Y. Radwan, Hina Tabassum, Fahad Syed Muhammad, Matthew Baker · 8 de julio de 2026

  Accurate and timely channel state information (CSI) is essential for next-generation wireless systems, yet existing works treat CSI compression and CSI prediction as separate problems, both in academia and in current 3GPP studies. Consequently, channel aging remains insufficiently addressed within s…

• Adaptive Inference Batching using Policy Gradients

  Ruslan Sharifullin · 7 de julio de 2026

  Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic. We investigate whether reinforcement learning (RL) can learn adaptive batch…

• Not Every Sync Is Safe: Calibrated DiLoCo Scheduling for Shared AI Infrastructure

  Maxwell Twelftree, David Lemphers, An-chi He, Yue Yang · 7 de julio de 2026

  DiLoCo-style training reduces communication by letting learner islands train locally before occasional outer synchronization, making it attractive for fragmented industrial AI fleets where training shares hardware with latency-sensitive serving. The question for such fleets is when an outer merge is…

• Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception

  Hongbo Wang · 3 de julio de 2026

  Certified world models estimate how long their predictions remain valid. We turn this validity horizon into an operational sensing clock: a rule for when an agent should stop coasting and re-sense. Starting from an audited equivariant world model, we derive a deadline for no-sensing intervals and sh…

• Minimizing Quantized Semantic Age of Information (QSAoI) in Foundation Model-Based Semantic Communications

  Huanyu Zhang, Yulin Hu, Xiaopeng Yuan, Aydin Sezgin, Anke Schmeink · 1 de julio de 2026

  The emerging techniques of semantic communications and edge computing in 6G networks necessitate a paradigm shift toward co-designed semantic-aware and adaptive resource allocation for short-packet transmissions. However, there is a fundamental gap between the semantic layer and the physical layer u…

• Retroactive Advantage Correction: Closed-Form V-Trace Bias Correction for Delay-Aware RLHF

  Arnav Raj · 29 de junio de 2026

  Reinforcement learning from human feedback (RLHF) in production does not always have a synchronous reward signal. Code-execution verifiers, slow judge ensembles, and queued human review can return several gradient steps after the rollout that produced them, breaking the synchronous-reward assumption…

• Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation

  Chanuka A. S. Hewa Kaluannakkage, Rajkumar Buyya · 17 de junio de 2026

  Decentralized Federated Learning(DFL) enables collaborative model training across wireless edge nodes, including IoT deployments, autonomous vehicles, UAV swarms, and satellite constellations. Operating over lossy wireless links under constraints, these systems cannot rely on retransmissions, so mod…

• Revisiting Outage for Edge Inference Systems

  Zhanwei Wang, Qunsong Zeng, Haotian Zheng, Kaibin Huang · 15 de junio de 2026

  One of the key missions of sixth-generation (6G) mobile networks is to deploy large-scale artificial intelligence (AI) models at the network edge to provide remote-inference services for edge devices. The resultant platform, known as edge inference, will support a wide range of Internet-of-Things ap…

• Inverse Probability Weighting and Age-of-Information Aggregation for Decentralized Federated Learning under Partial Reception

  Chanuka A. S. Hewa Kaluannakkage, Rajkumar Buyya · 10 de junio de 2026

  Decentralized Federated Learning (DFL) over lossy wireless networks faces two key challenges: selection bias, where updates from poor-quality links are systematically underrepresented due to partial model reception, and update staleness, where asynchronous nodes contribute outdated information. We s…

• Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets

  Yi Chen (Lucy), Rushuai Yang (Lucy), Qiang Chen (Lucy), Dongyan (Lucy), Huo · 10 de junio de 2026

  Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operational constraints. These features make it difficult to use standard deep reinforcement learning (DRL) algorithms, whose action interfaces typically assu…