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arXiv Machine Learning

Adaptive Deadline- and Energy-Aware Caching Algorithms in Dynamic Multi-UAV Edge Networks

The paper proposes two caching strategies for multi‑UAV edge networks that jointly consider content deadlines, UAV communication energy, and energy imbalance. The first, DE‑UBAC, uses a rule‑based hierarchical approach with deadline prioritization and cooperative placement, but its fixed thresholds limit adaptability. The second, GRU‑DEPPO, integrates GRU‑based temporal representations with a centralized PPO actor‑critic, achieving a 61% lower deadline‑miss rate and 40% reduced energy imbalance compared to DE‑UBAC.

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arXiv AI
Aug 26

Place, Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNB

The paper proposes a hierarchical Open Radio Access Network (O‑RAN) control framework for tethered millimeter‑wave UAV‑mounted 5G base stations (gNBs). A Non‑Real‑Time RIC application jointly manages UAV placement and slice budgets, while a Near‑Real‑Time RIC application allocates per‑user resources using a permutation‑equivariant DeepSets Soft Actor‑Critic scheduler. This two‑level controller improves eMBB service‑level agreement satisfaction by up to 17 % and URLLC on‑time delivery by up to 42 % compared with conventional schedulers.

By Alireza Mohammadhosseini, Fatemeh Afghah
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arXiv AI
Jul 21

Lyapunov Stability-Aware Stackelberg Game for Low-Altitude Economy: A Control-Oriented Pruning-Based DRL Approach

arXiv:2602. 01131v2 Announce Type: replace Abstract: With the rapid expansion of the low-altitude economy, Unmanned Aerial Vehicles (UAVs) serve as pivotal aerial base stations supporting diverse services from users, ranging from latency-sensitive critical missions to bandwidth-intensive data streaming.

By Yue Zhong, Jiawen Kang, Yongju Tong, Hong-Ning Dai, Dong In Kim, Abbas Jamalipour, Shengli Xie
reinforcement-learningefficiency
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arXiv AI
Oct 16

PriorityFEC: Deadline-Aware Streaming FEC for KV-Cache Handoff in Edge Multi-Agent Systems

PriorityFEC is a deadline‑aware streaming forward erasure correction scheme designed to hand off key–value (KV) cache data between agents in edge multi‑agent large language model systems. It prioritizes high‑utility KV blocks in the transmission stream and adapts repair packet scheduling based on receiver feedback to meet strict time‑to‑first‑token deadlines. Experiments with Qwen2.5‑7B‑Instruct and Llama‑3.1‑8B‑Instruct show that PriorityFEC improves task quality compared to other transport schemes, especially under tight deadline constraints.

By Han Shen, Qifu Tyler Sun, Fanyang Meng, Ye Wang, Yongsheng Liang
llmsagentsefficiency
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arXiv AI
Jul 7

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

arXiv:2607. 04290v1 Announce Type: cross Abstract: 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.

By Gerasimos Papanikolaou-Ntais, Alexandros Kaloxylos, Athanasios Kanavos
llmsagentsefficiencysafety
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Hugging Face Trending Papers
Jul 5

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

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 Agentic-V2X, an architecture where a small, locally deployed language model acts as a periodic non-real-time rApp-inspired policy creator, while a lightweight xApp-like controller executes validated policies at intervals suitable for scheduling.

llmsagentsefficiencysafety
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arXiv AI
Sep 28

Threat-Aware Energy-Efficient Deployment for Dynamic UAV Networks: A Multi-Agent RL Approach

The paper introduces a three‑step framework for deploying multi‑UAV networks in threat‑prone environments. First, a threat‑aware K‑means algorithm determines the minimal number of UAVs and safe initial positions. Second, an optimal matching stage assigns UAVs to these positions to reduce energy use. Third, a threat‑aware multi‑agent twin delayed deep deterministic policy gradient (MATD3) algorithm dynamically optimizes UAV trajectories, power, and user associations, achieving zero safety violations and superior energy efficiency compared to other learning methods and heuristic baselines.

By Faisal Al-Kamali, Hussein A. Ammar, Francois Chan, James H. Bayes, Yasser Gadallah, Mohamed H. Ahmed
agentsreinforcement-learningsafety
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arXiv Machine Learning
Jul 22

Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks

arXiv:2607. 18288v1 Announce Type: new Abstract: Load imbalance across edge and cloud layers degrades latency performance in hierarchical edge-cloud computing (HECC) systems under dynamic task arrivals and heterogeneous resources, leading to severe queuing delays and inefficient resource utilization.

By Vo Phi Son, Van-Dinh Nguyen, Ngoc Hung Nguyen, Trinh Van Chien, Symeon Chatzinotas
reinforcement-learning
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