arXiv:2605. 26418v2 Announce Type: replace-cross Abstract: A properly calibrated rule-based autoscaler can beat every one of six mainstream deep reinforcement learning (DRL) algorithms on cost across every workload we test - so when, if ever, does DRL actually help?
arXiv:2607. 13160v1 Announce Type: cross Abstract: Active beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) enables hybrid transmitting and reflecting mode to achieve effective signal amplification and full-space coverage, thus providing a promising solution for blockage-aware uplink offloading in heterogeneous mobile edge computing (MEC) systems.
arXiv:2606. 28339v1 Announce Type: cross Abstract: Industrial 6G networks require ultra-reliable, low-latency, and energy-efficient connectivity in dynamic and blockage-prone environments, where conventional terrestrial deployments often fail to ensure stable coverage.
arXiv:2609.15763v1 Announce Type: cross Abstract: Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it impor...
arXiv:2606. 05208v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has long been a powerful solution to various problems in communication networks.
arXiv:2605. 00457v4 Announce Type: replace-cross Abstract: The coexistence of NR-U and Wi-Fi in the unlicensed spectrum introduces a challenging resource management problem, where heterogeneous channel access mechanisms can lead to unbalanced spectrum utilization and severe Wi-Fi performance degradation.
arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.
arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.
This survey reviews over 100 studies on implementing reinforcement learning (RL) on field‑programmable gate arrays (FPGAs) and integrated circuits (ICs), covering a wide range of RL families from model‑based Markov decision processes to deep RL and multi‑agent methods. It organizes the literature by abstraction level, hardware realization strategy, and application domain, and evaluates key metrics such as resource utilization, latency, throughput, and power consumption. The paper identifies common architectural patterns, practical limitations, and open research challenges, offering a unified reference for researchers developing RL‑driven hardware systems.
The paper investigates UAV‑mounted reconfigurable intelligent surface (RIS) assisted device‑to‑device (D2D) communication with stochastic link activation. It models UAV motion, attitude, time‑varying Rician angles, and angle‑dependent RIS reflection, and formulates a joint optimization of UAV trajectory, attitude, and RIS phases to maximize average sum rate under mobility, energy, and hardware constraints. The authors employ deep reinforcement learning and a Decision Transformer trained on expert trajectories from multiple scenarios, showing that zero‑shot transfer outperforms direct DRL transfer and that online fine‑tuning achieves competitive performance with fewer interactions.
arXiv:2606. 18105v1 Announce Type: cross Abstract: Network planning optimization is a fundamental problem across diverse domains, including transportation systems, communication networks, and power grids.
arXiv:2606. 03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control.