CityDeploy-Bench is a new benchmark that frames multi‑transmitter wireless network deployment as a physics‑grounded spatial set planning problem, verified by a unified ray‑tracing engine. It decouples utility representation from planning dynamics, allowing systematic comparison of direct scalar rewards, relational models, and higher‑order interaction structures across different planners. Experiments show that as physical coupling increases, deployment quality hinges on capturing collective transmitter interactions; stronger search alone cannot compensate for missing relational structure, highlighting the coordination nature of the problem.
arXiv:2608. 14599v1 Announce Type: cross Abstract: The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks.
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.
arXiv:2609.09578v1 Announce Type: cross Abstract: Urban planning is a real-world spatial optimization problem that requires selecting feasible actions from large candidate spaces under practical obje...
The paper presents a geometry-driven framework for beam management in high-frequency massive MIMO systems. It constructs an offline virtual base station database using 3D LiDAR point clouds and location data to model dominant reflection paths, enabling a coarse channel reconstruction. A VBS-assisted orthogonal-pilot scheme and a dual-agent dueling double deep Q-network are then employed to refine beam estimates and perform coordinated beam selection, yielding improved training efficiency and performance over existing baselines.
arXiv:2608.28627v1 Announce Type: new Abstract: Designing high-performance tactical wireless networks under realistic operational constraints gives rise to challenging combinatorial optimization prob...
MAPF-World introduces an autoregressive action world model for multi‑agent path finding that predicts short‑horizon local observations and neighboring agents’ action intentions, enabling decisions beyond immediate local views. The model incorporates a spatio‑agent positional encoding within Transformer architectures to enhance spatial awareness and agent semantics, fostering coordinated behaviors. Experiments on diverse maps, including a new automated map generator based on real‑world urban layouts, show that MAPF‑World outperforms existing learned solvers, achieving strong performance, robust zero‑shot generalization, and high success rates even at high agent densities.
arXiv:2603. 23800v2 Announce Type: replace-cross Abstract: We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known environments.
The paper introduces Hybrid Belief Reinforcement Learning (HBRL), a two‑phase framework for coordinating multiple autonomous agents in spatial exploration tasks. In phase one, agents build spatial beliefs via a Log‑Gaussian Cox Process and plan information‑driven trajectories using a Pathwise Mutual Information planner; in phase two, a Soft Actor‑Critic policy is warm‑started through dual‑channel knowledge transfer that incorporates belief states and replay‑buffer demonstrations. Experiments on UAV wireless service provisioning and NYC taxi demand sensing show significant performance gains, with the dual‑channel transfer outperforming single‑channel approaches.
arXiv:2604. 22056v3 Announce Type: replace Abstract: Optimal wireless transmitter placement is a central task in radio-network planning, and exhaustive search becomes prohibitively expensive at scale.
The paper introduces WONDER, a radio world‑model‑based negotiation framework designed to optimize multi‑UAV coverage for rapid post‑disaster wireless restoration. WONDER employs a Joint‑Embedding Predictive Architecture to forecast the incremental radio impact of candidate UAV trajectories and uses multi‑round negotiation to sequentially commit trajectories while updating the context. Experiments in the RadioDynamics simulation environment demonstrate that WONDER outperforms six other methods, achieving a balanced score of 0.870 and a 0.162 coverage advantage over STACCA while preserving full UAV connectivity.
arXiv:2603. 18871v2 Announce Type: replace Abstract: Urban Vehicular Ad-Hoc Networks (VANETs) can become fragmented because buildings obstruct wireless links and vehicle mobility continuously changes the network topology.
arXiv:2609.37098v1 Announce Type: cross Abstract: Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providin...