Organizations: Zhejiang University, Hangzhou 310027, China · Zhejiang Lab, Hangzhou 310012, China · College of Communications and Engineering, Army Engineering University of PLA, Nanjing 210007, China · Macau University of Science and Technology, Macau, China
Post-disaster damage to terrestrial infrastructure can disrupt wireless coverage,while Uncrewed Aerial Vehicle (UAV) swarms provide a promising solution for rapid restoration.However, due to the limitations in local geometry observations hidden radio impact,and inter-UAV communication,there exists a significant gap between locally visible movement choices and swarm-level coverage outcomes.To combat this gap,we propose a raido World-model-based Optimized Negotiation framework for Distributed UAV covERage (WONDER).Particularly, to tackle the unavailability of the future radio field from onboard observations, WONDER uses a Joint-Embedding Predictive Architecture (JEPA)-based radio world model to learn and predict the incremental radio effect of each candidate trajectory from deployment-available information.Multi-round negotiation in WONDER then coordinates ranked proposals by committing one trajectory at a time and re-evaluating the remaining proposals under the updated context. Our theoretical analyses further validate the effectiveness of such a world model-based framework. WONDER also adopts a Proximal Policy Optimization (PPO)-style Actor and alternates between updating the world model and the actor. Furthermore,we build RadioDynamics,a comprehensive simulation environment that integrates UAV mobility,radio propagation, inter-UAV communication modeling,and digital-twin geometry with ray-traced fields in 62 metropolitan scenes.Experiments on 11 testing scenes in RadioDynamics show that WONDER achieves the highest balanced score among seven evaluated methods,reaching 0.870 with a 0.162 coverage advantage over STACCA, while maintaining 100% connectivity between UAVs.
TABLE I: A key summary of the key differences between WONDER and the literature.
Fig. 1: RadioDynamics setting for deployable UAV-swarm coverage. The upper flow constructs the radio environment from digital-twin geometry, UAV mobility, and ray-traced Reference Signal Received Power (RSRP) fields. The lower flow contrasts deployment-visible local geometry and constrained multi-hop communication with a full-information reference view. The marked asymmetries highlight the gaps between visible geometry and hidden radio impact, and between sparse local communication and swarm-level coverage objectives.
Symbol
Meaning
System and deployment information
N,N
UAV set and number of UAVs.
pi,t
Position of UAV i at deployment step t .
Ht,Nl
A2A communication graph and one subgraph.
Ninbr
One-hop neighbor set of UAV i .
oiloc
Deployment-visible local observation of UAV i .
TABLE II: Main notations used in the methodology.
Fig. 2: Visualization of flocking warm-up prefix, with Nwarm=15 . In each panel, blue markers denote UAV positions, orange segments denote feasible inter-UAV communication links, and the red circle in the upper left denotes the terminal backhaul range; UAVs outside this range depend on multi-hop UAV relaying for backhaul connectivity. dark-gray regions indicate building footprints, and the red – green heat map encodes RSRP strength.
Fig. 3: Loss curve and return curve of STACCA in RadioDynamics.
Fig. 4: Empirical evidence for counterfactual composition gap and sequential-plan gains in RadioDynamics. Both experiments are implemented across 51 train and validation scenes. Left: pairwise GCF(N;Tbase) measured from 3,726 active UAV pairs, comparing synchronized two-UAV counterfactual estimates with the impact of parallel execution. Right: the incremental balanced score of Trseq over T∥ increases with the negotiation round r ; the shaded region denotes the 95% confidence interval.
Fig. 5: Architecture of WONDER. The upper panel shows the deployment-time inference pipeline at a synchronization time step, including candidate trajectory generation, one-hop aggregation, subgraph reduction, multi-hop relay, global election, and trajectory commitment. The lower panel shows the three-stage training pipeline for the JEPA world model and election actor. Orange blocks denote modules used at deployment, and yellow blocks denote modules used only during training.
Fig. 6: Visualization of RadioDynamics closed-loop rollouts. Panels are recorded every five timesteps; the red time axis aligns the panels with rollout time, and the bottom curve reports the corresponding balanced score. The first to third rows show rollouts controlled by Flocking, STACCA, and WONDER, respectively.
Description
Notation
Value
Ground service area
╱
700×700 m 2
Ground grid
╱
350×350
Cell resolution
╱
2 m
A2G bandwidth
B
100 MHz [ 41 ]
A2A carrier frequency
fc
3.5 GHz [ 36 ]
A2A transmit power
PiA2A
23 dBm
TABLE III: Simulation and benchmark parameters.
Description
Notation
Value
Candidate proposal per reduction
Kc
3
Relay-depth bound
Kdep
2 hops
Negotiation-round limit
R
3
JEPA continuity constants
τcoh,εz
(0.25,10−8)
Connectivity penalty
λc
1.0
PPO constants
ϵclip,cv,ce
(0.2,0.5,0.01)
TABLE IV: Training parameters.
Fig. 7: Closed-loop performance under the shared RadioDynamics evaluation. Fair baselines are shown in blue , WONDER in red , and Exhausted references in gray . The balanced score summarizes the normalized metric vector.
Fig. 8: Representative cases where our method outperforms Sequential Exhausted. Sequential Exhausted visits UAVs in a fixed order and greedily commits each visited UAV’s locally best action. The gray actions yield marginal gain, while the yellow actions would produce higher global gain if committed earlier. Our method prioritizes the yellow actions through proposal-level negotiation, avoiding the local optimum induced by fixed-order greedy selection.
Entry
Scope
No. of Messages
Payload (kbit)
STACCA [ 26 ]
Local vectors
120
122.88
MAGI [ 28 ]
Neighborhood Message
120
245.76
WONDER
Local + proposal
264
270.336
Full Connection
Full Radio Map (raw)
120
470400
TABLE V: Payload upper bounds for message exchange.
Fig. 9: Training curves for JEPA and DR local proposal models.
Fig. 10: Offline recall of retained proposals for JEPA and DR.
Fig. 11: Training curve of the election policy.
Method
Coverage Area (%)
Mean Rate (Mbps)
5% Edge Rate (Mbps)
SINR Coverage (%)
Mean Interference (%)
Jain Fairness (%)
Connectivity (%)
Balanced Score
w/o JEPA Encoder
43.1
300.8
0.00357
38.5
0.98
29.8
66.5
0.659
w/o Election Module
44.5
322.2
0.00381
41.0
0.94
31.7
68.4
0.708
w/o Alternating update
49.4
353.2
0.00420
45.0
0.85
35.0
75.7
0.808
w/o Multi-Round Negotiation
48.3
343.3
0.00408
44.5
0.87
34.1
74.0
0.784
Our full method
52.2
362.5
0.00400
47.5
0.89
33.4
100.0
0.870
TABLE VI: Ablation analysis of the main WONDER components under the shared evaluation setting.
Coverage path planning designs robot routes that systematically survey or act across an area or surface while limiting missed regions, overlap, and travel cost.
Autonomous Unmanned Aerial Vehicle (UAV) swarms are increasingly used as rapidly deployable aerial relays and sensing platforms, yet practical deployments must operate under partial observability and intermittent peer-to-peer connectivity. We present a graph-based multi-agent reinforcement learning framework trained under centralized training with decentralized execution (CTDE): a centralized critic and global state are available only during training, while each UAV executes a shared policy using local observations and messages from nearby neighbors. Under restricted communication, neighbor relations are induced by an SNR-threshold connectivity graph. Our architecture encodes local agent state and nearby entities with an agent-entity attention module and aggregates inter-UAV messages with neighbor self-attention over a signal-quality-limited communication graph defined by a channel model. We evaluate the framework on a cooperative relay-deployment task, DroneConnect. Experimental results show that the proposed method achieves an approximately 12% increase in target coverage over MAPPO under restricted communication and partial observability, while remaining competitive with a mixed-integer linear programming (MILP)-based offline upper bound with full node observability.
Enguang Fan, Yifan Chen, Zihan Shan +3
University of Illinois at Urbana-Champaign · Boeing Research and Technology
This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Aware Inference Engine that proactively reconstructs neighbor trajectories via physical priors. This approach enables an efficient computation-for-communication trade-off, decoupling structural resilience from signaling frequency. Simulations confirm that PL-MARL maintains superior coverage and mission continuity under extreme signaling scarcity and node failure. Our results validate proactive inference as a scalable, low-latency solution for robust aerial coordination, effectively minimizing control overhead to preserve spectrum for payload services while ensuring resilience against interference.
Chuan-Chi Lai, Ang-Hsun Tsai
Department of Communications Engineering, National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan · Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan · Department of Communications Engineering, Feng Chia University, Taichung 407102, Taiwan
Autonomous UAV swarms require scalable coordination mechanisms that maintain mission performance under limited communication, environmental uncertainty, and component failures. Centralized approaches provide global coordination but suffer from communication bottlenecks and single-node vulnerabilities, whereas fully decentralized methods often lack mission-level consistency. This paper presents Layered Autonomous Edge Intelligence (LAEI), a UAV-swarm framework that combines onboard learned policies with lightweight mission-level supervision. Each UAV performs local perception, obstacle avoidance, and action selection onboard, while the supervisory layer provides adaptive goal reassignment, fault-aware recovery, and context-dependent policy guidance without directly controlling low-level actions. LAEI further incorporates recovery strategies, including dynamic reassociation, backup supervisory support, and fallback local autonomy, to maintain mission continuity under representative failure scenarios. We evaluate LAEI in simulated UAV-swarm scenarios using mission completion time, collision rate, and coverage efficiency. The results show that LAEI reduces mission completion time and improves operational efficiency while maintaining collision-aware distributed UAV-level decision-making.
Changmin Park, Wooyong Jung, Hwangnam Kim
School of Electrical Engineering Korea University Seoul 02841, Korea