The proliferation of low-altitude intelligent agents is increasing the demand for timely and socially responsible collaborative sensing in dynamic urban environments. However, jointly addressing heterogeneous spatiotemporal demands, environmental uncertainty, and human-centered operational constraints remains challenging. This paper studies 3D multi-UAV path planning and task assignment under uncertain ground PoI demands. Unlike existing work assuming static and fully known PoIs, we model persistent, temporally predictable, and emergent demands within a unified framework. We further incorporate altitude-dependent societal and environmental costs, including noise exposure and public safety risks, to balance sensing performance with socially compliant operations. To solve the resulting large-scale mixed-integer nonlinear problem, we propose FORTUNE, a hierarchical offline-online framework. Offline, a Transformer predicts Type-II PoI activation windows, while an enhanced sparrow search algorithm generates coordinated flight plans through priority-aware decoding and danger-aware evolution. Online, a lightweight refinement module accommodates emerging Type-III PoIs while preserving global mission coherence. Experiments on real-world traffic data and synthetic scenarios show that FORTUNE consistently outperforms state-of-the-art methods in effectiveness, scalability, and practical applicability.
Minghui Liwang, Wenhan Jia, Xinlei Yi et al.· 0 citations
The emergence of sixth-generation (6G) communication systems promotes an integrated sensing and communications (ISAC) framework to address growing demands for seamless connectivity. This paper proposes a cooperative resource allocation method (CRAM) that enables uninterrupted service for mobile terminals (MTs) moving across heterogeneous base stations (BSs) without resource handovers—inspired by a spotlight tracking a moving actor. Specifically, we tackle the challenge of dynamically managing resources in macro–micro heterogeneous ISAC networks by exploiting real-time MT speed and location information. CRAM achieves a spotlight effect for MTs through two core strategies: a resource pre-allocation mechanism driven by network topology sensing, and a resource sharing strategy that mitigates ping-pong effects in micro-BS overlapping regions. For high-velocity MTs, spectrum resources are assigned at macro-BSs to enhance service quality and minimize handovers. Furthermore, we incorporate the Cramer–Rao lower bound to derive optimized resource allocation policies adapted to diverse MT speeds and locations. Simulation results demonstrate that CRAM tailors resource distribution to MT characteristics, guaranteeing zero-interruption connectivity while maximizing system performance. In comparison with existing benchmarks, CRAM improves system throughput by 24.5% and 20.9%, reduces the total number of handovers by 29.1% and 16.5%, and increases the average signal-to-interference-plus-noise ratio (SINR) for all MTs by 41.3% and 30.3%. These outcomes highlight CRAM’s potential to redefine 6G network management by substantially boosting network efficiency and user experience.
Sai Zou, Meifang Wang, Minghui Liwang et al.· IEEE Transactions on Cogniti...· 0 citations
Future 6G networks are envisaged to tightly integrate communication, sensing, and computing, demanding real-time, intent-driven intelligence at the edge. While large language models (LLMs) excel in intent recognition and semantic reasoning, their application to real-time network lifecycle management at the edge is limited by heterogeneous application intents (APPIs), dynamic network conditions, and severe resource constraints. This paper proposes a novel lightweight LLM architecture, KGLlama-KD, that synergizes knowledge graphs (KGs) with knowledge distillation (KD) to enable intent-driven networking and enhance 6G edge intelligence. Specifically, a KG is constructed to formally describe the relationships among application scenarios, functional primitives, performance requirements within APPIs, and the correspondences between APPIs and network service requests (NSRs), thereby producing a structured intent training dataset. Building upon the Llama 3 foundation model, a two-phase optimization framework is designed to support lightweight edge deployment while preserving translation fidelity. The LLM is first fine-tuned with KG guidance and compressed via KD in the cloud, and then deployed on resource-constrained edge nodes to perform real-time, accurate, and efficient APPIs interpretation. Experiments validate that KGLlama-KD achieves 95% accuracy for APPI understanding, surpassing DeepSeek and Qwen by an average of 8%. The distilled model reduces inference latency by 60% compared to full-scale LLMs, fulfilling the sub-100 ms requirement for 6G latency-sensitive services.
Bing Wu, Sai Zou, Minghui Liwang et al.· IEEE Transactions on Mobile...· 3 citations