arXiv subjects

Fangzhi Li

Publications and source records attributed to Fangzhi Li.

3 recordsLinked to original sources

arXiv2026

Cooperative ISAC for LAE: Joint Trajectory Planning, Power allocation, and Dynamic Time Division

To enhance the performance of aerial-ground networks, this paper proposes an integrated sensing and communication (ISAC) framework for multi-UAV systems. In our model, ground base stations (BSs) cooperatively serve multiple unmanned aerial vehicles (UAVs), employing a dynamic time-division strategy where beam scanning for sensing precedes data communication in each time slot. To maximize the sum communication rate while satisfying a mission-level cumulative radar mutual information (MI) requirement, we jointly optimize the UAV trajectories, communication and sensing power allocation, and the time-division ratio. The resulting highly coupled non-convex optimization problem is efficiently solved using an alternating optimization (AO) and successive convex approximation (SCA) framework, which yields a non-decreasing objective sequence and convergence to a finite objective value under the adopted surrogate-based iterative procedure. Extensive simulation results demonstrate that our proposed joint design significantly outperforms benchmark schemes with static trajectories, partially optimized resources, or non-cooperative single-BS transmission. Furthermore, a comprehensive sensitivity analysis reveals the distinct mechanisms by which sensing thresholds and the number of UAVs influence resource allocation and spatial organization, highlighting the critical importance of dynamic, multi-dimensional resource management for effectively navigating the sensing-communication trade-off in low-altitude economies.

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arXiv2026

CRS-LLM: Cooperative Beam Prediction with a GPT-Style Backbone and Switch-Gated Fusion

Millimeter-wave (mmWave) communication depends on highly directional beamforming, while fast mobility, blockage, and rapid geometry changes in vehicle-to-everything (V2X) scenarios make beam tracking challenging. In cooperative multi-base-station (BS) systems, conventional hierarchical methods usually separate BS selection and beam selection, which may cause error propagation when beam states change abruptly. To address this issue, this paper proposes Cooperative Radio Sensing with Large Language Models (CRS-LLM), a cooperative beam prediction framework for next-step joint BS-beam prediction. CRS-LLM formulates beam tracking as a single classification problem over the joint BS-beam space, avoiding cascaded decision errors. To adapt channel state information (CSI) to large language models, a dual-view CSI tokenizer extracts frequency-domain and delay-domain channel features through a lightweight CNN front-end and temporal tokenization module. A truncated GPT-style backbone is then used for temporal modeling with parameter-efficient adaptation. In addition, a transition-aware switch-gated predictor combines a stable branch, a residual flip branch, and a low-rank transition prior to capture both smooth evolution and abrupt changes. Simulation results show that CRS-LLM outperforms CSI-Transformer, Hierarchical BS-Beam, and representative CNN- and recurrent-neural-network baselines in Top-1 accuracy and normalized beam gain under different SNR conditions, while also showing strong few-shot performance and promising zero-shot transferability.

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arXiv2025

Cooperative Sensing and Communication Beamforming Design for Low-Altitude Economy

To empower the low-altitude economy with high-accuracy sensing and high-rate communication, this paper proposes a cooperative integrated sensing and communication (ISAC) framework for aerial-ground networks. In the proposed system, the ground base stations (BSs) cooperatively serve the unmanned aerial vehicles (UAVs), which are equipped for either joint communication and sensing or sensing-only operations. The BSs employ coordinated beamforming to simultaneously transmit communication and sensing signals, while the UAVs execute their missions. To maximize the weighted sum rate under the sensing signal-to-interference-plus-noise ratio (SINR) constraints, we jointly optimize the transmit beamforming, receive filtering, and UAV trajectory. The resulting non-convex problem is solved using an alternating optimization framework incorporating semidefinite relaxation (SDR) and successive convex approximation (SCA). Simulation results demonstrate that the proposed joint design achieves higher communication throughput while ensuring required sensing robustness. Additionally, the sensing SINR threshold and the UAV altitude have a significant impact on the trajectory design, highlighting the necessity of adaptive deployment strategies in practical applications.

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