Multimodal Mixture-of-Experts for ISAC in Low-Altitude Wireless Networks

• URL: http://arxiv.org/abs/2512.01750v1
• Date: Mon, 01 Dec 2025 14:53:29 GMT
• Title: Multimodal Mixture-of-Experts for ISAC in Low-Altitude Wireless Networks
• Authors: Kai Zhang, Wentao Yu, Hengtao He, Shenghui Song, Jun Zhang, Khaled B. Letaief,
• Abstract summary: Integrated sensing and communication (ISAC) is a key enabler for low-altitude wireless networks (LAWNs)<n>We propose a mixture-of-experts (MoE) framework for multimodal ISAC in LAWNs.<n>To improve scalability under the stringent energy constraints of aerial platforms, we develop a sparse MoE variant that selectively activates only a subset of experts.
• Score: 33.986844210423996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrated sensing and communication (ISAC) is a key enabler for low-altitude wireless networks (LAWNs), providing simultaneous environmental perception and data transmission in complex aerial scenarios. By combining heterogeneous sensing modalities such as visual, radar, lidar, and positional information, multimodal ISAC can improve both situational awareness and robustness of LAWNs. However, most existing multimodal fusion approaches use static fusion strategies that treat all modalities equally and cannot adapt to channel heterogeneity or time-varying modality reliability in dynamic low-altitude environments. To address this fundamental limitation, we propose a mixture-of-experts (MoE) framework for multimodal ISAC in LAWNs. Each modality is processed by a dedicated expert network, and a lightweight gating module adaptively assigns fusion weights according to the instantaneous informativeness and reliability of each modality. To improve scalability under the stringent energy constraints of aerial platforms, we further develop a sparse MoE variant that selectively activates only a subset of experts, thereby reducing computation overhead while preserving the benefits of adaptive fusion. Comprehensive simulations on three typical ISAC tasks in LAWNs demonstrate that the proposed frameworks consistently outperform conventional multimodal fusion baselines in terms of learning performance and training sample efficiency.

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