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

arXiv: https://arxiv.org/abs/2512.01750 · HTML: https://mlsanity.com/paper/2512.01750/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Expert routing strategies, one of 12 directions in Mixture-of-experts.
- Builds on 26 of its 43 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Placed in

- Mixture-of-experts / [Expert routing strategies](https://mlsanity.com/moe/expert-routing-strategies/)

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction](https://mlsanity.com/paper/1911.06255/) (2019)
- [LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications](https://mlsanity.com/paper/2203.05548/) (2022)
- [An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing](https://mlsanity.com/paper/2102.12780/) (2021)
- [Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency](https://mlsanity.com/paper/1905.03761/) (2019)
- [Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines](https://mlsanity.com/paper/2306.14143/) (2023)
- [M3SC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration](https://mlsanity.com/paper/2306.14125/) (2023)
- [Joint Maneuver and Beamforming Design for UAV-Enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2110.02857/) (2021)
- [Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis](https://mlsanity.com/paper/2410.19225/) (2024)
- [Throughput Maximization for UAV-enabled Integrated Periodic Sensing and Communication](https://mlsanity.com/paper/2203.06358/) (2022)
- [From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks](https://mlsanity.com/paper/2506.12308/) (2025)
- [SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)](https://mlsanity.com/paper/2501.07459/) (2025)
- [UAV-enabled Integrated Sensing and Communication: Tracking Design and Optimization](https://mlsanity.com/paper/2401.03726/) (2024)
- [Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems](https://mlsanity.com/paper/2505.02381/) (2025)
- [Sensing-Assisted Communication in Vehicular Networks with Intelligent Surface](https://mlsanity.com/paper/2211.11475/) (2022)
- [Integrated Sensing and Communication enabled Multiple Base Stations Cooperative UAV Detection](https://mlsanity.com/paper/2404.12705/) (2024)
- [Low-Altitude UAV Tracking via Sensing-Assisted Predictive Beamforming](https://mlsanity.com/paper/2509.12698/) (2025)
- [Doppler-Based Multistatic Drone Tracking via Cellular Downlink Signals](https://mlsanity.com/paper/2509.25732/) (2025)
- [Cooperative Multi-Static ISAC Networks: A Unified Design Framework for Active and Passive Sensing](https://mlsanity.com/paper/2510.06654/) (2025)

## Built on by (most cited first, 2 of 2)

- [Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems](https://mlsanity.com/paper/2605.14331/) (2026)
- [AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC](https://mlsanity.com/paper/2609.39964/) (2026)

## Abstract (arXiv)

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.