The upcoming generations of wireless technologies promise an era where everything is interconnected and intelligent. As the need for intelligence grows, networks must learn to better understand the physical world. However, deploying dedicated hardware to perceive the environment is not always feasible, mainly due to costs and/or complexity. Integrated Sensing and Communication (ISAC) has made a step forward in addressing this challenge. Within ISAC, passive sensing emerges as a cost-effective solution that reuses wireless communications to sense the environment, without interfering with existing communications. Nevertheless, the majority of current solutions are limited to one technology (mostly Wi-Fi or 5G), constraining the maximum accuracy reachable. As different technologies work with different spectrums, we see a necessity in integrating more than one technology to augment the coverage area. Hence, we take the advantage of ISAC passive sensing, to present FAWN, a MultiEncoder Fusion-Attention Wave Network for ISAC indoor scene inference. FAWN is based on the original transformers architecture, to fuse information from Wi-Fi and 5G, making the network capable of understanding the physical world without interfering with the current communication. To test our solution, we have built a prototype and integrated it in a real scenario. Results show errors below 0.6 m around 84% of times.

翻译：下一代无线技术预示着一个万物互联且智能化的时代。随着对智能化的需求增长，网络必须学会更好地理解物理世界。然而，部署专用硬件来感知环境并不总是可行的，这主要受限于成本和/或复杂性。通感一体化（ISAC）技术在应对这一挑战方面迈出了重要一步。在ISAC框架下，无源感知作为一种经济高效的解决方案应运而生，它通过复用无线通信来感知环境，而不会干扰现有通信。然而，当前大多数解决方案局限于单一技术（主要是Wi-Fi或5G），这制约了可达的最大精度。由于不同技术采用不同频谱工作，我们认为有必要集成多种技术以扩大覆盖范围。因此，我们利用ISAC无源感知的优势，提出了FAWN——一种面向ISAC室内场景推理的多编码器融合注意力波网络。FAWN基于原始Transformer架构，用于融合来自Wi-Fi和5G的信息，使网络能够在不妨碍当前通信的情况下理解物理世界。为测试我们的方案，我们构建了原型系统并将其部署于真实场景中。实验结果表明，约84%的情况下定位误差低于0.6米。