用隐空间建模控制与信道动态，降低50%发射功率。
From Pixels to CSI: Distilling Latent Dynamics For Efficient Wireless Resource Management
- 联合嵌入预测架构在隐空间联合建模控制与信道状态。
- 仿真显示发射功率降低超50%，控制性能相当基准方法。
- 适合无线资源优化与远程控制场景的高效算法研究者。
本文旨在优化远程控制器与设备间通信系统的无线资源管理，其状态由图像帧表示，且不牺牲控制任务性能。我们提出一种新型机器学习技术，联合建模并预测控制系统动态与无线传播环境的隐空间动态。该方法采用两个耦合的联合嵌入预测架构（JEPAs）：控制型JEPA建模控制动态，并引导无线JEPA通过跨模态条件建模设备信道状态信息（CSI）的动态。随后，训练深度强化学习（RL）算法从隐空间控制动态中推导出控制策略，并设计一个功率预测器，基于隐空间的CSI表示估计具有有利信道条件的调度间隔。由此，控制器利用耦合的JEPA网络在隐空间中预判设备轨迹，从而最小化无线资源使用。我们在合成多模态数据上进行仿真，结果表明所提方法在保持控制性能接近基线方法的同时，将发射功率降低超过50%。
原文摘要 · Abstract (English)
In this work, we aim to optimize the radio resource management of a communication system between a remote controller and its device, whose state is represented through image frames, without compromising the performance of the control task. We propose a novel machine learning (ML) technique to jointly model and predict the dynamics of the control system as well as the wireless propagation environment in latent space. Our method leverages two coupled joint-embedding predictive architectures (JEPAs): a control JEPA models the control dynamics and guides the predictions of a wireless JEPA, which captures the dynamics of the device's channel state information (CSI) through cross-modal conditioning. We then train a deep reinforcement learning (RL) algorithm to derive a control policy from latent control dynamics and a power predictor to estimate scheduling intervals with favorable channel conditions based on latent CSI representations. As such, the controller minimizes the usage of radio resources by utilizing the coupled JEPA networks to imagine the device's trajectory in latent space. We present simulation results on synthetic multimodal data and show that our proposed approach reduces transmit power by over 50% while maintaining control performance comparable to baseline methods that do not account for wireless optimization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。