摘要
OTFS和ISAC技术均是6G移动通信的关键候选技术,OTFS和ISAC的融合系统OTFS-ISAC是当前移动通信研究的前沿.信道估计是接收机的关键处理,对系统性能起着重要的作用.同时,人工智能技术日益成为重要的通信系统信号处理手段.为此,对OTFS-ISAC系统的智能信道估计进行了综述.首先描述OTFS-ISAC的系统模型,包括调制、解调以及雷达和通信模型;其次,详细阐述了三种智能信道估计方法:基于贝叶斯学习的稀疏估计、基于具有自适应阈值的深度卷积残差网络的信道估计和基于迭代深度学习网络的信道估计,这些方法利用了人工智能技术为信道估计问题提供了新的解决途径;然后,探讨了OTFS-ISAC系统中信道估计面临的技术挑战,包括信道特性的复杂性、参数估计的不一致性与复杂性、资源分配和开销问题以及技术融合与兼容性问题;最后,展望了技术创新与突破、标准化与规范化、应用场景的拓展以及跨领域合作与融合的未来发展方向.
Abstract
OTFS and ISAC are pivotal candidate technologies for 6G mobile communications,and the integrated OTFS-ISAC system represents a cutting-edge research focus in this field.Channel estimation,a critical receiver process,significantly impacts system performance.Meanwhile,artificial intelligence(AI)has emerged as a transformative approach for signal processing in communication systems.This paper presents a comprehensive review of intelligent channel estimation for OTFS-ISAC systems.First,the system model of OTFS-ISAC is described,covering modulation,demodulation,and radar-communication models.Subsequently,three intelligent channel estimation methods are detailed:sparse estimation based on Bayesian learning,channel estimation using deep convolutional residual networks with adaptive thresholds,and iterative deep learning-based channel estimation.These methods leverage AI techniques to provide innovative solutions to channel estimation challenges.Furthermore,the technical challenges of channel estimation in OTFS-ISAC systems are discussed,including the complexity of channel characteristics,inconsistencies in parameter estimation,resource allocation and overhead issues,as well as technical integration and compatibility concerns.Finally,the future development directions are explored,emphasizing technological innovation and breakthroughs,standardization,application scenario expansion,and cross-domain collaboration and integration.
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