Integrated sensing and communications (ISAC) is pivotal for 6G communications
and is boosted by the rapid development of reconfigurable intelligent surfaces
(RISs). Using the channel state information (CSI) across multiple frequency
bands, RIS-aided multi-band ISAC systems can potentially track users' positions
with high precision. Though tracking with CSI is desirable as no communication
overheads are incurred, it faces challenges due to the multi-modalities of CSI
samples, irregular and asynchronous data traffic, and sparse labeled data for
learning the tracking function. This paper proposes the X2Track framework,
where we model the tracking function by a hierarchical architecture, jointly
utilizing multi-modal CSI indicators across multiple bands, and optimize it in
a cross-domain manner, tackling the sparsity of labeled data for the target
deployment environment (namely, target domain) by adapting the knowledge
learned from another environment (namely, source domain). Under X2Track, we
design an efficient deep learning algorithm to minimize tracking errors, based
on transformer neural networks and adversarial learning techniques. Simulation
results verify that X2Track achieves decimeter-level axial tracking errors even
under scarce UL data traffic and strong interference conditions and can adapt
to diverse deployment environments with fewer than 5% training data, or
equivalently, 5 minutes of UE tracks, being labeled.

提出了一种名为 X2Track 的框架，通过多模态 CSI 指标和跨领域优化的方式模型化追踪功能，并设计了基于转换神经网络和对抗学习技术的高效深度学习算法，以实现在稀缺的 UL 数据流量和强干扰条件下的几毫米级轴向追踪误差，并能够适应多种部署环境，只需少于 5% 的训练数据或等效于 5 分钟的 UE 轨迹进行标注。

RIS 辅助的多波段 ISAC 系统中基于跨域学习的用户追踪框架

Cross-domain Learning Framework for Tracking Users in RIS-aided  Multi-band ISAC Systems with Sparse Labeled Data

In the past few years, deep learning (DL) techniques have been introduced for
designing sparse arrays. These methods offer the advantages of feature
engineering and low prediction-stage complexity, which is helpful in tackling
the combinatorial search inherent to finding a sparse array. In this chapter,
we provide a synopsis of several direction finding applications of DL-based
sparse arrays. We begin by examining supervised and transfer learning
techniques that have applications in selecting sparse arrays for a cognitive
radar application. Here, we also discuss the use of meta-heuristic learning
algorithms such as simulated annealing for the case of designing
two-dimensional sparse arrays. Next, we consider DL-based antenna selection for
wireless communications, wherein sparse array problem may also be combined with
channel estimation, beamforming, or localization. Finally, we provide an
example of deep sparse array technique for integrated sensing and
communications (ISAC) application, wherein a trade-off of radar and
communications performance makes ISAC sparse array problem very challenging.
For each setting, we illustrate the performance of model-based optimization and
DL techniques through several numerical experiments. We discuss additional
considerations required to ensure robustness of DL-based algorithms against
various imperfections in array data.

近年来，深度学习技术已被引入用于设计稀疏阵列，本文综述了基于深度学习的稀疏阵列在方向寻找、天线选择、集成感知与通信等领域的应用，并通过数值实验展示了模型优化和深度学习技术在这些问题上的性能表现和鲁棒性要求。

利用深度学习进行方向找测的稀疏阵列设计

Sparse Array Design for Direction Finding using Deep Learning

Recent advances in wireless communication and solid-state circuits together
with the enormous demands of sensing ability have given rise to a new enabling
technology, integrated sensing and communications (ISAC). The ISAC captures two
main advantages over dedicated sensing and communication functionalities: 1)
Integration gain to efficiently utilize congested resources, and even, 2)
Coordination gain to balance dual-functional performance or/and perform mutual
assistance. Meanwhile, triggered by ISAC, we are also witnessing a paradigm
shift in the ubiquitous IoT architecture, in which the sensing and
communication layers are tending to converge into a new layer, namely, the
signaling layer.
In this paper, we first attempt to introduce a definition of ISAC, analyze
the various influencing forces, and present several novel use cases. Then, we
complement the understanding of the signaling layer by presenting several key
benefits in the IoT era. We classify existing dominant ISAC solutions based on
the layers in which integration is applied. Finally, several challenges and
opportunities are discussed. We hope that this overview article will serve as a
primary starting point for new researchers and offer a bird's-eye view of the
existing ISAC-related advances from academia and industry, ranging from
solid-state circuitry, signal processing, and wireless communication to mobile
computing.

本文介绍了一种新的启用技术，即集成感知和通信（ISAC），它在 IoT 架构中的关键作用以及其带来的协同优势和整合优势，并介绍了一些 ISAC 的新用例。该文将 ISAC 方案分为多个层次进行分类，并探讨了它面临的几个挑战。