We propose a robust beamforming design methodology for integrated sensing and communications (ISACs) beamform, where the beamforming design is investigated under the sensing optimal beamforming designed to overcome the channel uncertainty that arises from the communication system. Under the assumption that the channel state information (CSI) error is elliptically bounded, we study the robust ISAC beamforming design problem with the minimization of the Cramér-Rao bound (CRB) under the signal-to-noise ratio (SINR) threshold constraint. We consider the long-range and near-range cases separately and categorize them into point-target and extended-target for processing. In the point target scenario, we address the problem through distributed optimization using the S-procedure and solve it with the semidefinite relaxation (SDR) method. Meanwhile, in the extended target scenario, we transform the infinite constraints of the robust ISAC design problem into a finite set, employing linear matrix inequalities (LMIs) for equivalent representation. Under specific conditions, we illustrate that the SDR problem in this scenario can yield a rank-1 solution. Simulation results verify the effectiveness of the proposed CRB optimizationmin method and prove its application value in the next generation of Internet of Things devices.

We propose a robust beamforming design methodology for integrated sensing and communications beamforms. The beamforming design aims to address channel uncertainties in the communication system by optimizing the sensing beamform. Assuming the Channel State Information (CSI) error is elliptically bounded, we investigate the robust integrated sensing and communication (ISAC) beamforming design problem, focusing on minimizing the Cramér-Rao bound (CRB) under a signal-to-noise ratio (SINR) threshold constraint. The problem is addressed through distributed optimization using the S-procedure and solved with the SDR method. Simulation results verify the effectiveness of the proposed CRB_min method.

To solve the problems of limited computing resources and data privacy in the IoE scenario of 6G networks, this paper propose an efficient transmission and secure sharing architecture of sensing data based on federated learning. The architecture considers an integrated sensing and communication (ISAC) approach, employs knowledge distillation techniques to compress and accelerate data processing models, and implements data communication technology based on airborne computing aggregation to reduce data transmission delays and improve the efficiency of data communication and computation among nodes. To address the challenge of data sharing for largescale heterogeneous network nodes in the integrated scenario, this paper adopts a sample expansion technology of distributed remote sensing data based on WGAN-GP to address the issue of insufficient data, and considers blockchain encryption technology to protect data privacy, thus promoting progress in data privacy sharing under distributed ISAC conditions and facilitating the construction of the 6G communication network.

Efficient and green transmission of communication and sensing (C&S) signals is a vital problem in Internet of Things (IoT) networks. In this article, we propose a variational autoencoder (VAE)-empowered deep learning (DL) network to fuse and reconstruct the integrated C&S signals. Specifically, we present a convolutional neural network to fuse the input communication data and SAR images into a combined representation, which can then be transmitted to other nodes in space–air–ground–ocean-integrated IoT networks. Instead of directly transmitting C&S data, the transmission of a fused feature vector can greatly save network resources and reduce network burden. Then, a mirrored deconvolutional network is constructed to recover C&S data from the transmitted feature representation. An end-to-end unsupervised training strategy is considered to train the proposed DL network without any label information and human labor. Qualitative and quantitative experiments demonstrate the feasibility of our proposed approach for transmitting and reconstructing integrated C&S signals. Further analysis on hyperparameter sensitivity and loss functions verifies the necessity and efficiency of the components in the proposed DL model. Note that the proposed efficient fusion and reconstruction schemes for C&S signals may provide the convenience to information sharing under the distributed scenarios.

With the continuous evolution of emerging technologies such as mobile network, machine learning (ML), 5G, etc., digital twins (DT) bursts out great potential by its capacity of data analysis, data tracking, data prediction, etc, building a bridge between the physical and information world. Meanwhile, mobile network is moving towards data-driven paradigm, the issue of data privacy and data security seem to be a bottleneck. As a result, federated learning (FL) and mobile network are deeply converging. However, the mobile network is time-varying and the parameters of FL-empowered mobile network is huge and continue to increase with exponential growth of wireless terminals, result in the failure of traditional modeling. In the mobile networks, DT is conducive to prototyping, testing, and optimization, enabling mobile networks to be modelled more efficiently in a virtual environment and thus providing guidance for practical application. To this end, a communication-assisted sensing scenario is considered in this paper with FL in DT-empowered mobile networks. More specifically, two communication-assisted sensing architectures are proposed to improve communication efficiency of mobile network, namely, centralized architecture of federated transfer learning (FTL) and decentralized architecture of FTL. For centralized architecture of FTL, feature extraction of sensing information is conducted by FL between partial nodes and central server while the remaining nodes are used to train the fully connected layers at the central server. Considering data safety during the communication between sensing nodes, a decentralized architecture is designed based on FTL and Blockchain, where the feature extraction module is obtained by the fusion of sharing model (by Blockchain) and local model. The performance of proposed schemes is evaluated and demonstrated by the simulations.