Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios

In TDD mmWave massive MIMO systems, the downlink CSI can be attained through uplink channel estimation thanks to the uplink-downlink channel reciprocity. However, the channel aging issue is significant under high-mobility scenarios and thus necessitates frequent uplink channel estimation. In addition, large amounts of antennas and subcarriers lead to high-dimensional CSI matrices, aggravating the pilot training overhead. To systematically reduce the pilot overhead, a spatial, frequency, and temporal domain (3D) channel extrapolation framework is proposed in this paper. Considering the marginal effects of pilots in the spatial and frequency domains and the effectiveness of traditional knowledge-driven channel estimation methods, we first propose a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) for uplink channel estimation by exploiting the least square estimator for coarse channel estimation and joint spatial-frequency channel extrapolation to reduce the spatial-frequency domain pilot overhead. Then, resorting to the uplink-downlink channel reciprocity and temporal domain dependencies of downlink channels, a temporal uplink-downlink channel extrapolation network (TUDCEN) is proposed for slot-level channel extrapolation, aiming to enlarge the pilot signal period and thus reduce the temporal domain pilot overhead under high-mobility scenarios. Specifically, we propose the spatial-frequency sampling embedding module to reduce the representation dimension and consequent computational complexity, and we propose to exploit the autoregressive generative Transformer for generating downlink channels autoregressively. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by more than 16 times and improving the system's spectral efficiency under high-mobility scenarios.

Integrated Sensing and Communications Framework for 6G Networks

In this paper, we propose a novel integrated sensing and communications (ISAC) framework for the sixth generation (6G) mobile networks, in which we decompose the real physical world into static environment, dynamic targets, and various object materials. The ubiquitous static environment occupies the vast majority of the physical world, for which we design static environment reconstruction (SER) scheme to obtain the layout and point cloud information of static buildings. The dynamic targets floating in static environments create the spatiotemporal transition of the physical world, for which we design comprehensive dynamic target sensing (DTS) scheme to detect, estimate, track, image and recognize the dynamic targets in real-time. The object materials enrich the electromagnetic laws of the physical world, for which we develop object material recognition (OMR) scheme to estimate the electromagnetic coefficient of the objects. Besides, to integrate these sensing functions into existing communications systems, we discuss the interference issues and corresponding solutions for ISAC cellular networks. Furthermore, we develop an ISAC hardware prototype platform that can reconstruct the environmental maps and sense the dynamic targets while maintaining communications services. With all these designs, the proposed ISAC framework can support multifarious emerging applications, such as digital twins, low altitude economy, internet of vehicles, marine management, deformation monitoring, etc.

Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory

In this paper, we propose an environment sensing-aided beam prediction model for smart factory that can be transferred from given environments to a new environment. In particular, we first design a pre-training model that predicts the optimal beam by sensing the present environmental information. When encountering a new environment, it generally requires collecting a large amount of new training data to retrain the model, whose cost severely impedes the application of the designed pre-training model. Therefore, we next design a transfer learning strategy that fine-tunes the pre-trained model by limited labeled data of the new environment. Simulation results show that when the pre-trained model is fine-tuned by 30\% of labeled data from the new environment, the Top-10 beam prediction accuracy reaches 94\%. Moreover, compared with the way to completely re-training the prediction model, the amount of training data and the time cost of the proposed transfer learning strategy reduce 70\% and 75\% respectively.

Electromagnetic Property Sensing in ISAC with Multiple Base Stations: Algorithm, Pilot Design,and Performance Analysis

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals to sense the electromagnetic (EM) property of the target and thus identify the materials of the target. Specifically, we first establish an EM wave propagation model with Maxwell equations, where the EM property of the target is captured by a closed-form expression of the channel. We then build the mathematical model for the relative permittivity and conductivity distribution (RPCD) within a predetermined region of interest shared by multiple base stations (BSs). Based on the EM wave propagation model, we propose an EM property sensing method, in which the RPCD can be reconstructed from compressive sensing techniques that exploits the joint sparsity structure of the EM property vector. We then develop a fusion algorithm to combine data from multiple BSs, which can enhance the reconstruction accuracy of EM property by efficiently integrating diverse measurements. Moreover, the fusion is performed at the feature level of RPCD and features low transmission overhead. We further design the pilot signals that can minimize the mutual coherence of the equivalent channels and enhance the diversity of incident EM wave patterns. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification.

Environment Reconstruction based on Multi-User Selection and Multi-Modal Fusion in ISAC

Integrated sensing and communications (ISAC) has been deemed as a key technology for the sixth generation (6G) wireless communications systems. In this paper, we explore the inherent clustered nature of wireless users and design a multi-user based environment reconstruction scheme. Specifically, we first select users based on the estimation precision of channel's multipath, including the line-of-sight (LOS) and the non-line-of-sight (NLOS) paths, to enhance the accuracy of environment reconstruction. Then, we develop a fusion strategy that merges communications signalling with camera image to increase the accuracy and robustness of environment reconstruction. The simulation results demonstrate that the proposed algorithm can achieve a remarkable sensing accuracy of centimeter level, which is about 17 times better than the scheme without user selection. Meanwhile, the fusion of communications data and vision data leads to a threefold accuracy improvement over the image only method, especially under challenging weather conditions like raining and snowing.

STAR-RIS Aided Integrated Sensing and Communication over High Mobility Scenario

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user equipment (UE) and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle. Firstly, an efficient transmission structure is developed, where a number of training sequences with orthogonal precoders and combiners are respectively utilized at BS and RSUs for channel parameter extraction. Then, the near-field static channel model between the STAR-RIS and in-vehicle UE as well as the far-field time-frequency selective BS-RIS-RSUs channel model are characterized. By utilizing the multidimensional orthogonal matching pursuit (MOMP) algorithm, the cascaded channel parameters of the BS-RIS-RSUs links can be obtained at the RSUs. Thus, the vehicle localization and its velocity measurement can be acquired by jointly utilizing these extracted cascaded channel parameters of all RSUs. Note that the MOMP algorithm can be further utilized to extract the channel parameters of the BS-RIS-UE link for communication. With the help of sensing results, the phase shifts of the STAR-RIS are delicately designed, which can significantly improve the received signal strength for both the RSUs and the in-vehicle UE, and can finally enhance the sensing and communication performance. Moreover, the trade-off for sensing and communication is designed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.

Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we develop a novel material sensing scheme that utilizes OFDM pilot signals in ISAC systems to sense the electromagnetic (EM) property and identify the material of the target. Specifically, we first establish an end-to-end EM propagation model by means of Maxwell equations, where the electrical properties of the material are captured by a closed-form expression for the non-line-of-sight (NLOS) channel, incorporating the Lippmann-Schwinger equation and the method of moments (MOM) for discretization. We then model the relative permittivity and conductivity distribution (RPCD) within a specified detection region. Based on the sensing model, we introduce a multi-frequency-based material sensing method by which the RPCD can be reconstructed from compressive sensing techniques that exploits the joint sparsity structure of the contrast source vector. To improve the sensing accuracy, we design a beamforming strategy from the communications transmitter based on the Born approximation, which can minimize the mutual coherence of the sensing matrix. The optimization problem is cast in terms of the Gram matrix and is solved iteratively to obtain the optimal beamforming matrix. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification. Furthermore, improvements in RPCD reconstruction quality and material classification accuracy are observed with increased signal-to-noise ratio (SNR) or reduced target-transmitter distance.

6D Radar Sensing and Tracking in Monostatic Integrated Sensing and Communications System

In this paper, we propose a novel scheme for sixdimensional (6D) radar sensing and tracking of dynamic target based on multiple input and multiple output (MIMO) array for monostatic integrated sensing and communications (ISAC) system. Unlike most existing ISAC studies believing that only the radial velocity of far-field dynamic target can be measured based on one single base station (BS), we find that the sensing echo channel of MIMO-ISAC system actually includes the distance, horizontal angle, pitch angle, radial velocity, horizontal angular velocity, and pitch angular velocity of the dynamic target. Thus we may fully rely on one single BS to estimate the dynamic target's 6D motion parameters from the sensing echo signals. Specifically, we first propose the long-term motion and short-term motion model of dynamic target, in which the short-term motion model serves the single-shot sensing of dynamic target, while the long-term motion model serves multiple-shots tracking of dynamic target. As a step further, we derive the sensing channel model corresponding to the short-term motion. Next, for singleshot sensing, we employ the array signal processing methods to estimate the dynamic target's horizontal angle, pitch angle, distance, and virtual velocity. By realizing that the virtual velocities observed by different antennas are different, we adopt plane fitting to estimate the radial velocity, horizontal angular velocity, and pitch angular velocity of dynamic target. Furthermore, we implement the multiple-shots tracking of dynamic target based on each single-shot sensing results and Kalman filtering. Simulation results demonstrate the effectiveness of the proposed 6D radar sensing and tracking scheme.

A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems

Accurate channel state information (CSI) is essential for downlink precoding at the base station (BS), especially for frequency FDD wideband massive MIMO systems with OFDM. In FDD systems, CSI is attained through CSI feedback from the user equipment (UE). However, large-scale antennas and large number of subcarriers significantly increase CSI feedback overhead. Deep learning-based CSI feedback methods have received tremendous attention in recent years due to their great capability of compressing CSI. Nonetheless, large amounts of collected samples are required to train deep learning models, which is severely challenging in practice. Besides, with the rapidly increasing number of antennas and subcarriers, most of these deep learning methods' CSI feedback overhead also grow dramatically, owing to their focus on full-dimensional CSI feedback. To address this issue, in this paper, we propose a low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems. To further reduce the feedback overhead, a low-dimensional eigenvector-based CSI matrix is first formed with the incorporation process at the UE, and then recovered to the full-dimensional eigenvector-based CSI matrix at the BS via the extrapolation process. After that, to alleviate the necessity of the extensive collected samples and enable few-shot CSI feedback, we further propose a knowledge-driven data augmentation method and an artificial intelligence-generated content (AIGC) -based data augmentation method by exploiting the domain knowledge of wireless channels and by exploiting a novel generative model, respectively. Numerical results demonstrate that the proposed IEFSF can significantly reduce CSI feedback overhead by 16 times compared with existing CSI feedback methods while maintaining higher feedback accuracy using only several hundreds of collected samples.

Integrated Sensing and Communications in Clutter Environment

In this paper, we propose a practical integrated sensing and communications (ISAC) framework to sense dynamic targets from clutter environment while ensuring users communications quality. To implement communications function and sensing function simultaneously, we design multiple communications beams that can communicate with the users as well as one sensing beam that can rotate and scan the entire space. To minimize the interference of sensing beam on existing communications systems, we divide the service area into sensing beam for sensing (S4S) sector and communications beam for sensing (C4S) sector, and provide beamforming design and power allocation optimization strategies for each type sector. Unlike most existing ISAC studies that ignore the interference of static environmental clutter on target sensing, we construct a mixed sensing channel model that includes both static environment and dynamic targets. When base station receives the echo signals, the mean phasor cancellation (MPC) method is employed to filter out the interference from static environmental clutter and to extract the effective dynamic target echoes. Then a complete and practical dynamic target sensing scheme is designed to detect the presence of dynamic targets and to estimate their angles, distances, and velocities. In particular, dynamic target detection and angle estimation are realized through angle-Doppler spectrum estimation (ADSE) and joint detection over multiple subcarriers (MSJD), while distance and velocity estimation are realized through the extended subspace algorithm. Simulation results demonstrate the effectiveness of the proposed scheme and its superiority over the existing methods that ignore environmental clutter.