Channel-Adaptive Wireless Image Semantic Transmission with Learnable Prompts

Recent developments in Deep learning based Joint Source-Channel Coding (DeepJSCC) have demonstrated impressive capabilities within wireless semantic communications system. However, existing DeepJSCC methodologies exhibit limited generalization ability across varying channel conditions, necessitating the preparation of multiple models. Optimal performance is only attained when the channel status during testing aligns precisely with the training channel status, which is very inconvenient for real-life applications. In this paper, we introduce a novel DeepJSCC framework, termed Prompt JSCC (PJSCC), which incorporates a learnable prompt to implicitly integrate the physical channel state into the transmission system. Specifically, the Channel State Prompt (CSP) module is devised to generate prompts based on diverse SNR and channel distribution models. Through the interaction of latent image features with channel features derived from the CSP module, the DeepJSCC process dynamically adapts to varying channel conditions without necessitating retraining. Comparative analyses against leading DeepJSCC methodologies and traditional separate coding approaches reveal that the proposed PJSCC achieves optimal image reconstruction performance across different SNR settings and various channel models, as assessed by Peak Signal-to-Noise Ratio (PSNR) and Learning-based Perceptual Image Patch Similarity (LPIPS) metrics. Furthermore, in real-world scenarios, PJSCC shows excellent memory efficiency and scalability, rendering it readily deployable on resource-constrained platforms to facilitate semantic communications.

Interference Management in MIMO-ISAC Systems: A Transceiver Design Approach

The integrated sensing and communication (ISAC) system under multi-input multi-output (MIMO) architecture achieves dual functionalities of sensing and communication on the same platform by utilizing spatial gain, which provides a feasible paradigm facing spectrum congestion. However, the dual functionalities of sensing and communication operating simultaneously in the same platform bring severe interference in the ISAC systems. Facing this challenge, we propose a joint optimization framework for transmit beamforming and receive filter design for ISAC systems with MIMO architecture. We aim to maximize the signal-to-clutter-plus-noise ratio (SCNR) at the receiver while considering various constraints such as waveform similarity, power budget, and communication performance requirements to ensure the integration of the dual functionalities. In particular, the overall transmit beamforming is refined into sensing beamforming and communication beamforming, and a quadratic transformation (QT) is introduced to relax and convert the complex non-convex optimization objective. An efficient algorithm based on covariance matrix tapers (CMT) is proposed to restructure the clutter covariance matrix considering the mismatched steering vector, thereby improving the robustness of the ISAC transceiver design. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm.

Neural Probabilistic Logic Learning for Knowledge Graph Reasoning

Knowledge graph (KG) reasoning is a task that aims to predict unknown facts based on known factual samples. Reasoning methods can be divided into two categories: rule-based methods and KG-embedding based methods. The former possesses precise reasoning capabilities but finds it challenging to reason efficiently over large-scale knowledge graphs. While gaining the ability to reason over large-scale knowledge graphs, the latter sacrifices reasoning accuracy. This paper aims to design a reasoning framework called Neural Probabilistic Logic Learning(NPLL) that achieves accurate reasoning on knowledge graphs. Our approach introduces a scoring module that effectively enhances the expressive power of embedding networks, striking a balance between model simplicity and reasoning capabilities. We improve the interpretability of the model by incorporating a Markov Logic Network based on variational inference. We empirically evaluate our approach on several benchmark datasets, and the experimental results validate that our method substantially enhances the accuracy and quality of the reasoning results.

Hybrid Beamforming Design for Near-Field ISAC with Modular XL-MIMO

A novel modular extremely large-scale multiple-input-multiple-output (XL-MIMO) integrated sensing and communication (ISAC) framework is proposed in this paper. We consider a downlink ISAC scenario and exploit the modular array architecture to enhance the communication spectral efficiency and sensing resolution while reducing the channel modeling complexity by employing the hybrid spherical and planar wavefront model. Considering the hybrid digital-analog structure inherent to modular arrays, we formulate a joint analog-digital beamforming design problem based on the communication spectral efficiency and sensing signal-to-clutter-plus-noise ratio (SCNR). By exploring the structural similarity of the communication and sensing channels, it is proved that the optimal transmit covariance matrix lies in the subspace spanned by the subarray response vectors, yielding a closed-form solution for the optimal analog beamformer. Consequently, the joint design problem is transformed into a low-dimensional rank-constrained digital beamformer optimization. We first propose a manifold optimization method that directly optimizes the digital beamformer on the rank-constrained Stiefel manifold. Additionally, we develop an semidefinite relaxation (SDR)-based approach that relaxes the rank constraint and employ the randomization technique to obtain a near-optimal solution. Simulation results demonstrate the effectiveness of the proposed modular XL-MIMO ISAC framework and algorithms.

Carrier Aggregation Enabled MIMO-OFDM Integrated Sensing and Communication

In the evolution towards the forthcoming era of sixth-generation (6G) mobile communication systems characterized by ubiquitous intelligence, integrated sensing and communication (ISAC) is in a phase of burgeoning development. However, the capabilities of communication and sensing within single frequency band fall short of meeting the escalating demands. To this end, this paper introduces a carrier aggregation (CA)- enabled multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system fusing the sensing data on high and low-frequency bands by symbol-level fusion for ultimate communication experience and high-accuracy sensing. The challenges in sensing signal processing introduced by CA include the initial phase misalignment of the echo signals on high and low-frequency bands due to attenuation and radar cross section, and the fusion of the sensing data on high and lowfrequency bands with different physical-layer parameters. To this end, the sensing signal processing is decomposed into two stages. In the first stage, the problem of initial phase misalignment of the echo signals on high and low-frequency bands is solved by the angle compensation, space-domain diversity and vector crosscorrelation operations. In the second stage, this paper realizes symbol-level fusion of the sensing data on high and low-frequency bands through sensing vector rearrangement and cyclic prefix adjustment operations, thereby obtaining high-precision sensing performance. Then, the closed-form communication mutual information (MI) and sensing Cramer-Rao lower bound (CRLB) for the proposed ISAC system are derived to explore the theoretical performance bound with CA. Simulation results validate the feasibility and superiority of the proposed ISAC system.

Multi-Objective Optimization-based Transmit Beamforming for Multi-Target and Multi-User MIMO-ISAC Systems

Integrated sensing and communication (ISAC) is an enabling technology for the sixth-generation mobile communications, which equips the wireless communication networks with sensing capabilities. In this paper, we investigate transmit beamforming design for multiple-input and multiple-output (MIMO)-ISAC systems in scenarios with multiple radar targets and communication users. A general form of multi-target sensing mutual information (MI) is derived, along with its upper bound, which can be interpreted as the sum of individual single-target sensing MI. Additionally, this upper bound can be achieved by suppressing the cross-correlation among reflected signals from different targets, which aligns with the principles of adaptive MIMO radar. Then, we propose a multi-objective optimization framework based on the signal-to-interference-plus-noise ratio of each user and the tight upper bound of sensing MI, introducing the Pareto boundary to characterize the achievable communication-sensing performance boundary of the proposed ISAC system. To achieve the Pareto boundary, the max-min system utility function method is employed, while considering the fairness between communication users and radar targets. Subsequently, the bisection search method is employed to find a specific Pareto optimal solution by solving a series of convex feasible problems. Finally, simulation results validate that the proposed method achieves a better tradeoff between multi-user communication and multi-target sensing performance. Additionally, utilizing the tight upper bound of sensing MI as a performance metric can enhance the multi-target resolution capability and angle estimation accuracy.

Target Localization with Macro and Micro Base Stations Cooperative Sensing

Addressing the communication and sensing demands of sixth-generation (6G) mobile communication system, integrated sensing and communication (ISAC) has garnered traction in academia and industry. With the sensing limitation of single base station (BS), multi-BS cooperative sensing is regarded as a promising solution. The coexistence and overlapped coverage of macro BS (MBS) and micro BS (MiBS) are common in the development of 6G, making the cooperative sensing between MBS and MiBS feasible. Since MBS and MiBS work in low and high frequency bands, respectively, the challenges of MBS and MiBS cooperative sensing lie in the fusion method of the sensing information in high and low-frequency bands. To this end, this paper introduces a symbol-level fusion method and a grid-based three-dimensional discrete Fourier transform (3D-GDFT) algorithm to achieve precise localization of multiple targets with limited resources. Simulation results demonstrate that the proposed MBS and MiBS cooperative sensing scheme outperforms traditional single BS (MBS/MiBS) sensing scheme, showcasing superior sensing performance

Spectrum Sharing Policy in the Asia-Pacific Region

In this chapter, we investigate the spectrum measurement results in Asia-Pacific region. Then the spectrum sharing policy in the Asia-Pacific region is reviewed in details, where the national projects and strategies on spectrum refarming and spectrum sharing in China, Japan, Singapore, India, Korea and Australia are investigated. Then we introduce the spectrum sharing test-bed that is developed in China, which is a cognitive radio enabled TD-LTE test-bed utilizing TVWS. This chapter provides a brief introduction of the spectrum sharing mechanism and policy of Asia-Pacific region.

Deep Learning Based Multi-Node ISAC 4D Environmental Reconstruction with Uplink- Downlink Cooperation

Utilizing widely distributed communication nodes to achieve environmental reconstruction is one of the significant scenarios for Integrated Sensing and Communication (ISAC) and a crucial technology for 6G. To achieve this crucial functionality, we propose a deep learning based multi-node ISAC 4D environment reconstruction method with Uplink-Downlink (UL-DL) cooperation, which employs virtual aperture technology, Constant False Alarm Rate (CFAR) detection, and Mutiple Signal Classification (MUSIC) algorithm to maximize the sensing capabilities of single sensing nodes. Simultaneously, it introduces a cooperative environmental reconstruction scheme involving multi-node cooperation and Uplink-Downlink (UL-DL) cooperation to overcome the limitations of single-node sensing caused by occlusion and limited viewpoints. Furthermore, the deep learning models Attention Gate Gridding Residual Neural Network (AGGRNN) and Multi-View Sensing Fusion Network (MVSFNet) to enhance the density of sparsely reconstructed point clouds are proposed, aiming to restore as many original environmental details as possible while preserving the spatial structure of the point cloud. Additionally, we propose a multi-level fusion strategy incorporating both data-level and feature-level fusion to fully leverage the advantages of multi-node cooperation. Experimental results demonstrate that the environmental reconstruction performance of this method significantly outperforms other comparative method, enabling high-precision environmental reconstruction using ISAC system.

Deep Cooperation in ISAC System: Resource, Node and Infrastructure Perspectives

With the mobile communication system evolving into 6th-generation (6G), the Internet of Everything (IoE) is becoming reality, which connects human, big data and intelligent machines to support the intelligent decision making, reconfiguring the traditional industries and human life. The applications of IoE require not only pure communication capability, but also high-accuracy and large-scale sensing capability. With the emerging integrated sensing and communication (ISAC) technique, exploiting the mobile communication system with multi-domain resources, multiple network elements, and large-scale infrastructures to realize cooperative sensing is a crucial approach to satisfy the requirements of high-accuracy and large-scale sensing in IoE. In this article, the deep cooperation in ISAC system including three perspectives is investigated. In the microscopic perspective, namely, within a single node, the cooperation at the resource-level is performed to improve sensing accuracy by fusing the sensing information carried in the time-frequency-space-code multi-domain resources. In the mesoscopic perspective, the sensing accuracy could be improved through the cooperation of multiple nodes including Base Station (BS), User Equipment (UE), and Reconfigurable Intelligence Surface (RIS), etc. In the macroscopic perspective, the massive number of infrastructures from the same operator or different operators could perform cooperative sensing to extend the sensing coverage and improve the sensing continuity. This article may provide a deep and comprehensive view on the cooperative sensing in ISAC system to enhance the performance of sensing, supporting the applications of IoE.