ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication

Recent advances in wireless communication with the enormous demands of sensing ability have given rise to the integrated sensing and communication (ISAC) technology, among which passive sensing plays an important role. The main challenge of passive sensing is how to achieve high sensing performance in the condition of communication demodulation errors. In this paper, we propose an ISAC network (ISAC-NET) that combines passive sensing with communication signal detection by using model-driven deep learning (DL). Dissimilar to existing passive sensing algorithms that first demodulate the transmitted symbols and then obtain passive sensing results from the demodulated symbols, ISAC-NET obtains passive sensing results and communication demodulated symbols simultaneously. Different from the data-driven DL method, we adopt the block-by-block signal processing method that divides the ISAC-NET into the passive sensing module, signal detection module and channel reconstruction module. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for passive sensing and communication in wireless communications.Comment: 29 pages, 11 figure

Modeling and Design of the Communication Sensing and Control Coupled Closed-Loop Industrial System

With the advent of 5G era, factories are transitioning towards wireless networks to break free from the limitations of wired networks. In 5G-enabled factories, unmanned automatic devices such as automated guided vehicles and robotic arms complete production tasks cooperatively through the periodic control loops. In such loops, the sensing data is generated by sensors, and transmitted to the control center through uplink wireless communications. The corresponding control commands are generated and sent back to the devices through downlink wireless communications. Since wireless communications, sensing and control are tightly coupled, there are big challenges on the modeling and design of such closed-loop systems. In particular, existing theoretical tools of these functionalities have different modelings and underlying assumptions, which make it difficult for them to collaborate with each other. Therefore, in this paper, an analytical closed-loop model is proposed, where the performances and resources of communication, sensing and control are deeply related. To achieve the optimal control performance, a co-design of communication resource allocation and control method is proposed, inspired by the model predictive control algorithm. Numerical results are provided to demonstrate the relationships between the resources and control performances.Comment: 6 pages, 3 figures, received by GlobeCom 202

Intra-Pulse Frequency Coding Design for a High-Resolution Radar against Smart Noise Jamming

Smart noise jamming forms active jamming by intercepting, modulating, and forwarding radar signals into the radar receiver, which seriously affects the radar range recovery performance. In this paper, we propose a novel waveform design approach and an efficient range recovery method for high-resolution radar in the jamming scenario. Firstly, we propose an intra-pulse frequency-coded frequency-modulated continuous waveform (IPFC-FMCW), which contains multiple FMCW chips with different widths and frequencies, to combat the smart noise jamming. After the jamming suppression, the proposed waveform has a low sidelobe level, which is different from traditional FMCW signals for which the observations are periodically missing, resulting in high sidelobe levels. Then, to improve the range recovery performance of the waveform after jamming suppression, we optimize the range profile by designing the transmit waveform and then solve it by a simulated annealing algorithm. Next, based on the designed waveform, we derive the echo model after jamming suppression and propose a gridless compressed sensing (CS) method to recover the range of the targets. Compared with the existing waveforms and methods, the proposed waveform and the processing method achieve better range recovery performance in the jamming scenario. Numerical simulations are utilized to demonstrate the range recovery effectiveness of the proposed waveform and method in smart noise jamming

Radio map-based spectrum sharing for joint communication and sensing networks

Spectrum sharing among different wireless applications, e.g., communication and sensing (C&S), has become increasingly crucial in the upcoming sixth-generation (6G) era. To achieve effective coexistence, mutual interference should be mitigated. Motivated by this, we consider a joint communication and sensing (JCAS) network with distributed multiple-input and multiple-output (MIMO) configuration, and address the interference problem of spectrum sharing. A loose coordination regime is proposed which only requires C&S systems to exchange some position information. To do so, a site-specific database named the radio map is utilized to estimate the slowly-varying large-scale channel state information (CSI). On this basis, a joint power allocation scheme is designed to optimize the radar detection performance under a service constraint of the communication system. To tackle the complex nonlinear problem, the mathematical tools of the auxiliary function method and fraction programming are applied. An iterative algorithm is further proposed to solve this problem in a low-complexity way. Simulation results corroborate that the extrinsic information, i.e., position, is effective for geographically decoupling C&S interference. This will largely reduce the coordination complexity and cost in practice

OsBSK1-2, an Orthologous of AtBSK1, Is Involved in Rice Immunity

The brassinosteroid-SIGNALING KINASE (BSK) belongs to the receptor-like cytoplasmic kinase XII subgroup. BSK1 regulates development and immunity in Arabidopsis. However, the function of rice (Oryza sativa) BSK1 is largely unknown. Here, we report that the expression level of OsBSK1-2 is induced after a chitin or fagellin22 (flg22) treatment. Silencing OsBSK1-2 in rice results in compromised responses to chitin- or flg22-triggered immunity and resistance to Magnaporthe oryzae, but does not alter the plant’s architecture nor reduce plant responses to brassinosteroid signaling. Our study reveals that OsBSK1-2 functions as a major regulator in rice plant immunity