Millimeter-wave (MMW) radar is one of the indispensable sensors for advanced driver assistance systems (ADASs) and autonomous driving. This paper adopts inter-pulse frequency agility (FA) to synthesize a broadband signal, achieving broadband imaging without increasing the sampling rate. Meanwhile, given that Doppler division multiple access (DDMA) has higher transmission efficiency than time division multiple access (TDMA) and is widely used in automotive radar, this paper proposes a signal transmission framework that integrates DDMA and FA. Simulation results demonstrate that the proposed DDMA FA multiple-input multiple-output (DDMA-FA-MIMO) processing architecture can yield 30 to 40 times more detection points for a single target compared to traditional methods, preliminarily achieving target contour imaging.

Steganography is a technology that discreetly embeds secret information into the redundant space of a carrier, enabling covert communication. As generative models continue to advance, steganography has evolved from traditional modification-based methods to generative steganography, which includes generative linguistic and image based forms. However, while large model agents are rapidly emerging, no method has exploited the stable redundant space in their action processes. Inspired by this insightful observation, we propose a steganographic method leveraging large model agents, employing their actions to conceal secret messages. In this paper, we introduce StegoAgent, a generative steganography framework based on graphical user interface (GUI) agents, which effectively demonstrates the remarkable potential and effectiveness of large model agent-based steganographic methods.

Recently, various privacy-preserving schemes have been proposed to resolve privacy issues in federated learning (FL). However, most of them ignore the fact that anomalous users holding low-quality data may reduce the accuracy of trained models. Although some existing works manage to solve this problem, they either lack privacy protection for users’ sensitive information or introduce a two-cloud model that is difficult to find in reality. A reliable and privacy-preserving FL scheme named reliable and privacy-preserving federated learning (RPPFL) based on a single-cloud model is proposed. Specifically, inspired by the truth discovery technique, we design an approach to identify the user’s reliability and thereby decrease the impact of anomalous users. In addition, an additively homomorphic cryptosystem is utilized to provide comprehensive privacy preservation (user’s local gradient privacy and reliability privacy). We give rigorous theoretical analysis to show the security of RPPFL. Based on open datasets, we conduct extensive experiments to demonstrate that RPPEL compares favorably with existing works in terms of efficiency and accuracy.

Latency sensitive services have attracted much attention lately and imposed stringent requirements on the access network design. Passive optical networks (PONs) provide a potential long-term solution for the underlying transport network supporting these services. This paper discusses latency limitations in PON and recent progress in PON standardization to improve latency. Experimental results of a low latency PON system are presented as a proof of concept.

Compared with conventional multiple-input multiple-output (MIMO), massive MIMO system with tens or even hundreds of antennas is able to give better performance in capacity and spectral efficiency, which is a promising technology for 5G. Considering this, massive MIMO has become a hot research topic all over the world. In this paper, the channel measurements and models of massive MIMO in recent years are summarized. Besides, the related 256 antenna elements with 200 MHz bandwidth at 3.5 GHz proposed by our team, the verification of rationality of the measurement method, and the spatial evolution of clusters in mobile scenario are provided.