Frontiers of Information Technology & Electronic Engineering最新文献

Reconfigurable intelligent surface (RIS) is widely accepted as a potential technology to assist in communication between base stations (BSs) and users in edge areas. We study the energy efficiency of a RIS-assisted multi-cell communication system with a realistic RIS power consumption model. With the goal of maximizing the energy efficiency of the system, we optimize the transmit beamforming vectors at the BS and the RIS phase shift matrix by a proposed alternative optimization algorithm. First, the transmit beamforming vector is optimized by solving the transformed weighted minimum mean square error (WMMSE) problem. Subsequently, to solve the inconvenience incurred by the discrete relationship between the RIS reflecting unit power consumption and its discrete phase shift, we use a continuous function to approximate their relationship. With this approximation, we can use the majorization minimization (MM) technique to optimize the continuous RIS phase shifts, and then quantize the obtained phase shifts to discrete ones. Simulation results demonstrate that the energy efficiency of the system is effectively optimized by the proposed algorithm.

This research investigates the digital-to-analog converter (DAC) free architecture for the digital reconfigurable intelligent surface (RIS) system, where the transmission lines are implemented for reflection coefficient (RC) control to reduce power consumption. In the proposed architecture, the radio frequency (RF) switch based phase shifter is considered. By using a single-pole four-throw (SP4T) switch to simultaneously control the RCs of a group of elements, a 2-bit phase shifter is realized for passive beam steering. A novel modulation scheme is developed to explore the cost effectiveness, which approaches the performance of traditional quadrature amplitude modulation (QAM). Specifically, to overcome the limitation of the phase shift bits, joint frequency-shift and phase-rotation operations are applied to the constellation points. The simulation and experimental results demonstrate that the proposed architecture is capable of providing an ideal transmission performance. Moreover, 64- and 256-QAM modulation schemes could be implemented by expanding the elements and phase bits.

Due to the openness of the wireless propagation environment, wireless networks are highly susceptible to malicious jamming, which significantly impacts their legitimate communication performance. This study investigates a reconfigurable intelligent surface (RIS) assisted anti-jamming communication system. Specifically, the objective is to enhance the system’s anti-jamming performance by optimizing the transmitting power of the base station and the passive beamforming of the RIS. Taking into account the dynamic and unpredictable nature of a smart jammer, the problem of joint optimization of transmitting power and RIS reflection coefficients is modeled as a Markov decision process (MDP). To tackle the complex and coupled decision problem, we propose a learning framework based on the double deep Q-network (DDQN) to improve the system achievable rate and energy efficiency. Unlike most power-domain jamming mitigation methods that require information on the jamming power, the proposed DDQN algorithm is better able to adapt to dynamic and unknown environments without relying on the prior information about jamming power. Finally, simulation results demonstrate that the proposed algorithm outperforms multi-armed bandit (MAB) and deep Q-network (DQN) schemes in terms of the anti-jamming performance and energy efficiency.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been attracting significant attention in both academia and industry for their advantages of achieving 360° coverage and enhanced degrees-of-freedom. This article first identifies the fundamentals of STAR-RIS, by discussing the hardware models, channel models, and signal models. Then, three representative categorizing approaches for STAR-RISs are introduced from the phase-shift, directional, and energy consumption perspectives. Furthermore, the beamforming design of STAR-RISs is investigated for both independent and coupled phase-shift cases. As a recent advance, a general optimization framework, which has high compatibility and provable optimality regardless of the application scenarios, is proposed. As a further advance, several promising applications are discussed to demonstrate the potential benefits of applying STAR-RISs in sixth-generation wireless communication. Lastly, a few future directions and research opportunities are highlighted.

Abstract

Artificial intelligence generated content (AIGC) has emerged as an indispensable tool for producing large-scale content in various forms, such as images, thanks to the significant role that AI plays in imitation and production. However, interpretability and controllability remain challenges. Existing AI methods often face challenges in producing images that are both flexible and controllable while considering causal relationships within the images. To address this issue, we have developed a novel method for causal controllable image generation (CCIG) that combines causal representation learning with bi-directional generative adversarial networks (GANs). This approach enables humans to control image attributes while considering the rationality and interpretability of the generated images and also allows for the generation of counterfactual images. The key of our approach, CCIG, lies in the use of a causal structure learning module to learn the causal relationships between image attributes and joint optimization with the encoder, generator, and joint discriminator in the image generation module. By doing so, we can learn causal representations in image’s latent space and use causal intervention operations to control image generation. We conduct extensive experiments on a real-world dataset, CelebA. The experimental results illustrate the effectiveness of CCIG.

Abstract

Diffusion models are effective purification methods, where the noises or adversarial attacks are removed using generative approaches before pre-existing classifiers conducting classification tasks. However, the efficiency of diffusion models is still a concern, and existing solutions are based on knowledge distillation which can jeopardize the generation quality because of the small number of generation steps. Hence, we propose TendiffPure as a tensorized and compressed diffusion model for purification. Unlike the knowledge distillation methods, we directly compress U-Nets as backbones of diffusion models using tensor-train decomposition, which reduces the number of parameters and captures more spatial information in multi-dimensional data such as images. The space complexity is reduced from O(N²) to O(NR²) with R ≤ 4 as the tensor-train rank and N as the number of channels. Experimental results show that TendiffPure can more efficiently obtain high-quality purification results and outperforms the baseline purification methods on CIFAR-10, Fashion-MNIST, and MNIST datasets for two noises and one adversarial attack.

Abstract

The rise of artificial intelligence generated content (AIGC) has been remarkable in the language and image fields, but artificial intelligence (AI) generated three-dimensional (3D) models are still under-explored due to their complex nature and lack of training data. The conventional approach of creating 3D content through computer-aided design (CAD) is labor-intensive and requires expertise, making it challenging for novice users. To address this issue, we propose a sketch-based 3D modeling approach, Deep3DSketch-im, which uses a single freehand sketch for modeling. This is a challenging task due to the sparsity and ambiguity. Deep3DSketch-im uses a novel data representation called the signed distance field (SDF) to improve the sketch-to-3D model process by incorporating an implicit continuous field instead of voxel or points, and a specially designed neural network that can capture point and local features. Extensive experiments are conducted to demonstrate the effectiveness of the approach, achieving state-of-the-art (SOTA) performance on both synthetic and real datasets. Additionally, users show more satisfaction with results generated by Deep3DSketch-im, as reported in a user study. We believe that Deep3DSketch-im has the potential to revolutionize the process of 3D modeling by providing an intuitive and easy-to-use solution for novice users.

This article addresses the secure finite-time tracking problem via event-triggered command-filtered control for nonlinear time-delay cyber physical systems (CPSs) subject to cyber attacks. Under the attack circumstance, the output and state information of CPSs is unavailable for the feedback design, and the classical coordinate conversion of the iterative process is incompetent in relation to the tracking task. To solve this, a new coordinate conversion is proposed by considering the attack gains and the reference signal simultaneously. By employing the transformed variables, a modified fractional-order command-filtered signal is incorporated to overcome the complexity explosion issue, and the Nussbaum function is used to tackle the varying attack gains. By systematically constructing the Lyapunov–Krasovskii functional, an adaptive event-triggered mechanism is presented in detail, with which the communication resources are greatly saved, and the finite-time tracking of CPSs under cyber attacks is guaranteed. Finally, an example demonstrates the effectiveness.

Abstract

This paper discusses a strategy for estimating Hammerstein nonlinear systems in the presence of measurement noises for industrial control by applying filtering and recursive approaches. The proposed Hammerstein nonlinear systems are made up of a neural fuzzy network (NFN) and a linear state`-space model. The estimation of parameters for Hammerstein systems can be achieved by employing hybrid signals, which consist of step signals and random signals. First, based on the characteristic that step signals do not excite static nonlinear systems, that is, the intermediate variable of the Hammerstein system is a step signal with different amplitudes from the input, the unknown intermediate variables can be replaced by inputs, solving the problem of unmeasurable intermediate variable information. In the presence of step signals, the parameters of the state-space model are estimated using the recursive extended least squares (RELS) algorithm. Moreover, to effectively deal with the interference of measurement noises, a data filtering technique is introduced, and the filtering-based RELS is formulated for estimating the NFN by employing random signals. Finally, according to the structure of the Hammerstein system, the control system is designed by eliminating the nonlinear block so that the generated system is approximately equivalent to a linear system, and it can then be easily controlled by applying a linear controller. The effectiveness and feasibility of the developed identification and control strategy are demonstrated using two industrial simulation cases.

Data imputation is an essential pre-processing task for data governance, aimed at filling in incomplete data. However, conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data, and they fail to achieve the best balance between accuracy and efficiency. In this paper, we present a novel visual analysis approach for data imputation. We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables. Then, we perform the initial imputation of incomplete data using correlated data entries from other tables. Additionally, we develop a visual analysis system to refine data imputation candidates. Our interactive system combines the multi-party data imputation approach with expert knowledge, allowing for a better understanding of the relational structure of the data. This significantly enhances the accuracy and efficiency of data imputation, thereby enhancing the quality of data governance and the intrinsic value of data assets. Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using their domain knowledge.