To enhance the performance of non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC) systems in multi-user distributed scenarios, an improved Gaussian Mixture Model (GMM)-based user clustering algorithm is proposed. This algorithm is tailored for ISAC systems, significantly improving bandwidth reuse gains and reducing serial interference. First, using the Sum of Squared Errors (SSE), the algorithm reduces sensitivity to the initial cluster center locations, improving clustering accuracy. Then, direction weight factors are introduced based on the base station position and a penalty function involving users’ Euclidean distances and sensing power. Modifications to the EM algorithm in calculating posterior probabilities and updating the covariance matrix help align user clusters with the characteristics of NOMAISAC systems. This improves users’ interference resistance, lowers decoding difficulty, and optimizes the system’s sensing capabilities. Finally, a fractional programming (FP) approach addresses the non-convex joint beamforming design problem, enhancing power and channel gains and achieving co-optimizing sensing and communication signals. The simulation results show that, under the improved GMM user clustering algorithm and FP optimization, the NOMA-ISAC system improves user spectral efficiency by 4.3% and base station beam intensity by 5.4% compared to traditional ISAC systems.

This article proposes a distributed intelligent Coordinated Multi-Point Non-Orthogonal Multiple-Access (CoMP-NOMA) collaborative transmission model with the assistance of reconfigurable intelligent surfaces (RISs) to address the issues of poor communication quality, low fairness, and high system power consumption for edge users in multi-cellular networks. By analyzing the interaction mechanisms and influencing factors among RIS signal enhancement, NOMA user scheduling, and multi-point collaborative transmission, the model establishes RIS-enhanced edge user grouping and coordinates NOMA user clusters based on this. In the multi-cell RIS-assisted JT-CoMP NOMA downlink transmission, joint optimization of the power allocation (PA), user clustering (UC), and RIS phase-shift matrix design (PS) poses a challenging Mixed-Integer Non-Linear Programming (MINLP) problem. The original problem is decomposed by optimizing the formulas into joint sub-problems of PA, UC, and PA and PS, and solved using an alternating optimization approach. Simulation results demonstrate that the proposed scheme effectively reduces the system’s power consumption while significantly improving the system’s throughput and rates.

In this paper, a compressed adaptive image-sensing method based on an overcomplete ridgelet dictionary is proposed. Some low-complexity operations are designed to distinguish between smooth blocks and texture blocks in the compressed domain, and adaptive sampling is performed by assigning different sampling rates to different types of blocks. The efficient, sparse representation of images is achieved by using an overcomplete ridgelet dictionary; at the same time, a reasonable dictionary-partitioning method is designed, which effectively reduces the number of candidate dictionary atoms and greatly improves the speed of classification. Unlike existing methods, the proposed method does not rely on the original signal, and computation is simple, making it particularly suitable for scenarios where a device’s computing power is limited. At the same time, the proposed method can accurately identify smooth image blocks and more reasonably allocate sampling rates to obtain a reconstructed image with better quality. The experimental results show that our method’s image reconstruction quality is superior to that of existing ARCS methods and still maintains low computational complexity.

To obtain an excellent electromagnetic wave (EMW) absorption material, a strategy was proposed in this study with the aid of in-situ growth of carbon nanotubes (CNTs) on the surface of a metal–organic framework (MOF)-derived FeCoNiMnMg high-entropy alloy (HEA). The HEA@CNT composite was successfully prepared via a solvothermal method combined with a one-step pyrolysis process. With the pyrolysis temperature increasing from 600 °C to 800 °C, the length of CNTs grew from 200 nm to about 600 nm approximately, while the defect density of CNTs was enhanced. This structural evolution significantly improved the dielectric properties and impedance matching. Consequently, the sample prepared at 800 °C (HEA@CNT-800) exhibited outstanding microwave absorption performances, achieving a minimum reflection loss (RL_min) of −57.52 dB at a matched thickness of 2.3 mm and an effective absorption bandwidth (EAB) of 4.4 GHz at a thinner thickness of 1.9 mm. This work provides a novel perspective for designing high-performance MOF-derived absorption materials.

In existing UAV communication systems incorporating active reconfigurable intelligent surfaces (ARIS), hardware impairments (HIs) in transceivers and thermal noise from active units are frequently overlooked. This oversight leads to signal distortion at user terminals and excessive system power consumption. To address these challenges, this study proposes a solution to enhance signal transmission quality by jointly optimizing the dynamic topology of an ARIS and the average achievable rate (AAR) for users. Firstly, to mitigate inter-element interference in the ARIS, a hybrid genetic algorithm (HGA) is proposed. This algorithm integrates the global search capability of genetic algorithms with the local optimization efficiency of the tabu search algorithm (TSA) to iteratively derive the optimal dynamic topology matrix for the ARIS. Secondly, to maximize the AAR by increasing received signal power, fractional programming with quadratic transformation is combined with semidefinite relaxation and successive convex approximation to tackle the highly coupled multi-variable non-convex fractional programming problem. This approach transforms subproblems into single-variable convex optimizations. Finally, an alternating iterative method is employed to solve the convex subproblems, yielding a suboptimal solution. The simulation results demonstrate that the proposed UAV-ARIS dynamic topology optimization scheme improves the system AAR by 27–130% and energy efficiency by 19–32% compared with conventional schemes, while ensuring flexible deployment and high energy efficiency.