The performance of full-duplex (FD) cooperative non-orthogonal multiple access (C-NOMA) relaying systems in the presence of in-phase and quadrature-phase imbalance (IQI) and imperfect successive interference cancellation (ipSIC) is analyzed and evaluated. In particular, by considering two classes of users located far from and near to the information source, closed-form exact and analytical approximate expressions for the outage probability (OP) and ergodic sum rate (ESR), respectively, are derived. The accuracy of the analytical approach is verified by computer simulations which have shown that for both C-NOMA and equivalent OMA systems IQI has a deleterious effect on their OP and ESR performance in moderate and high signal-to-noise ratio (SNR) regions and the OP of C-NOMA is always slightly lower than that of the traditional OMA. Additionally, comparisons with equivalent half-duplex (HD) C-NOMA schemes have revealed that for the far user the FD C-NOMA scheme is less sensitive to ipSIC.

Land-use mapping (LUM) using high-spatial resolution remote sensing images (HSR-RSIs) is a challenging and crucial technology. However, due to the characteristics of HSR-RSIs, such as different image acquisition conditions and massive, detailed information, and performing LUM faces unique scientific challenges. With the emergence of new deep learning (DL) algorithms in recent years, methods to LUM with DL have achieved huge breakthroughs, which offer novel opportunities for the development of LUM for HSR-RSIs. This article aims to provide a thorough review of recent achievements in this field. Existing high spatial resolution datasets in the research of semantic segmentation and single-object segmentation are presented first. Next, we introduce several basic DL approaches that are frequently adopted for LUM. After reviewing DL-based LUM methods comprehensively, which highlights the contributions of researchers in the field of LUM for HSR-RSIs, we summarize these DL-based approaches based on two LUM criteria. Individually, the first one has supervised learning, semisupervised learning, or unsupervised learning, while another one is pixel-based or object-based. We then briefly review the fundamentals and the developments of the development of semantic segmentation and single-object segmentation. At last, quantitative results that experiment on the dataset of ISPRS Vaihingen and ISPRS Potsdam are given for several representative models such as fully convolutional network (FCN) and U-Net, following up with a comparison and discussion of the results.

The advent of 6G communication systems has enabled, through integrated sensing and communications (ISAC), the vision of the intelligent connection of everything. Since such ISAC systems will be utilizing the sub-6-GHz and millimeter-wave ­(mm-wave) bands, appropriate hybrid, i.e., sensing and communication channel models, are required for their proper operation. This article first presents a comprehensive survey of channel models used in ISAC applications viewed from different modeling perspectives by identifying the challenges encountered in these modeling approaches. A novel hybrid approach for ISAC channel modeling is then introduced wherein the modeling ­methodology is partitioned into three elements: targets, clutters, and interferences. The advantage of such a flexible modeling approach using these three elements enhances the proposed model’s ability to effectively deal with the specific sensing and communication operational requirements. Finally, further performance enhancement methods, based upon real-time 3D environment reconstruction techniques as well as artificial intelligence (AI), are proposed and discussed.

Federated learning (FL) is a promising decentralized deep learning technique that allows users to collaboratively update models without sharing their own data. However, due to its decentralized nature, no one can monitor workers’ behavior, and they may thus deviate protocols (e.g., participating without updating any models). To solve this problem, many researchers have proposed blockchain-enabled FL to reward workers (or users) with cryptocurrencies to encourage workers to follow the protocols. However, there is a lack of theoretical discussions concerning how such rewards impact workers’ behavior and how much should be given to workers. In this paper, we propose a mechanism-design-oriented FL protocol on a public blockchain network. Mechanism design (MD) is often used to make a rule intended to achieve a specific goal. With MD in mind, we introduce the concept of competition into blockchain-based FL so that only workers who have contributed well can obtain rewards, which naturally prevents workers from deviating from the protocol. We then mathematically answer the following questions with contest theory, a novel field of study in economics: i) What behavior will workers take?; ii) how much effort should workers exert to maximize their profits?; iii) how many workers should be rewarded?; and iv) what is the best proportion for reward distribution?.

This paper investigates the spectral efficiency (SE) of an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS) aided cell-free massive multiple-input multiple-output (CF-mMIMO) system under correlated Rayleigh fading. In order to improve the system quality-of-service (QoS) and suppress the pilot contamination, fractional power control and an appropriate pilot assignment are adopted. Furthermore, a large-scale fading decoding (LSFD) method is introduced, based on which the analytical SE expression for the considered system is derived. Numerical and simulation results show that the adopted LSFD detection leads to higher SE than maximal ratio combining does, and as compared to an equivalent active RIS scheme, aSTAR-RIS significantly improves the SE of the CF-mMIMO system.

Accurate identification and segmentation of individual tree points are crucial for assessing forest spatial distribution, understanding tree growth and structure, and managing forest resources. Traditional methods based on Canopy Height Models (CHM) are simple yet prone to over- and/or under-segmentation. To deal with this problem, this paper introduces a novel approach that combines marker-controlled watershed segmentation with a spectral clustering algorithm. Initially, we determined the local maxima within a series of variable windows according to the lower bound of the prediction interval of the regression equation between tree crown radius and tree height to preliminarily segment individual trees. Subsequently, using this geometric shape analysis method, the under-segmented trees were identified. For these trees, vertical tree crown profile analysis was performed in multiple directions to detect potential treetops which were then considered as inputs for spectral clustering optimization. Our experiments across six plots showed that our method markedly surpasses traditional approaches, achieving an average Recall of 0.854, a Precision of 0.937, and an F1-score of 0.892.