Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

Hybrid Block Diagonalization Precoding for Multi-User Weighted Sum-Rate Maximization

This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

S^(2)ANet:Combining local spectral and spatial point grouping for point cloud processing

Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider only the spatial domain in the feature extraction process.Methods In this paper,we propose a spectral and spatial aggregation convolutional network(S^(2)ANet),which combines spectral and spatial features for point cloud processing.First,we calculate the local frequency of the point cloud in the spectral domain.Then,we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency.We simultaneously extract the local features in the spatial domain to supplement the final features.Results S^(2)ANet was applied in several point cloud analysis tasks;it achieved stateof-the-art classification accuracies of 93.8%,88.0%,and 83.1%on the ModelNet40,ShapeNetCore,and ScanObjectNN datasets,respectively.For indoor scene segmentation,training and testing were performed on the S3DIS dataset,and the mean intersection over union was 62.4%.Conclusions The proposed S^(2)ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks.

High mobility group box 1 in the central nervous system:regeneration hidden beneath inflammation

High-mobility group box 1 was first discovered in the calf thymus as a DNA-binding nuclear protein and has been widely studied in diverse fields,including neurology and neuroscience.High-mobility group box 1 in the extracellular space functions as a pro-inflammatory damage-associated molecular pattern,which has been proven to play an important role in a wide variety of central nervous system disorders such as ischemic stroke,Alzheimer’s disease,frontotemporal dementia,Parkinson’s disease,multiple sclerosis,epilepsy,and traumatic brain injury.Several drugs that inhibit high-mobility group box 1 as a damage-associated molecular pattern,such as glycyrrhizin,ethyl pyruvate,and neutralizing anti-high-mobility group box 1 antibodies,are commonly used to target high-mobility group box 1 activity in central nervous system disorders.Although it is commonly known for its detrimental inflammatory effect,high-mobility group box 1 has also been shown to have beneficial pro-regenerative roles in central nervous system disorders.In this narrative review,we provide a brief summary of the history of high-mobility group box 1 research and its characterization as a damage-associated molecular pattern,its downstream receptors,and intracellular signaling pathways,how high-mobility group box 1 exerts the repair-favoring roles in general and in the central nervous system,and clues on how to differentiate the pro-regenerative from the pro-inflammatory role.Research targeting high-mobility group box 1 in the central nervous system may benefit from differentiating between the two functions rather than overall suppression of high-mobility group box 1.

Closed BER expression of multi-user DS UWB systemsin indoor fading channel

The bit error rate （BER） performance of multi-user direct spreading bi-phase shift keying （DSBPSK） direct impulse ultra wideband （UWB） systems is analyzed and simulated based on a statistical indoor multi-path fading channel model. The BER of the system is theoretically derived and given in closed form, which is expressed in terms of channel parameters and system parameters such as pulse width parameter, pulse repeat period, user number and pulse waveform. With this BER expression, the effect of these parameters on the system performance can be evaluated in a uniform way. Simulation results well match the theory numerical results, and prove that the multi-access interference （MAI） of DS-BPSK UWB is a normal distribution.

Adaptive resource allocation in downlink multi-user MC-CDMA systems

The joint channel and power allocation in the downlink transmission of multi-user multi-carrier code division multiple access（MC-CDMA） systems are investigated and the throughput maximization problem is considered as a mixed integer optimization problem. For simplicity of analysis, the problem is divided into two less complex sub-problems： power allocation and channel allocation, which can be solved by a suboptimal adaptive power allocation （APA）algorithm and an optimal adaptive channel allocation （ACA） algorithm, respectively. By combining APA and ACA algorithms, an adaptive channel and power allocation scheme is proposed. The numerical results show that the proposed APA algorithm is more suitable for MC-CDMA systems than the conventional equal power allocation algorithm, and that the proposed channel and power allocation scheme can significantly improve the system throughout performance.

Image quality assessment based on perceptual grouping

To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.

金融机构ESG投资的“漂绿”与“反漂绿”——基于DWSGroup的案例分析

深化绿色金融发展、助力经济绿色发展,是实现“双碳”目标的重要途径,特别是在ESG理念盛行的今天,金融机构的ESG投资行为因其深远影响而颇受投资者青睐,但层出不穷的“漂绿”现象也牢牢制约着绿色金融的发展,制约着ESG投资的发展。西方国家作为绿色金融发展先驱,从信息披露、制度监管、实践探索等方面对“漂绿”现象进行了研究和治理。本文在学习其先进经验的基础上,基于舞弊三角理论对DWSGroup案例进行分析,从压力、机会和自我合理化因素角度对金融机构ESG投资“漂绿”行为的动因进行分析,并据此提出我国“反漂绿”体系框架,以期为绿色金融的平稳发展和“双碳”目标的实现作出贡献。

Optimal subcarrier allocation for cognitive radio with multi-user OFDM and distributed antenna

The subcarrier allocation problem in cognitive radio（CR）networks with multi-user orthogonal frequency-division multiplexing（OFDM）and distributed antenna is analyzed and modeled for the flat fading channel and the frequency selective channel,where the constraint on the secondary user（SU）to protect the primary user（PU）is that the total throughput of each PU must be above the given threshold instead of the ＂interference temperature＂.According to the features of different types of channels,the optimal subcarrier allocation schemes are proposed to pursue efficiency（or maximal throughput）,using the branch and bound algorithm and the 0-1 implicit enumeration algorithm.Furthermore,considering the tradeoff between efficiency and fairness,the optimal subcarrier allocation schemes with fairness are proposed in different fading channels,using the pegging algorithm.Extensive simulation results illustrate the significant performance improvement of the proposed subcarrier allocation schemes compared with the existing ones in different scenarios.

Relationship between CT grouping and complications of liver cirrhosis

BACKGROUND: Imaging examination is important for hepatic cirrhosis. But the relationship between magnetic resonance (MR), computed tomography (CT) or ultrasound findings and pathological groups, degree, or reserve function of the cirrhotic liver is not clear. In this study, we investigated the relationship between the CT groupings of liver cirrhosis and its complications and clinical conditions. METHODS: The CT findings in 357 patients with liver cirrhosis were grouped. The complications were analyzed, included splenomegaly, varicose collateral veins, ascites, pleurorrhea, primary liver carcinoma, gallbladder stone, etc. Blood routine (BRt), and serum usea nitrogen (SUN), creatinine and uric acid were measured and hypersplenia and liver-kidney syndrome were estimated. RESULTS: Three hundred and fifty-seven patients with cirrhosis were divided into homogeneous group (87 patients, 24.4%), segmental group (41, 11.5%), and nodal group (229, 64.2%). The grade of spleen enlargement in the segmental and the nodal groups was significantly greater than that in the homogeneous group (P <0. 01 and P<0.001). The patients with varices were shown in a descending order in the segmental group (70.7%), the nodal group (17.0%) and the homogeneous group (2.3%), respectively. Significant difference was observed among the 3 groups ( P < 0.001). Ascites was seen in 182 patients (79.5%) of the nodal group, in 11 patients (26.8%) of the segmental group and in 9 patients ( 10.3%) of the homogeneous group (P<0.001). Sixty-eight patients (29.7%) in the nodal group had primary liver carcinoma and 1 (2.4%) in the segmental group and 5 (5.8%) in the homogeneous group (P<0.001). The number of patients with decreased concentration of hematoglobin in the nodal group was more than that in the homogeneous group ( P < 0. 001). The mean values of hematoglobin and platelet in the nodal group and the segmental group were significantly lower than those in the homogeneous group ( P < 0. 05 ). The number of patients with increased concentration of SUN in the nodal group and the segmental group was more than that in the homogeneous group (P<0.005). The concentration of SUN in the nodal group was significantly higher than that in the homogeneous group (P <0.002). CONCLUSION: There is a close relationship between the grouping of liver cirrhosis by CT findings and complications caused by the cirrhosis. The grouping is significant for estimating clinical conditions.

Grouping separation of mixed rare earths from their coexisting aqueous solutions by liquid-column elution

A new approach was proposed for grouping separation of 14 lanthanide rare-earth ions from their coexisting mixed aqueous solutions, by performing liquid-column elution using the aqueous solution containing 14 lanthanide rare-earth ions as the stationary phase and the dispersed organic oil droplets containing P507 extractant as the mobile phase. It was revealed that 14 lanthanide rare-earth ions could be separated into four groups, according to the lanthanide tetrad effect, respectively eluting out from the liquid column at different time in a certain order. Various effects including the saponification degree of P507, the concentration of P507 in organic phase, the length and inner diameter of the extraction column on the performance of grouping separation of rare-earth ions were discussed. The changes of the mass transfer coefficients were also investigated. The separation efficiency of the four groups of rareearth elements(REEs) was evaluated based on the elution resolution, Rs, of the elution peaks of La(Ⅲ),Gd(Ⅲ), Ho(Ⅲ) and Lu(Ⅲ), the four representative elements respectively from each of the four groups of REEs. Experimental results demonstrated that the separation of REEs by liquid-column elution mainly depended on the competitive adsorption of different rare-earth groups onto the surface of ascending P507 oil droplets. The affinity of different rare-earth groups with P507 extractant and a limited adsorption capacity of P507 molecules at the surface of the oil droplets ascending in liquid column play the important role. The present work highlights a promising technique for grouping separation of multiple lanthanide elements co-existing complex systems.

Grouping response method for equivalent static wind loads based on a modified LRC method

Wind loading is one of the most important loads for controlling the design of large-span roof structures. Equivalent static wind loads, which can generally aim at determining a specific response, are widely used by structural designers. A method for equivalent static wind loads applicable to multi-responses is proposed in this paper. A modified load- response-correlation （LRC） method corresponding to a particular peak response is presented, and the similarity algorithm implemented for the group response is described. The main idea of the algorithm is that two responses can be put into one group if the value of one response is close to that of the other response, when the structure is subjected to equivalent static wind loads aiming at the other response. Based on the modified LRC, the grouping response method is put forward to construct equivalent static wind loading. This technique can simultaneously reproduce peak responses for some grouped responses. To verify its computational accuracy, the method is applied to an actual large-span roof structure. Calculation results show that when the similarity of responses in the same group is high, equivalent static wind loads with high accuracy and reasonable magnitude of equivalent static wind distribution can be achieved.

Multi-User Connection Performance Assessment of NOMA Schemes for Beyond 5G

Non-orthogonal multiple access(NOMA)is proved to be useful to satisfy the requirements of beyond 5th generation such as massive multi-user connection.Here we compare the performances of two NOMA schemes:low code rate spreading(LCRS)scheme and interleaver division multiple access(IDMA)scheme.It is found that LCRS is superior to IDMA when number of users is small due to coding gain achieved.While IDMA is preferred when number of users is high because repetition applied in IDMA can suppress multi-user interference effectively.And interleaver is important in IDMA for randomizing the interference.Also,this paper evaluates the impact of channel decoder.It is observed that Log-MAP decoder has much better performance than that of Max-Log-MAP when number of users is large.Thus,it is recommended to use Log-MAP decoder in NOMA in high user overloading case.We also compared the performance of NOMA by using different type of channel codes.We find that NOMA using specific convolutional code has a better performance than that of using specific LDPC code when number of users is high.

THE WAVE GROUPING EFFECT ON WAVE FORCES ON A VERTICAL BREAKWATER

Linear wave theory and Longuet-Higgins and Steward’s (1964) group-induced second-order longweve (GSLW) theory ware used in this study on the grouping effect on wave forces acting on a verticalbreakwater. The calculated variance of total wave pressure on the vertical breakwater was closer tothe measured value if the wave grouping effect was considered.

Molecular Grouping of Grateloupia Tissues Collected Along Chinese Coast and Microsatellite Diversity Analysis of G. asiatica

Genus Grateloupia is one of the most speciose genera in family Halymeniales. It is also one of the sources for natural materials, food and medicine. With different environments, Grateloupia change their morphological characteristics, making their morphological identification very difficult. In addition, few of the species diversity in this genus has been described before. In this study, phylogenetic analysis based on rbc L gene sequence was employed to group Grateloupia collected from three locations along Chinese coast. The microsatellites were also used to evaluate their genetic diversity. In total, the tissue parts of 6 putative species were collected from G. asiatica, G. livida, G. lanceolate, G. catenata, G. turuturu and G. filicina. In order to evaluate their genetic diversity and then conserve them better, 40 microsatellites available for Grateloupia were used to evaluate their genetic diversity, and 11 microsatellites were found to be applicable to determine the genetic diversity of G. asiatica. It was found that the genetic diversity of G. asiatica around Qingdao was very rich. We suggested that the species of genus Grateloupia should be identified based on rbc L phylogenetic analysis before the diversity evaluation with microsatellites. The microsatellites should be developed for each species of Grateloupia so that their genetic diversity can be evaluated appropriately.

Complex field network-coded cooperation based on multi-user detection in wireless networks

Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation（CFNCC） scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation（CC） scheme and network-coded cooperation（NCC） scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability（SEP） performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio（SNR） values.

Sensors Grouping Model for Wireless Sensor Network

The grouping of sensors is a calculation method for partitioning the wireless sensor network into groups, each group consisting of a collection of sensors. A sensor can be an element of multiple groups. In the present paper, we will show a model to divide the wireless sensor network sensors into groups. These groups could communicate and work together in a cooperative way in order to save the time of routing and energy of WSN. In addition, we will present a way to show how to organize the sensors in groups and provide a combinatorial analysis of some issues related to the performance of the network.