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.

Multi-Attribute Preferences Mining Method for Group Users with the Process of Noise Reduction

Traditional researches on user preferences mining mainly explore the user’s overall preferences on the project,but ignore that the fundamental motivation of user preferences comes from their attitudes on some attributes of the project.In addition,traditional researches seldom consider the typical preferences combination of group users,which may have influence on the personalized service for group users.To solve this problem,a method with noise reduction for group user preferences mining is proposed,which focuses on mining the multi-attribute preference tendency of group users.Firstly,both the availability of data and the noise interference on preferences mining are considered in the algorithm design.In the process of generating group user preferences,a new path is used to generate preference keywords so as to reduce the noise interference.Secondly,the Gibbs sampling algorithm is used to estimate the parameters of the model.Finally,using the user comment data of several online shopping websites as experimental objects,the method is used to mine the multi-attribute preferences of different groups.The proposed method is compared with other methods from three aspects of predictive ability,preference mining ability and preference topic similarity.Experimental results show that the method is significantly better thap other existing methods.

A novel similarity measurement approach considering intrinsic user groups in collaborative filtering

To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach constructs the taxonomy trees for each categorical attribute of users. Based on the taxonomy trees, the distance between numerical and categorical attributes is computed in a unified framework via a proper weight. Then, using the proposed distance method, the nave k-means cluster method is modified to compute the intrinsic user groups. Finally, the user group information is incorporated to improve the performance of traditional similarity measurement. A series of experiments are performed on a real world dataset, M ovie Lens. Results demonstrate that the proposed approach considerably outperforms the traditional approaches in the prediction accuracy in collaborative filtering.

Deep Learning Based User Grouping for FD-MIMO Systems Exploiting Statistical Channel State Information

The joint spatial division and multiplexing(JSDM)is a two-phase precoding scheme for massive multiple-input-multiple-output(MIMO)system under frequency division duplex(FDD)mode to reduce the amount of channel state information(CSI)feedback.To apply this scheme,users need to be partitioned into groups so that users in the same group have similar channel covariance eigenvectors while users in different groups have almost orthogonal eigenvectors.In this paper,taking the clustered user model into account,we consider the user grouping of JSDM for the downlink massive MIMO system with uniform planar antenna array(UPA)at base station(BS).A deep learning based user grouping algorithm is proposed to improve the efficiency of the user grouping process.The proposed grouping algorithm transfers the statistical CSI of all users into a picture,and utilizes the deep learning enabled objective detection model you look only once(YOLO)to divide the users into different groups rapidly.Simulation results show that the proposed user grouping scheme can achieve higher sum rate with less time delay.

Grouping pilot allocation scheme based on matching algorithm in massive MIMO system

Pilot allocation is one of the effective means to reduce pilot pollution in massive multiple-input multiple-output(MIMO)systems.The goal of this paper is to improve the uplink achievable sum rates of strong users,and ensure the quality of service(QoS)requirements of weak users at the same time,so that the sum rates of system can be improved.Combining with the technical advantage of pilot grouping,a low complexity pilot allocation scheme based on matching algorithm is proposed,which divides the users in the target cell into weak user group and strong user group,and adopts the minimum-maximum matching method to allocate pilots in weak user group.Through the introduction of Hungarian algorithm,a pilot allocation method is designed to ensure the fairness of the strong users.The simulation results show that,compared with the smart pilot allocation scheme,the pilot allocation scheme based on Hungarian algorithm,the pilot allocation scheme based on user grouping and the random pilot allocation scheme,the system performance of the proposed scheme has been effectively improved.

Research on User Pairing Techniques Based on NOMA in Cognitive Radio Networks

This paper outlined a Non-Orthogonal Multiple Access (NOMA) grouping transmission scheme for cognitive radio networks. To address the problems of small channel gain difference of the middle part users caused by the traditional far-near pairing algorithm, and the low transmission rate of the traditional Orthogonal Multiple Access (OMA) transmission, a joint pairing algorithm was proposed, which provided multiple pairing schemes according to the actual scene. Firstly, the secondary users were sorted according to their channel gain, and then different secondary user groups were divided, and the far-near pairing combined with (Uniform Channel Gain Difference (UCGD) algorithm was used to group the secondary users. After completing the user pairing, the power allocation problem was solved. Finally, the simulation data results showed that the proposed algorithm can effectively improve the system transmission rate.

Users’Participation in Community Forest Management:A Comparative Study

Local community participation in forest management is pivotal since they are familiar with the forest environment.In the successful management of community forestry(CF),both males and females along with the representation of poor and disadvantaged groups are of vital importance.This research compares the users’perception in community forest management(CFM)activities,and socio-economic variables influencing participation in studied community forestry user groups(CFUGs).Primary data were collected through reconnaissance surveys,interviewing key informants,focus group discussions,and household surveys.Secondary data were collected from the division forest office,CFUGs’operational plan(OP)and Constitution,internet,and authenticated websites.The chi-square(χ^(2))test was applied to test separately association variables like gender,caste,age class,education level,and wealth ranking with participation.Using ordered logit regression,the variables affecting participation in OP and constitution-making,Silvicultural activities,Forest products collection,and CF fund mobilization were quantified.Gender and Education were found to be the most promising factor influencing participation in Jagriti CFUG and Jhankrikhola CFUG respectively.In general,higher caste,older age,and rich people dominate the major decision-making activities.However,lower caste and poor people have been involved comparatively more in Forest product collection.

Joint resource allocation for hybrid NOMA-assisted MEC in 6G networks

Multi-access Edge Computing(MEC)is an essential technology for expanding computing power of mobile devices,which can combine the Non-Orthogonal Multiple Access(NOMA)in the power domain to multiplex signals to improve spectral efficiency.We study the integration of the MEC with the NOMA to improve the computation service for the Beyond Fifth-Generation(B5G)and the Sixth-Generation(6G)wireless networks.This paper aims to minimize the energy consumption of a hybrid NOMA-assisted MEC system.In a hybrid NOMA system,a user can offload its task during a time slot shared with another user by the NOMA,and then upload the remaining data during an exclusive time duration served by Orthogonal Multiple Access(OMA).The original energy minimization problem is non-convex.To efficiently solve it,we first assume that the user grouping is given,and focuses on the one group case.Then,a multilevel programming method is proposed to solve the non-convex problem by decomposing it into three subproblems,i.e.,power allocation,time slot scheduling,and offloading task assignment,which are solved optimally by carefully studying their convexity and monotonicity.The derived solution is optimal to the original problem by substituting the closed expressions obtained from those decomposed subproblems.Furthermore,we investigate the multi-user case,in which a close-to-optimal algorithm with lowcomplexity is proposed to form users into different groups with unique time slots.The simulation results verify the superior performance of the proposed scheme compared with some benchmarks,such as OMA and pure NOMA.

Resource allocation for sum-rate maximization in NOMA-based generalized spatial modulation

The Multiple-Input Multiple-Output(MIMO)Non-Orthogonal Multiple Access(NOMA)based on Spatial Modulation(SM-MIMO-NOMA)system has been proposed to achieve better spectral efficiency with reduced radio frequency chains comparing to the traditional MIMO-NOMA system.To improve the performance of SM-MIMO-NOMA systems,we extend them to generalized spatial modulation scenarios while maintaining moderate complexity and fairness.In this paper,system spectral efficiency and transmission quality improvements are proposed by investigating a sum-rate maximization resource allocation problem that is subject to the total transmitted power,user grouping,and resource block constraints.To solve this non-convex and difficult problem,a graph-based user grouping strategy is proposed initially to maximize the mutual gains of intragroup users.An auxiliary-variable approach is then adopted to transform the power allocation subproblem into a convex one.Simulation results demonstrate that the proposed algorithm has better performance in terms of bit error rate and sum rates.

Community Participation in Forest Resource Management in Nepal

Livelihood of the people in Nepal hills depends much upon forest resources in addition to farming as forest plays a critical role in the well being of the farming households where access to alternative sources, such as energy for cooking, nutrition for animals, materials for fertilizer and constructing materials for shelter, are limited. Thus, the well being of the people in the hills is directly affected by the management of these forest resources. This issue was addressed in this paper by examining the forest resource management practices and its effect on well being of rural people in two different stages in a village lying in the steep hill of Mahabharat Range in the southern hills of Kathmandu valley, Lalitpur District. The main ethnic/caste groups in the village are Brahmin/Chhetri (high Hindu caste), Magar/Tamang (Tibeto Burmans) and Kami (occupational caste: cobbler). Currently there are four community forest users groups, with mixed ethnic membership, organized to manage the forest resources. The endowments, weak institutional settings, before 1990 helped the Bhramins, Magars and Tamangs to get access into the private forest endowment, which made them easy to get access to the forest resources, mainly fuel wood, fodder and timber in 1990 and enhanced their well beings. But the socially backward Kami could not get benefit from the institutions that existed during that time and had less chance to enhance their well beings. After the set up of different endowments during late 1990s, i.e., hand over of forest management to usersgroupsin line with the concept of community forest, environment to use the forest resources became better for all the groups, along with the management of the forest. This enhanced the well beings of all the groups in the study village. However, the ability of Kami to use the forest resources to enhance their well beings was still lacking behind. The reason was partially due to the difference in endowments carried over from the endowments before 1990, and partly due to their occupational work and location of their settlements.

Rural coping and adaptation strategies for climate change by Himalayan communities in Nepal

Climate change has major impacts on the livelihoods of forest-dependent communities.The unpredictable weather conditions in rural Nepal have been attributed to a changing climate.This study explored the climate change adaptation and coping strategies that rural communities adopt for the conservation of natural resources and livelihoods in the mid-hills of Nepal.This paper explored major climatic hazards,assessed different coping and adaptation measures,and barrier faced to climate change adaptation based on perceptions by forest-dependent communities.We conducted focus group discussions,questionnaire surveys,and semistructured interviews with local communities and stakeholders.The results showed that rural communities had experienced significant impacts of climate change and variability.In response,they are practicing diverse coping and adaptation strategies,including the construction of bioengineering structures and planting different species that grow quickly and establish promptly.

A pilot allocation method for multi-cell multi-user massive MIMO system

Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output(MIMO)systems.This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency.The pilots of the terminals are allocated onebit orthogonal identifier to diminish the cell categories by the operation of exclusive OR(XOR).At the same time,the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm,and different pilot sequences are assigned to different groups.The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.

A joint optimization scheme of resource allocation in downlink NOMA with statistical channel state information

In a real communication scenario,it is very difficult to obtain the real-time channel state infor-mation(CSI)accurately,so the non-orthogonal multiple access(NOMA)system with statistical CSI has been researched.Aiming at the problem that the maximization of system sum rate cannot be solved directly,a step-by-step resource allocation optimization scheme based on machine learning is proposed.First,in order to achieve a trade-off between the system sum rate and user fairness,the system throughput formula is derived.Then,according to the combinatorial characteristics of the system throughput maximization problem,the original optimization problem is divided into two sub-problems,that are power allocation and user grouping.Finally,genetic algorithm is introduced to solve the sub-problem of power allocation,and hungarian algorithm is introduced to solve the sub-problem of user grouping.By comparing the ergodic data rate of NOMA users with statistical CSI and perfect CSI,the effectiveness of the statistical CSI sorting is verified.Compared with the orthogonal multiple access(OMA)scheme,the NOMA scheme with the fixed user grouping scheme and the random user grouping scheme,the system throughput performance of the proposed scheme is signifi-cantly improved.

Design and Implementation of RBAC Model Based on Graduate Education Management System

After analysis of the existing problems of traditional RBAC model, user group and resource domain are introduced to conduct finely granular extension of RBAC model. Extended model reduces the redundancy of roles, lowers the complexity of authorization management and enhances the flexibility and maintainability of users＇ authorization. It is well proved in its application in postgraduate student management system.

Enhancing Next-Item Recommendation Through Adaptive User Group Modeling

Session-based recommender systems are increasingly applied to next-item recommendations.However,existing approaches encode the session information of each user independently and do not consider the interrelationship between users.This work is based on the intuition that dynamic groups of like-minded users exist over time.By considering the impact of latent user groups,we can learn a user’s preference in a better way.To this end,we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user groups.Specifically,we utilize two network units to learn users’long and short-term sessions,respectively.Meanwhile,we employ two additional units to determine the affiliation of users with specific latent groups,followed by an aggregation of these latent group representations.Finally,user preference representations are shaped comprehensively by considering all these four aspects,based on an attention mechanism.Moreover,to avoid setting the number of groups manually,we further incorporate an adaptive learning unit to assess the necessity for creating a new group and learn the representation of emerging groups automatically.Extensive experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall,mean average precision(mAP),and area under curve(AUC)metrics.

Matching theory based user-grouping for indoor non-orthogonal multiple access visible light communication heterogeneous networks

This Letter proposes a model of indoor visible light communication(VLC)heterogeneous networks entirely based on LEDs with different specifications and applies non-orthogonal multiple access(NOMA)to it because of the narrow modulation bandwidth of LEDs.Moreover,a user-grouping scheme that is based on matching theory is proposed to improve the network achievable sum rate.Simulation results indicate that when each NOMA cluster contains 6 users,the proposed scheme has a 49.54%sum-rate enhancement compared with the traditional user-grouping scheme.As the number of users in each NOMA cluster increases,the proposed scheme performs better at the cost of computational complexity.

User grouping and optimization-based pilot scheduling for mitigating the pilot contamination in massive multi cell MIMO systems

Massive multiple-input multiple-output(massive MIMO)is a promising approach in wireless communication systems for providing improved link reliability and spectral effi-ciency and it helps several users.The main aim is to solve pilot contamination issue in massive MIMO systems;this research paper utilizes two approaches for reducing the contamination.This paper presents the user grouping approach based on sparse fuzzy C-means clustering(sparse FCM),which groups user parameters based on parameters such as large-scale fading factor,SINR,and user distance.Here,same pilot sequences are assigned to center users in which the impact of pilot contamination is limited,while the algorithm assigns orthogonal pilot sequences to the edge users that suffer severely from pilot contamination.Therefore,the proposed user grouping keeps away from the inappropriate grouping of users,enabling effective grouping even under the worst situations of the channel.Secondly,pilot scheduling is done based on elephant spider monkey optimization(ESMO),which is designed by integrating elephant herding optimization(EHO)into spider monkey optimization(SMO).The performance of pilot scheduling based on grouping-based ESMO is evaluated based on achievable rate and SINR.The proposed method achieves maximal achievable rate of 41.29 bps/Hz and maximal SINR of 124.31 dB.

A low-complexity multiple signal representation scheme in downlink OFDM-CDMA

OFDM-CDMA is an attractive technique for broadband wireless communication. However, the high peakto-average power ratio （PAPR） of the downlink signals, generated from multiple spread codes, remains a serious problem. In this paper, a low-complexity multiple signal representation （MSR） scheme is proposed to control the PAPR problem in downlink OFDM-CDMA systems. The proposed scheme generates multiple candidate signals by a novel user grouping scheme, which is without distortion and can provide more PAPR reduction than the conventional MSR schemes, such as partial transmit sequence （PTS） and selective mapping （SLM）. Furthermore, a low-complexity processing structure is developed using a novel joint spreading and inverse fast Fourier transform （S-IFFT） to simplify the generation of multiple candidate signals. Complexity analysis and numerical results show that the OFDM-CDMA systems employing the proposed scheme have better tradeoff between PAPR reduction and computational complexity, compared with the conventional MSR schemes.