User Clustering Research Articles

Cluster based resource management using H-NOMA in heterogeneous networks beyond 5G

High throughput, secure transmission, and minimum interference are required in beyond-fifth-generation (B5G) networks. For this purpose, a mix of hybrid non-orthogonal multiple access (H-NOMA) and mobile user clustering (MUC) in a heterogeneous network (HetNet) can be explored. In this paper, we propose sum-secrecy throughput (SST) maximization problem based on a novel MUC-assisted downlink (DL) H-NOMA strategy in the presence of an eavesdropper in HetNet. We compare the performance of the proposed strategy for HetNet and a high-powered base station-only (HPS-only) network. We also compare the performance of the proposed strategy with the existing orthogonal multiple access only (OMA-only) and non-orthogonal multiple access only (NOMA-only) strategies. We have used an ϵ-optimal outer approximation algorithm (OAA) to solve the formulated problem based on the proposed strategy. We have evaluated the performance of the proposed strategy by considering key performance indicators (KPIs), i.e., the mobile user (MU)’s admission in the cluster, MU’s association with the HPS or low-powered base station (LPS), power allocation (PA) to MUs, quality of service (QoS), fairness in association with HPS or LPS, and SST. Extensive simulation results considering KPIs reveal that the proposed strategy performs better (1) in HetNet, (2) for HetNet compared to the HPS-only network, and (3) compared to the existing OMA-only and NOMA-only strategies in HetNet. Additionally, the proposed system model is compared with Amin et al. (2021) and Fu et al. (2022) in terms of SST and the simulation results reveal that the proposed system model’s performance is superior compared to Amin et al. (2021) and Fu et al. (2022) regarding SST.

Why Do People Share Political Information and Misinformation Online? Developing a Bottom-Up Descriptive Framework

Social media users are key actors in the spreading of misleading or incorrect information. To develop an integrative parsimonious summary of social media users’ own accounts of motives for sharing political information, we conducted: (1) a literature review of motives for personally sharing false information as reported by social media users and (2) qualitative research concerning these motives using an innovative, ecologically valid method. Based on our findings, we developed a pool of items evaluating social media users’ motives for sharing false political information, which we then tested and analyzed the dimensionality of in (3) a pre-registered questionnaire-based study to identify key clusters of users’ own accounts of motives for sharing both true and false political information. The current findings show that there are distinct sets of motives people report for their misinformation sharing behavior: prosocial activism, attack or manipulation of others, entertainment, awareness, political self-expression, and fighting false information. Also, these sets of motives are associated with variables known to predict sharing misinformation, and some of these sets predict social media users’ self-reports of having shared misinformation in the past. Our findings highlight and elaborate on users’ motives that reflect a concern with “making things better” and acting in a manner that is beneficial to society as a whole, and suggest that different interventions may be required to combat misinformation sharing driven by different motives. A potential set of 18 items that could be used in questionnaires measuring motivations for sharing political news online is described.

Unsupervised Machine Learning-Based User Clustering in THz-NOMA Systems

In this paper, different unsupervised machine learning (ML)-based user clustering algorithms, including K-Means, agglomerative hierarchical clustering (AHC), and density-based spatial clustering of applications with noise (DBSCAN) are applied in non-orthogonal multiple access (NOMA) assisted terahertz (THz) networks. The key contribution of this paper is to design ML-based approaches to ensure that the secondary users can be clustered without knowing the number of clusters and degrading the performance of the primary users. The studies carried out in the paper show that the proposed schemes based on AHC and DBSCAN can achieve superior performance on system throughput and connectivity compared to the traditional clustering strategy, i.e., K-means, where the number of clusters is determined in an adaptive and automatic manner.

TRASH TALK: WHO USES WHICH REUSABLE PRODUCT? USER INSIGHTS AND DESIGN OPPORTUNITIES FOR SINGLE-USE ALTERNATIVES

AbstractSingle-use products often end up in the environment as waste, threatening ecosystems and human life. This indicates a need to transition towards sustainable reuse routines. In this study, we investigate to what extent reusable products are already established in society and what design properties users require. We compare the results between user clusters and countries. To create a meaningful list of reusable products to investigate, we distinguish four categories based on typical barriers by means of focus groups (n=3) and interviews (n=32). Next, we did a survey (n=3000) in three countries (Belgium, Russia, U.S.) to define user clusters and investigate product usage and design requirements. Most established products are hard-material products such as lunch boxes, while intimate hygiene products, such as menstrual cups, are not established yet. Multifunctionality and compactness are the most indicated product requirements. There are significant differences between countries and clusters for both research questions. We conclude that different types of users have different needs: while a sharing system might work for one group, a customizable option would be more suitable for others.

Exploiting User Clustering and Fixed Power Allocation for Multi-Antenna UAV-Assisted IoT Systems.

Internet of Things (IoT) systems cooperative with unmanned aerial vehicles (UAVs) have been put into use for more than ten years, from transportation to military surveillance, and they have been shown to be worthy of inclusion in the next wireless protocols. Therefore, this paper studies user clustering and the fixed power allocation approach by placing multi-antenna UAV-mounted relays for extended coverage areas and achieving improved performance for IoT devices. In particular, the system enables UAV-mounted relays with multiple antennas together with non-orthogonal multiple access (NOMA) to provide a potential way to enhance transmission reliability. We presented two cases of multi-antenna UAVs such as maximum ratio transmission and the best selection to highlight the benefits of the antenna-selections approach with low-cost design. In addition, the base station managed its IoT devices in practical scenarios with and without direct links. For two cases, we derive closed-form expressions of outage probability (OP) and closed-form approximation ergodic capacity (EC) generated for both devices in the main scenario. The outage and ergodic capacity performances in some scenarios are compared to confirm the benefits of the considered system. The number of antennas was found to have a crucial impact on the performances. The simulation results show that the OP for both users strongly decreases when the signal-to-noise ratio (SNR), number of antennas, and fading severity factor of Nakagami-m fading increase. The proposed scheme outperforms the orthogonal multiple access (OMA) scheme in outage performance for two users. The analytical results match Monte Carlo simulations to confirm the exactness of the derived expressions.

Adaptive Clustering of Users in Power Domain NOMA.

By enabling multiple non-orthogonal transmissions, power domain non-orthogonal multiple access (PD-NOMA) potentially increases a system's spectral efficiency. This technique can become an alternative for future generations of wireless communication networks. The efficiency of this method fundamentally depends on two previous processing steps: an appropriate grouping of users (transmission candidates) as a function of the channel gains and the choice of power levels that will be used to transmit each signal. Thus far, the solutions presented in the literature to address the problems of user clustering and power allocation do not consider the dynamics of communication systems, i.e., the temporal variation in the number of users and the channel conditions. In order to consider these dynamic characteristics in the clustering of users in NOMA systems, this work proposes a new clustering technique based on a modification of the DenStream evolutionary algorithm, chosen for its evolutionary capacity, noise robustness and online processing. We evaluated the performance of the proposed clustering technique considering, for simplicity, the use of an already widely known power allocation strategy called improved fractional strategy power allocation (IFSPA). The results show that the proposed clustering technique can follow the system dynamics, clustering all users and favoring the uniformity of the transmission rate between the clusters. Compared to orthogonal multiple access (OMA) systems, the proposed model's gain was approximately 10%, obtained on a challenging communication scenario for NOMA systems since the channel model adopted does not favor a large difference in the channel gains between users.

Energy-Efficient Resource Allocation for NOMA HetNets in Millimeter Wave Communications

The integration of millimeter-wave (mmWave) and non-orthogonal multiple access (NOMA) has attracted academic and industrial interest as a promising enabling approach due to their massive bandwidth support and greater spectral efficiency. In this paper, we consider NOMA heterogeneous network (HetNet) in mmWave communications, consisting of the macro-cell and small cell tiers connected through the wireless backhaul. Following this, we propose a user grouping algorithm to significantly simplify the user clustering process where each cluster consists of highly correlated users to suppress the inter-cluster interference. We then propose a hybrid analog/digital precoding scheme at the macro base station. The goal is to maximize the system’s total energy-efficiency (EE) in NOMA HetNet by jointly optimizing the transmission of hybrid precoding, power allocation, and bandwidth partition. Next, we formulate an optimization problem within the proposed framework under the minimum data rate and total maximum transmission power constraints. Due to the non-convexity nature of the formulated problem, it is challenging to obtain the optimal solution. As a result, we transform the original problem into a quasi-convex equivalent by analyzing its structure. Then we propose energy-efficient distributed power allocation algorithms to optimize the resource allocation of macro-cell and small cells. Finally, we exhibit the simulation results to demonstrate the proposed algorithm’s performance gains. The simulation results reveal that the proposed algorithmic approach surpasses state-of-the-art and traditional mmWave orthogonal multiple access systems in both capacity and EE.

Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free Wireless Networks

We consider a cell-free wireless system operated in Time Division Duplex (TDD) mode with user-centric clusters of remote radio units (RUs). Since the uplink pilot dimensions per channel coherence slot is limited, co-pilot users might incur mutual pilot contamination. In the current literature, it is assumed that the long-term statistical knowledge of all user channels is available. This enables Minimum Mean-Square Error channel estimation or simplified dominant subspace projection, which achieves significant pilot decontamination under certain assumptions on the channel covariance matrices. However, estimating the channel covariance matrix or even just its dominant subspace at all RUs forming a user cluster is not an easy task. In fact, if not properly designed, a piloting scheme for such long-term statistics estimation will also be subject to the contamination problem. In this paper, we propose a new channel subspace estimation scheme explicitly designed for cell-free wireless networks. Our scheme is based on 1) a sounding reference signal (SRS) using latin squares wideband frequency hopping, and 2) a subspace estimation method based on robust Principal Component Analysis (R-PCA). The SRS hopping scheme ensures that for any user and any RU participating in its cluster, only a few pilot measurements will contain strong co-pilot interference. These few heavily contaminated measurements are (implicitly) eliminated by R-PCA, which is designed to regularize the estimation and discount the “outlier” measurements. Our simulation results show that the proposed scheme achieves almost perfect subspace knowledge, which in turns yields system performance very close to that with ideal channel state information, thus essentially solving the problem of pilot contamination in cell-free user-centric TDD wireless networks.

Identifying drivers of evaluation bias in online reviews of city destinations

Online reviews are a significant part of decision-making processes of customers and organizations. Varying user rating behaviors and lack of consistency between ratings and expressed user opinion in reviews have been a subject of recent studies. This paper provides insights into forms of bias affecting users` evaluations in urban destination reviews. Our opinion mining approach identifies context-specific, intrinsic terms associated with rating levels. We assess the individual impact of negativity, positivity and user characteristics on ratings using regression and classification methods, to verify two main sources of biased rating behavior. First, opinion-related evaluation asymmetries, namely positivity bias, negativity bias and bias related to positivity of low arousal. Second, bias associated with users` past rating behavior. Building upon results, we distinguish five user clusters with distinct evaluation bias characteristics. Results enhance understanding of users` formation of rating choices, while our simplified segmentation approach can be useful for relevant user profiling practices.

Gender-Specific Clustering of Lifestyle Behaviors and Its Impacts on Cardiovascular Health.

Unhealthy lifestyle behaviors associated with cardiovascular risk manifest a clustering pattern. Our aim was to identify behavioral clusters by using 5 unhealthy lifestyle behaviors among the Korean population and examine the impacts of identified behavioral clusters on cardiovascular health (CVH). A cross-sectional study was conducted using data from the sixth Korea National Health and Nutrition Examination Survey. The participants were 7898, aged 19 to 64 years. The cluster analysis was performed using the behaviors of current smoking, binge drinking, physical inactivity, insufficient fruit intake, and sugar-sweetened beverage drinking. Cardiovascular health was defined as a composite modified z score calculated using biophysical factors. Men manifested 4 clusters (ie, risky binge drinkers, dominant smokers, dominant sugar-sweetened beverage drinkers, and nonsubstance/low-fruit eaters) characterized predominantly by substance use; women had 4 clusters (ie, substance users, physically inactive/low-fruit eaters, physically inactive/fruit eaters, and active adherers) characterized predominantly by physical inactivity. Among men, the clusters of dominant smokers and risky binge drinkers had significantly lower CVH scores than those with poor eating behaviors. Among women, the clusters of substance users and physically inactive/low-fruit eaters had significantly lower CVH scores than the active adherers. All the clusters in men had lower CVH scores than the worst cluster in women. There was a gender difference in the clustering pattern. The clusters with smoking and binge drinking in men and women were associated with negative impacts on CVH. Healthcare professionals should pay attention to the clustering pattern to design an efficient lifestyle intervention for cardiovascular disease prevention.

Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China

Understanding equity and travelers’ behavior plays a key role in creating suitable strategies to promote the development of the expressway. Especially, finding clusters of expressway users could help managers provide targeted policies in order to enhance service quality. However, it is challenging to identify expressway travel behaviors, such as traffic flow distribution and users’ classification. Electronic toll collection (ETC) has been widely applied to improve expressway management, because it can record the origin–destination information of users. This paper proposes a framework to analyze the equity and travel behavior of expressway users with a large amount of ETC data. In the first stage, the Gini coefficient is adopted to analyze expressway equity. In the second stage, 12 kinds of indicators are extracted, including number of trips, car type, mean distance, etc. In the third stage, kmeans algorithm is adopted to cluster the users, based on the introduced indicators. Finally, we analyze the traffic flow distribution of each group by constructing a traffic flow network. The results show that the Gini coefficient is 0.4193, which demonstrates evident inequity in the expressway service. Moreover, statistical analysis shows that expressway flow is complicated and 70.77% of travelers do not make repeat trips. It is demonstrated that expressway users can be divided into six groups, and the flow networks of cluster 2 and cluster 3 are connected more closely and evenly than other clusters are.

The social media audience of diplomatic crisis

Social media communication makes visible the linkages between governments’ actions to diplomatic events and domestic audiences’ reactions. This study analyses tweets that discuss the recent diplomatic crisis between Japan and Korea. Through the detection of clusters of Twitter users and the content analysis of the tweets in the two countries, this study shows the public’s interpretation of a crisis widely differs by their stances towards their country’s government. The analysis finds that the clusters are aligned with their pro- and anti-stances towards the current government and that pro-government clusters tend to interpret the diplomatic crisis through a historical perspective, while anti-government clusters interpret it as a matter of present-day politics. Furthermore, we find strong negativity in the tweets discussing the opponent online groups, especially by the anti-government cluster against the pro-government online camp. These findings suggest that a diplomatic crisis may create or deepen domestic polarisation.

Joint User Clustering and Graph Coloring Based Pilot Assignment for Cell-Free Massive MIMO Systems.

Pilot contamination due to pilot reuse seriously mitigates the performance of the cell-free massive multiple-input multiple-output (MIMO) systems. In this paper, we propose a joint pilot assignment scheme that employs user clustering and graph coloring (UC-GC) to reduce pilot contamination. The proposed method consists of two steps: firstly, we utilize AP selection to classify all users; secondly, we assign pilots to users with more severe pilot contamination using the graph coloring algorithm and then assign pilots to the remaining users. The numerical simulation results show that the proposed scheme outperforms existing pilot assignment schemes and significantly improves throughout with low complexity.

Joint resource allocation and power allocation scheme forMIMOassistedNOMAsystem

AbstractMultiple input multiple output (MIMO) plays an integral role in improving energy efficiency for enhancing all users' quality of service (QoS). For 5G wireless communication (WC), the NOMA strategy is used with the MIMO system to improve the system's sum rate and computational complexity. In this, terahertz (THz) band communication is introduced for future demand to carry more users and obtains high multiplexing gain. Multiple antenna arrays are used in the MIMO‐NOMA system to achieve high signal transmission. However, the use of antenna arrays increases as the number of users increases. A beam‐forming‐based signal transmission is introduced in this work to overcome this issue. For performing digital and Analog beam‐forming, a hybrid minimum mean square error (MMSE) with zero‐forcing (ZF) pre‐coder is proposed to eliminate the inter‐cluster interference. Initially, user clustering is undertaken using a fuzzy k‐means clustering (FKM) algorithm, and beam‐forming is performed among the user clustering. The joint power allocation (PA) and resource allocation (RA) is done using a hybrid artificial gorilla troop with leader optimization (HGTLO) technique to achieve a maximum sum rate of the RF channel. The performance of a proposed method is analyzed using the MATLAB tool. In the experimental scenario, the performance of energy efficiency (EE), spectral efficiency (SE), achievable sum rate, throughput, bit error rate (BER), and mean square error (MSE) attains the value of 35.82 bits/Hz/Joules, 198.11 bps/Hz, 116.78 Mbps, 93.06%, .003 and .011 respectively. The performance measures prove the efficacy of a proposed method.

Heuristic Radio Access Network Subslicing with User Clustering and Bandwidth Subpartitioning

In 5G and beyond, the network slicing is a crucial feature that ensures the fulfillment of service requirements. Nevertheless, the impact of the number of slices and slice size on the radio access network (RAN) slice performance has not yet been studied. This research is needed to understand the effects of creating subslices on slice resources to serve slice users and how the performance of RAN slices is affected by the number and size of these subslices. A slice is divided into numbers of subslices of different sizes, and the slice performance is evaluated based on the slice bandwidth utilization and slice goodput. A proposed subslicing algorithm is compared with k-means UE clustering and equal UE grouping. The MATLAB simulation results show that subslicing can improve slice performance. If the slice contains all UEs with a good block error ratio (BLER), then a slice performance improvement of up to 37% can be achieved, and it comes more from the decrease in bandwidth utilization than the increase in goodput. If a slice contains UEs with a poor BLER, then the slice performance can be improved by up to 84%, and it comes only from the goodput increase. The most important criterion in subslicing is the minimum subslice size in terms of resource blocks (RB), which is 73 for a slice that contains all good-BLER UEs. If a slice contains UEs with poor BLER, then the subslice can be smaller.

Willingness of Participation in an Application-Based Digital Data Collection among Different Social Groups and Smartphone User Clusters.

The main question of this paper is what factors influence willingness to participate in a smartphone-application-based data collection where participants both fill out a questionnaire and let the app collect data on their smartphone usage. Passive digital data collection is becoming more common, but it is still a new form of data collection. Due to the novelty factor, it is important to investigate how willingness to participate in such studies is influenced by both socio-economic variables and smartphone usage behaviour. We estimate multilevel models based on a survey experiment with vignettes for different characteristics of data collection (e.g., different incentives, duration of the study). Our results show that of the socio-demographic variables, age has the largest influence, with younger age groups having a higher willingness to participate than older ones. Smartphone use also has an impact on participation. Advanced users are more likely to participate, while users who only use the basic functions of their device are less likely to participate than those who use it mainly for social media. Finally, the explorative analysis with interaction terms between levels has shown that the circumstances of data collection matter differently for different social groups. These findings provide important clues on how to fine-tune circumstances to improve participation rates in this novel passive digital data collection.

Prediction meets time series with gaps: User clusters with specific usage behavior patterns

With mHealth apps, data can be recorded in real life, which makes them useful, for example, as an accompanying tool in treatments. However, such datasets, especially those based on apps with usage on a voluntary basis, are often affected by fluctuating engagement and by high user dropout rates. This makes it difficult to exploit the data using machine learning techniques and raises the question of whether users have stopped using the app. In this extended paper, we present a method to identify phases with varying dropout rates in a dataset and predict for each. We also present an approach to predict what period of inactivity can be expected for a user in the current state. We use change point detection to identify the phases, show how to deal with uneven misaligned time series and predict the user’s phase using time series classification. In addition, we examine how the evolution of adherence develops in individual clusters of individuals. We evaluated our method on the data of an mHealth app for tinnitus, and show that our approach is appropriate for the study of adherence in datasets with uneven, unaligned time series of different lengths and with missing values.

Who uses what food retailers? A cluster analysis of food retail usage in the Netherlands.

The aim of this study is to describe how individuals use different food retailers and how food retail usage varies according to socio-demographic and diet-related characteristics. A cross-sectional survey among Dutch adults (N=1784) was used. Results from the Two-step cluster analysis indicated that there were five clusters of food retail users. Use of discount supermarkets, organic supermarkets, fast-food outlets, and restaurants contributed to clustering, but use of regular supermarkets, local food shops and whether food retailers were close to home or further from home did not. The clusters included mixed food outlet users, discount supermarket and restaurant users, fast-food and restaurant users, predominant discount supermarket users and supermarkets, fast-food and restaurant users. Participants in each cluster had their own characteristics especially in terms of socio-economic position and diet quality. Future studies need to consider further how food retail selection links physical exposure to the food environment and diet.

When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach

Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users' demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes.

Refined Analysis of Older eHealth Users From an Agency Perspective: Quantitative Telephone Interview Study.

Most studies on the eHealth divide among older people have compared users to nonusers and found that age, gender, and education were associated with eHealth misuse. They assumed that these characteristics were structural barriers to eHealth adoption. Furthermore, eHealth practices have been examined in a narrow and incomplete way, and the studies disagree about the association between health conditions and eHealth use. Using a more dynamic theoretical lens, we investigated the potential motivations driving older adults' agential adoption of eHealth practices despite their advanced age. This study aimed to obtain a complete and detailed description of eHealth uses among older adults; examine whether demographic characteristics such as age, gender, and education (previously related to eHealth misuse) are still associated with the various eHealth clusters; and determine whether contextual factors such as changes in the health condition of older eHealth users or their loved ones are associated with older adult eHealth use. We conducted a 30-minute telephone interview with a representative sample of 442 Israeli adults (aged ≥50 years) with a sampling error of 2.04%. The interviews were conducted in Hebrew, Arabic, and Russian. Using factor analysis with 21 eHealth use questions, we identified 4 eHealth clusters: instrumental and administrative information seeking, information sharing, seeking information from peers, and web-based self-tracking. In addition to age, gender, education, internet experience, frequency of internet use, perceived eHealth literacy, and self-rated health, we asked respondents to indicate how much they had used offline health services because of a health crisis in the past year. We found differences in the number of older eHealth users in the various clusters. They used instrumental and administrative information (420/442, 95%) and obtained information from peers (348/442, 78.7%) the most; followed by web-based self-tracking related to health issues (305/442, 69%), and only a few (52/442, 11.3%) uploaded and shared health information on the web. When controlling for personal attributes, age, gender, and education were no longer predictors of eHealth use, nor was a chronic ailment. Instead, internet experience, frequency of internet use, and perceived eHealth literacy were associated with 3 eHealth clusters. Looking for health information for family and friends predicted all 4 eHealth clusters. Many older adults can overcome structural barriers such as age, gender, and education. The change in their or their loved ones' circumstances encouraged them to make deliberate efforts to embrace the new practices expected from today's patients. Seeking health information for family and friends and dealing with unexpected health crises motivates them to use eHealth. We suggest that health professionals ignore their tendency to label older people as nonusers and encourage them to benefit from using eHealth and overcome stereotypical ways of perceiving these patients.