User System Research Articles

User-centric base station clustering and resource allocation for cell-edge users in 6G ultra-dense networks

Ultra-Dense Networks (UDNs) have been proposed to meet the ultra-high system capacity and ultra-high user experience rate requirements of sixth generation (6G) mobile networks. However, ultra-dense base stations (BSs) deployment poses challenges for cell-edge users such as BS selection, severe interference, resource allocation, and inter-cell handover. To tackle these obstacles, this paper formulates a joint BS clustering and resource allocation problem for cell-edge users in a 6G heterogeneous UDN system. For efficient resolution, this problem is decoupled into two sub-problems to be solved independently. We first propose a user-centric BS clustering algorithm based on many-to-many matching for the BS clustering problem in the heterogeneous UDN system; This algorithm considers the constraint of the system capacity. A many-to-many stable matching between users and small cell BSs is constructed under the optimization objective of maximizing the achievable user rate. Furthermore, we analyze the effectiveness, stability, convergence, and complexity of the BS clustering algorithm. Given established small cell BS clusters, we also propose a user-centric resource allocation algorithm based on network partitioning. This algorithm accounts for the interference of all users in the heterogeneous UDN system and maximizes intra-sub-network interference while minimizing inter-sub-network interference via a spectral clustering-based sub-network partitioning algorithm. Next, orthogonal allocation of resource blocks (RBs) is implemented within each sub-network, and RBs are spatially multiplexed between sub-networks. Numerical results confirm the benefits of the proposed methods: our algorithms outperform benchmark solutions in terms of the sum achievable system rate, average achievable user rate, and user spectral efficiency.

KLASIFIKASI BUAH SEGAR DAN BUSUK MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK DENGAN TFLITE SEBAGAI MEDIA PENERAPAN MODEL MACHINE LEARNING

CNN (Convolutional Neural Network) is one of the type of Deep Learning algorithm that can accept input in the form of images, or objects such as fruit, animals, office equipment, and so on. This CNN works is inspired by the neuron system in humans based on visual perception. The CNN model can be implemented into the Android system using TFLite. TFLite is a Machine Learning library specially designed for Android devices to create Machine Learning models in small binary and low latency. This can make it easier for Machine Learning researchers to use smartphones to develop the research system. Current technological advances make the fruit industry no longer use the manual fruit sorting process carried out by humans. The process of sorting fruit that is fit for consumption (fresh) and unfit for consumption (rotten) can reach hundreds and thousands, so it needs the help of machines to increase work speed. This fruit sorting process can be circumvented by creating a Machine Learning model that can classify fresh and rotten fruit which will then be processed by the system based on the desired output requirements. If the model can perform the classification well, then the fruit industry can take steps to make an automatic sorter by implementing the model into its system. This study aims to create a model with the CNN (Convolutional Neural Network) Algorithm and design a prototype system that can classify fresh and rotten fruit (red apples, bananas, and mandarin oranges) and apply the resulting model using TFLite on an Android Smartphone. The results indicated that the resulting prototype model can be used by users in the Android system. The results of the training model obtained an accuracy value of 98.20%. Meanwhile, the results of testing showed that the model can classify images from the same or different sources by 91.65% from 1090 images. These results indicate that the model is included in the excellent classification category.

How to Support Users in Understanding Intelligent Systems? An Analysis and Conceptual Framework of User Questions Considering User Mindsets, Involvement, and Knowledge Outcomes

The opaque nature of many intelligent systems violates established usability principles and thus presents a challenge for human-computer interaction. Research in the field therefore highlights the need for transparency, scrutability, intelligibility, interpretability and explainability, among others. While all of these terms carry a vision of supporting users in understanding intelligent systems, the underlying notions and assumptions about users and their interaction with the system often remain unclear. We review the literature in HCI through the lens of implied user questions to synthesise a conceptual framework integrating user mindsets, user involvement, and knowledge outcomes to reveal, differentiate and classify current notions in prior work. This framework aims to resolve conceptual ambiguity in the field and enables researchers to clarify their assumptions and become aware of those made in prior work. We further discuss related aspects such as stakeholders and trust, and also provide material to apply our framework in practice (e.g., ideation/design sessions). We thus hope to advance and structure the dialogue on supporting users in understanding intelligent systems.

Enabling Verifiable Privacy-Preserving Multi-Type Data Aggregation in Smart Grids

In this article, we analyze the inherent characteristic of smart grid systems, where we observe that a smart meter always generates different types of electricity consumption data for one user, and a control center (CC) always conducts an in-depth statistic analysis on these data for subsequent services. Among these data, some of them are very sensitive and should be prevented for any leakage and modification. Furthermore, due to the large number of users in a smart grid system, it is advantageous for CC to receive and process the data from different users simultaneously. To this end, we propose a verifiable privacy-preserving multi-type data aggregation scheme (VPMDA) for smart grids. VPMDA enables an aggregator gateway (AG) to aggregate encrypted multi-type data and forward the aggregated data to CC, such that CC checks the integrity of aggregated data and obtains the statistic analysis results (e.g., average, variance) on the aggregated data without learning each individual data content. We further extend VPMDA to improve the performance of verifying data integrity on CC significantly. We formally prove the security of VPMDA against various attacks. We also implement a prototype of VPMDA and conduct a comprehensive performance evaluation to demonstrate its feasibility and efficiency.

Non-orthogonal transmission for user and control plane split architecture in 5G systems and beyond: Performance analysis and design insights

In next generation networks, mobility management will be a critical issue due to dense base station (BS) deployment, for which user and control plane split architecture provides a promising solution. Jointly designing such architecture with nonorthogonal transmission brings in more flexibility to further improve system efficiency. This paper proposes a non-orthogonal transmission design for user and control plane split architecture. In this design, user equipments (UEs) will select the BS providing the strongest received signal to associate its data channel, but constantly connect its control channel to the nearest macro-cell BS (MBS). Upon non-orthogonal transmission, an MBS can multiplex data traffics and control signals on the same resource. Stochastic geometry based analysis is carried out to investigate outage probability, which extends its regular definition by jointly considering data and control channels, and then mobility-aware outage rate. Numerical results show that: 1) The proposed split architecture alleviates the increase in handover rate for ultra dense networking, compared with conventional architecture. 2) Non-orthogonal transmission outperforms traditional orthogonal transmission in the split architecture, because it is capable of accommodating more control channels. 3) By carefully adjusting power levels, minimum outage probabilities can be reached for macro-cell UEs in the proposed design.

Asynchronous Partial Gaussian Approximation Detection Algorithm for Uplink-Grouped MIMO-SCMA System

To relieve the impact caused by different arrival times of multiple users in the uplink MIMO-SCMA system, a grouped multiuser transmission model, according to different delays and its corresponding detection scheme, is proposed in this paper. Assuming perfect synchronization in one group, a message-passing algorithm based on serial propagation (SP-MPA) is proposed to reduce the bit error rate (BER), which could transfer the updated information to the next symbol as its initial probability. Furthermore, with a more practical case, the partial Gaussian approximation method (PGA) is designed to decrease the interference resulting from the imperfect synchronization in one group. As the result, the computing complexity of the proposed PGA method could be decreased by at least 20% compared with SP-MPA and the BER could be improved by about 10%.

Fitness landscape analysis and niching genetic approach for hybrid beamforming in RIS-aided communications

Reconfigurable intelligent surface (RIS) is a revolutionizing technology to achieve cost-effective communications. The active beamforming at the base station (BS) and the discrete phase shifts at RIS should be jointly designed to customize the propagation environment. However, current phase-shift setting methods ignore the non-separable property of phase shifts, degrading the performance, especially in cases with a large-sized RIS. To understand the problem characteristics related to the phase shifts and further tailor an eligible method with such characteristics, this paper, for the first time, analyzes the fitness landscape of the sum-rate maximization problem (maximizing the sum rate of users in a downlink multi-user multiple-input single-output system assisted by a RIS). Results show that the problem has a severe unstructured and rugged landscape, especially in cases with a large-sized RIS. This observation answers why current methods are ineligible and provides insightful guidance for designing a more intelligent method. With the landscape findings in mind, this paper introduces a niching genetic algorithm to solve the problem. In particular, the niching idea is employed to locate multiple local optima. These local optima act as stepping stones to facilitate approaching the global optima. Simulation results demonstrate that the proposed niching genetic algorithm obtains significant capacity gains over current methods in cases with large-sized RIS.

Joint Placement and Power Optimization of UAV-Relay in NOMA Enabled Maritime IoT System

In this paper, an unmanned aerial vehicle is utilized as an aerial relay to connect onshore base station with offshore users in a maritime IoT system with uplink non-orthogonal multiple access enabled. A coordinated direct and relay transmission scheme is adopted in the proposed system, where close shore maritime users directly communicate with onshore BS and offshore maritime users need assistance of an aerial relay to communicate with onshore BS. We aim to minimize the total transmit energy of the aerial relay by jointly optimizing the UAV hovering position and transmit power allocation. The minimum rate requirements of maritime users and transmitters’ power budgets are considered. The formulated optimization problem is non-convex due to its non-convex constraints. Therefore, we introduce successive convex optimization and block coordinate descent to decompose the original problem into two subproblems, which are alternately solved to optimize the UAV energy consumption with satisfying the proposed constraints. Numerical results indicate that the proposed algorithm outperformed the benchmark algorithm, and shed light on the potential of exploiting the energy-limited aerial relay in IoT systems.

Acceptability of Second-Line Antidepressant Medications Using Filled Prescription Sequences in a Nationwide Cohort Study.

Background: Although about half of patients do not respond to a first-line antidepressant medication, there is no consensus on the best second-line option. The aim of this nationwide population-based study was to rank antidepressants according to their relative acceptability (ie, efficacy and tolerability) using filled prescription sequences after failure of first treatment.Methods: About 1.2 million people were identified as new antidepressant users in the French national health data system in 2011. The inclusion criterion was having at least 2 filled prescriptions of a second-line treatment after a filled prescription of a first-line treatment, resulting in 63,726 participants. The outcome was clinical acceptability as measured by the continuation/change ratio for second-line treatment. Continuation sequence was defined as at least 2 refills of the same treatment. Change sequence was defined as at least 1 filled prescription of another antidepressant. Adjusted odds ratios (aORs) were computed through multivariable binary logistic regressions.Results: Intraclass switch had a better acceptability than interclass switch (aOR [95% CI]: 1.23 [1.20-1.28]). According to the first-line treatment, intraclass switch remained more acceptable for selective serotonin reuptake inhibitors only (1.37 [1.31-1.42]). For α2 blockers and tricyclic agents, combination antidepressant therapy was the most acceptable second-line option (1.59 [1.27-2.01] and 2.53 [1.53-4.04], respectively), whereas for serotonin-norepinephrine reuptake inhibitors there was no significant difference between the strategies. For other antidepressants, intraclass switch had lower acceptability than interclass switch (0.70 [0.51-0.95]).Conclusions: Administrative claim databases may help with ranking acceptability of second-line treatments in real world settings and complement randomized controlled trials in informing clinicians about the most acceptable second-line options according to the first-line treatment.

A Framework to Model and Control the State of Presence in Virtual Reality Systems

This work is concerned with the modelling and control of the state of presence of a user in a virtual reality system (VRS). The manner how the state of a user interacting with the VRS is measured by some of its physiological signals is discussed. Based on the user-VRS interaction, a novel feedback control schema to manipulate the user state is proposed. In this, the error between the required and the actual user behavior is used by a controller to select the appropriate stimulus that must be executed by the VRS core scenario to reduce the error. The schema includes the modelling of the presence state by a discrete event strategy based on Petri Nets. Based on the obtained model, the control strategy to select the appropriate stimuli is devised. Finally, some real experiments with a VRS prototype are carried out to show the effectiveness of the proposal.

Frequency-Packed Faster-Than-Nyquist Signaling via Symbol-Level Precoding for Multiuser MISO Redundant Transmissions

This work addresses the issue of interference generated by co-channel users in downlink multi-antenna multicarrier systems with frequency-packed faster-than-Nyquist (FTN) signaling. The resulting interference stems from an aggressive strategy for enhancing the throughput via frequency reuse across different users and the squeezing of signals in the time-frequency plane beyond the Nyquist limit. The spectral efficiency is proved to be increasing with the frequency packing and FTN acceleration factors. The lower bound for the FTN sampling period that guarantees information losslesness is derived as a function of the transmitting-filter roll-off factor, the frequency-packing factor, and the number of subcarriers. Space-time-frequency symbol-level precoders (SLPs) that trade off constructive and destructive interblock interference (IBI) at the single-antenna user terminals are proposed. Redundant elements are added as guard interval to cope with vestigial destructive IBI effects. The proposals can handle channels with delay spread longer than the multicarrier-symbol duration. The receiver architecture is simple, for it does not require digital multicarrier demodulation. Simulations indicate that the proposed SLP outperforms zero-forcing precoding and achieves a target balance between spectral and energy efficiencies by controlling the amount of added redundancy from zero (full IBI) to half (destructive IBI-free) the group delay of the equivalent channel.

Joint beamforming for secure communication in RIS-assisted cognitive radio networks

This paper studies the security and communication efficiency problem of a reconfigurable intelligent surface (RIS) assisted cognitive radio network, in which we proposed a scheme where secrecy rate (SR) and transmission rate are jointly considered. In specific, with the help of RIS, we exploit the green interference caused by the secondary network to ensure the physical layer security of the primary network. By taking the mutual interference between these two networks into account, we establish a problem to minimize the total transmit power subject to the quality-of-service requirement of secondary user (SU), SR requirement of the primary users (PUs) and limited interference temperature of PUs. In our proposed iterative algorithm, the transmit and reflect beamforming are alternatively optimized by transforming PUs' SR constraint into a second order cone (SOC) form, and the optimal transmit and reflect beamforming are achieved based on the iterative penalty function. Simulation results show the superiority of our proposed algorithm in terms of the total transmit power requirement compared with other benchmarks, and the total transmit power of our proposed algorithm is not very susceptible to the number of eavesdroppers and PUs, which is conducive to serving multiple users in the wireless communication system.

Quaternion-based knowledge graph neural network for social recommendation

In recent years, the surge in the number of users in recommender systems has brought unprecedented opportunities and challenges to research on recommender systems. The development of the graph neural network makes social recommendation based on graph data more advantageous, but it also brings problems with parameterization. Although the recommender system contains a large amount of data, such as users, items, and user-item interactions, the social relationship data of users in social recommendation are still sparse. To solve the problem of parameterization, we propose a novel quaternion-based knowledge graph neural network for social recommendation (QSoR), which applies hypercomplex space to the social recommendation. Without affecting the recommendation quality, QSoR utilizes the expressibility of the quaternion and weight sharing mechanism of the Hamilton product to reduce the parameters during training. To solve the problem of the sparsity of users’ social relationship data, we propose a quaternion-based explicit and implicit social relationships integration algorithm, which obtains users’ implicit social relationships from the user-item graph as a supplement for users’ social information. We design a double-end attention mechanism when obtaining the user’s preferences and social relationships, which makes full use of the item information and the interaction information between the user and the neighbor nodes. In this way, we can obtain users’ personalized preferences, distinguish the importance of user relationships, and obtain neighbors with a high degree of influence on users, thus improving the accuracy of recommendation results. The social relationship graph and the user-item graph are effectively combined in the quaternion space to make personalized recommendations for users. Experiments on three public datasets, Yelp, Last-FM, and Epinions, demonstrate the validity of the proposed model QSoR.

Beyond Click-and-View: a Comparative Study of Data Management Approaches for Interactive Visualization

Visual analytics solutions have been growing in popularity in recent years, not only for showing final results but also for assisting in interactive analysis and decision-making. Analysis of a large amount of data requires flexible exploration and visualizations. However, queries that span geographical regions over time slices are expensive to compute, which turns it challenging to accomplish interactive speeds for huge data sets. Such systems require efficient data availability, so that response time does not interfere with the user’s ability to observe and analyze. Simultaneously, researches in the database domain have proposed solutions that can be used to support visualization systems. This article presents a comparative study of data management approaches to support interactive visualizations. The chosen data management solutions are (i) Apache Drill (a Polystore system), (ii) Apache Spark (a big data framework), (iii) Elasticsearch (a search engine), (iv) MonetDB (a column-oriented DBMS), and (v) PostgreSQL (a relational DBMS). To evaluate the performance of each solution, we selected a list of spatiotemporal queries among multiple queries submitted by users in a visual analytics system for rainfall data analysis named TEMPO. The results of this study show that Apache Spark and MonetDB present the best performance for the selected queries.

Physical fitness data monitoring of college students based on the internet of things and blockchain.

Contemporary college students are the pillars of the country and bear the responsibility of building a great country. College students should not only have smart brains, but also have strong bodies. The state has always attached great importance to the physical condition of college students and has promulgated a series of relevant policies and regulations to ensure the effective development of college students' physical health work. This paper aims to monitor and research college students' physical fitness data based on the Internet of Things and blockchain technology. This paper first introduces the data collection based on the Internet of Things, the Internet of Things data collection system has good versatility, ease of use, and quite rich functions, which can realize the collection and reliable transmission of different environmental data. Then focuses on the data collection and confidentiality technology based on blockchain. Each user in the blockchain system has a pair of public and private keys, and elliptic curve algorithms are usually used to generate public key cryptography. Finally, based on the Internet of Things and blockchain technology, the physical fitness data of college students is analyzed and researched. The experimental results of this paper show that, according to the data collection technology of the Internet of Things and blockchain, the analysis of variance is carried out on the data of male pull-ups and female sit-ups of 2019 students. The analysis of variance F of boys' pull-ups is 76.222, and the significance is about 0, that is, P < 0.01. The difference is very obvious, which proves that there is a significant difference in boys' pull-ups in the past 3 years. The analysis of variance F for girls' sit-ups is 89.187, and the significance is about 0. Similarly, it shows that there are significant differences in girls' sit-ups in the past 3 years. Therefore, the existing teaching mode is stabilized and physical exercise is enhanced. Meanwhile, to enhance the physical fitness of students, it is necessary to strengthen the strength of physical education teachers and increase the introduction of sports talents and business training.

Securing Radio Resources Allocation With Deep Reinforcement Learning for IoE Services in Next-Generation Wireless Networks

The next generation wireless network (NGWN) is undergoing an unprecedented revolution, in which trillions of machines, people, and objects are interconnected to realize the Internet of Everything (IoE). with the emergence of IoE services such as virtual reality, augmented reality, and industrial 5 G, the scarcity of radio resources becomes more serious. Moreover, there are hidden dangers of untrusted terminals accessing the system and illegally manipulating interconnected devices. To tackle these challenges, this paper proposes a securing radio resources allocation scheme with Deep Reinforcement Learning for IoE services in NGWN. First, the solution uses a BP neural network based on multi-feature optimized Firefly Algorithm (FA) for spectrum prediction, thereby improving the prediction accuracy and avoiding interference between unauthorized and authorized users with efficient radio utilization. Then, a spectrum sensing method based on deep reinforcement learning is proposed to identify the untrusted users in system while fusing the sensing results, to enhance the security of the cooperative process and the detection accuracy of spectrum holes. Extensive simulation results show that the proposal is superior to the traditional solutions in terms of prediction accuracy, spectrum utilization and energy consumption, and is suitable for deployment in future wireless systems.

Graph Neural Network Aided MU-MIMO Detectors

Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. A base station serves many users in an uplink MU-MIMO system, leading to a substantial multi-user interference (MUI). Designing a high-performance detector for dealing with a strong MUI is challenging. This paper analyses the performance degradation caused by the posterior distribution approximation used in the state-of-the-art message passing (MP) detectors in the presence of high MUI. We develop a graph neural network based framework to fine-tune the MP detectors’ cavity distributions and thus improve the posterior distribution approximation in the MP detectors. We then propose two novel neural network based detectors which rely on the expectation propagation (EP) and Bayesian parallel interference cancellation (BPIC), referred to as the GEPNet and GPICNet detectors, respectively. The GEPNet detector maximizes detection performance, while GPICNet detector balances the performance and complexity. We provide proof of the permutation equivariance property, allowing the detectors to be trained only once, even in the systems with dynamic changes of the number of users. The simulation results show that the proposed GEPNet detector performance approaches maximum likelihood performance in various configurations and GPICNet detector doubles the multiplexing gain of BPIC detector.

Strategic access in a Green IoT(Internet of Things) system with an unreliable server

The present research aims to study the strategic behaviour of users in an Internet of Things(IoT) system that is impeded by an unreliable server. In the system, an IoT device is equipped with an energy harvesting unit. The IoT system under study is employed as a Markovian retrial queueing system that is used in a single server, subject to active failures. The failed server is repaired immediately, while the user who was served before the system’s breakdown event waits in the server until the server is repaired. The generating function approach is used to identify important system performance metrics. Moreover, queueing theory concepts are incorporated into game theory for the analysis of the users’ strategies. The users in the IoT system, act to maximize their expected benefit without being perturbed by other users in the system and determine their equilibrium joining strategies. Furthermore, taking into account social welfare factors, the IoT device acts as a social planner, and as a consequence, socially optimal joining strategies are also analysed. Finally, the analytical findings are validated with numerical examples.

Downlink beamforming design for mobile users in massive MIMO system

With numerous antennas, the massive multiple-input multiple-output (MIMO) system can precisely point to the user equipments (UEs) with narrow beams. Mobile UEs, however, may suffer from the narrow beam nature of the massive MIMO system, since UEs may move out of the beam coverage. Frequent CSI feedback and beam adjustment help to track mobile UEs. However, a significant amount of transmission and computation overhead is incurred. In this paper, we consider a mobility adaption problem in massive MIMO systems with infrequent CSI feedback. We propose a beam design method to use one beam to serve multiple transmissions, thus reducing the overhead of frequency beam adjustment. An optimization problem is formulated that maximizes the overall system sum rate during one beam adjustment, i.e., during the entire time interval between CSI feedback. We assume that each transmission antenna has a peak power constraint, as the power amplifier in each branch limits the transmit power. A fractional programming based algorithm is proposed to solve the problem. To further reduce the computation overhead, a sub-optimal solution is obtained. Simulation results show that the proposed algorithms achieve satisfactory performance with little transmission and computation overhead compared to existing algorithms.

Advancing Digital Payment Systems: Combining AI, Big Data, and Biometric Authentication for Enhanced Security

Digital payment solutions have recently gained extraordinary popularity across the globe. Many individuals in cities or urban areas have begun to use these payment systems for financial transactions. The combined use of different advanced technologies is indispensable for widespread adoption. Various obstacles to performing financial transactions using digital payment systems must be considered. Among these obstacles, security is the most remarkable point. Although this online payment system is fast and convenient, the chances of fraud are very high. A cybercriminal can hack into the system, stealing personal information like phone numbers and passwords. This article will focus on a new digital payment security system that will take advantage of artificial intelligence (AI), big data, and biometric authentication (BA). When using this security system, a transaction request will be sent. First, it will check whether the phone number is a registered user and if it is, further authentication will be carried out. The system will automatically authenticate the user’s identity with the help of facial recognition technology that will analyze the features of the face. Further, the user has to show various gestures that are analyzed by the system. This and other personal information will be converted into a number and then encrypted before being stored in the database. The incoming transaction request will be compared against the personal number. If the user is not unique, the transaction request will be immediately rejected. Secondly, if the number matches but the user is fraudulent, the transaction will also be rejected. Only if both are unique, the system will check for duplicate numbers among the other transactions for the particular period. If other numbers are not found, the transaction is executed; otherwise, it is still rejected. This is done to find out fake and auto-generated numbers. This security system will be helpful to ensure the authenticity of the user in digital payment systems.