User Selection Research Articles

A user selection algorithm for downlink MU-MIMO systems using product of singular values of effective channels

User selection plays a crucial role when aiming to improve channel throughput in multiuser multiple- input multiple-output downlink systems with block diagonalization linear precoding. While an optimal group of users can be found by exhaustively searching all possible combinations of users, the process becomes prohibitively complex when there is a large number of users. In this paper, we propose a greedy suboptimal user selection algorithm, which iteratively selects a user to maximize the product of the singular values of the effective channels from a set of unselected users. The proposed algorithm is based on the iterative precoder design method, whereby interuser interference is perfectly cancelled with lower complexity than with the singular value decomposition-based precoding technique. Moreover, to avoid frequent use of singular value decomposition, the proposed algorithm applies QR decomposition to the selected channel matrices in order to obtain singular values, which are equivalent to the product of diagonal elements in the upper triangular matrix. Simulation results show that, under a high SNR regime, the proposed algorithm outperforms several other greedy algorithms and can provide the same performance as a capacity-based algorithm for both correlated and uncorrelated channels with significantly reduced complexity.

Energy management and demand side management framework for nano-grid under various utility strategies and consumer’s preference

This research proposes a day-ahead scheduling utilizing both demand side management (DSM), and Energy Management (EM) in a grid-tied nanogrid comprises of photovoltaic, battery, and diesel generator for optimizing the generation cost and the energy not supplied (at grid-outage). Wider terminology is introduced to combine both load controllability (considered in traditional DSM), and interval capability to accommodate additional loads defined as flexible, non-flexible, and semi-flexible intervals. Moreover, the user selection for EM or combined operation of EM with DSM at different degrees of interval flexibility is defined as user preference. In addition, three utility’s operations are considered denoted as fixed rate pricing (FRP), time-of-use (ToU) pricing, and FRP with grid-outage. Hence, the suggested framework utilizes the opportunities of generation diversity, the electricity pricing strategy, and the load flexibility. The obtained result show that, DSM with flexible intervals reduces the cost by 21.02%, 25.23%, and 18.15% for FRP, ToU, and FRP with grid-outage scenarios respectively. And cost reduction by 20.41%, 22.42%, and 17.81% for DSM with semi-flexible intervals and 16.24%, 21.15%, and 13.8% for DSM with non-flexible intervals. This cost reduction is associated with full utilization of renewable energy generation and reduction of the energy from/to battery which enhances its lifetime or reduces the required battery size during design stage for cost and provisions saving in flexible and semi-flexible intervals. A hybrid optimization technique of Moth-flame optimization algorithm, and Lagrange’s multiplier is proposed and confirms its effectiveness with detailed comparison with other techniques.

Electric vehicle selection for industrial users using an interval-valued intuitionistic fuzzy COPRAS-based model

Electric vehicle selection for industrial users using an interval-valued intuitionistic fuzzy COPRAS-based model

Evaluating Benchtop Additive Manufacturing Processes Considering Latest Enhancements in Operational Factors

With the evolution of additive manufacturing technologies, concerning their material processing techniques, range of material choices and deposition speed, 3D printers are extensively employed in academia and industry for a number of purposes. It is no longer uncommon to have a portable, desktop 3D printer and build specific designs in a matter of minutes or hours. The functionality, costs, materials and applications of desktop 3D printers differ. Among the several desktop 3D printers with a variety of characteristics, it might be challenging to choose which one is optimal for the intended applications and uses. In this study, a variety of commercially available thermoplastic and photopolymer resin desktop 3D printers are presented and compared for user selection. This article intends to provide end-users of desktop 3D printers with fundamental information and guidelines via a comparison of desktop 3D-printing technologies and their technical characteristics, enabling them to assess and select appropriate desktop 3D printers for a variety of applications.

Trust enhanced POI recommendation with collaborative learning

With the rapid development and popularization of smart mobile devices, users tend to share their visited points-of-interest (POIs) on the network with attached location information, which forms a location-based social network (LBSN). LBSNs contain a wealth of valuable information, including the geographical coordinates of POIs and the social connections among users. Nowadays, lots of trust-enhanced approaches have fused the trust relationships of users together with other auxiliary information to provide more accurate recommendations. However, in the traditional trust-aware approaches, the embedding processes of the information on different graphs with different properties (e.g., user-user graph is an isomorphic graph, user-POI graph is a heterogeneous graph) are independent of each other and different embedding information is directly fused together without guidance, which limits their performance. More effective information fusion strategies are needed to improve the performance of trust-enhanced recommendation. To this end, we propose a Trust Enhanced POI recommendation approach with Collaborative Learning (TECL) to merge geographic information and social influence. Our proposed model integrates two modules, a GAT-based graph autoencoder as trust relationships embedding module and a multi-layer deep neural network as a user-POI graph learning module. By applying collaborative learning strategy, these two modules can interact with each other. The trust embedding module can guide the selection of user’s potential features, and in turn the user-POI graph learning module enhances the embedding process of trust relationships. Different information is fused through the two-way interaction of information, instead of travelling in one direction. Extensive experiments are conducted using real-world datasets, and results illustrate that our suggested approach outperforms state-of-the-art methods.

A Privacy-Preserving and Quality-Aware User Selection Scheme for IoT

In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these mobile users is a key indicator in selecting high-quality participants, as it directly reflects the reliability of the data they submit and their past performance. However, existing approaches often rely on a trusted centralized server, which can lead to single points of failure and increased vulnerability to attacks. Additionally, they may not adequately address the potential manipulation of reputation scores by malicious entities, leading to unreliable and potentially compromised user selection. To address these challenges, we propose PRUS, a privacy-preserving and quality-aware user selection scheme for IoT. By leveraging the decentralized and immutable nature of the blockchain, PRUS enhances the reliability of the user selection process. The scheme utilizes a public-key cryptosystem with distributed decryption to protect the privacy of users’ data and reputation, while truth discovery techniques are employed to ensure the accuracy of the collected data. Furthermore, a privacy-preserving verification algorithm using reputation commitment is developed to safeguard against the malicious tampering of reputation scores. Finally, the Dirichlet distribution is used to predict future reputation values, further improving the robustness of the selection process. Security analysis demonstrates that PRUS effectively protects user privacy, and experimental results indicate that the scheme offers significant advantages in terms of communication and computational efficiency.

QoS-Aware User Selection and Resource Assignment for Coexistence of NR-U and Wi-Fi-Enabled IoT Networks

QoS-Aware User Selection and Resource Assignment for Coexistence of NR-U and Wi-Fi-Enabled IoT Networks

Site-specific template generative approach for retrosynthetic planning

Retrosynthesis, the strategy of devising laboratory pathways by working backwards from the target compound, is crucial yet challenging. Enhancing retrosynthetic efficiency requires overcoming the vast complexity of chemical space, the limited known interconversions between molecules, and the challenges posed by limited experimental datasets. This study introduces generative machine learning methods for retrosynthetic planning. The approach features three innovations: generating reaction templates instead of reactants or synthons to create novel chemical transformations, allowing user selection of specific bonds to change for human-influenced synthesis, and employing a conditional kernel-elastic autoencoder (CKAE) to measure the similarity between generated and known reactions for chemical viability insights. These features form a coherent retrosynthetic framework, validated experimentally by designing a 3-step synthetic pathway for a challenging small molecule, demonstrating a significant improvement over previous 5-9 step approaches. This work highlights the utility and robustness of generative machine learning in addressing complex challenges in chemical synthesis.

An energy-efficient JT-CoMP enabled framework with adaptive OMA/NOMA in HetNets

The increasing demand for higher data rates and improved energy efficiency (EE) in next-generation wireless networks necessitates the optimized selection of multiple-access and coordination techniques. A hybrid joint transmission (JT)-coordinated multi-point (CoMP) enabled orthogonal multiple access (OMA)/non-orthogonal multiple access (NOMA) technique, combining the spectral efficiency (SE) and capacity benefits of CoMP NOMA with the interference mitigation of CoMP OMA, offers a highly adaptable solution for future wireless networks. This paper studies the joint optimization of CoMP/non-CoMP selection, OMA/NOMA selection, power allocation, and user pairing, with the objective of maximizing the EE in the network. A Dynamic CoMP user selection with energy-efficient adaptive multiple access (DCEAMA) algorithm to solve the formulated problem is proposed. Our Monte Carlo simulations show that the DCEAMA surpasses both the pure CoMP OMA and CoMP NOMA schemes in terms of EE, with an average increase of 38% and 26% respectively. We compare our heuristic technique to an exhaustive search strategy to evaluate its efficiency. The findings indicate that our strategy produces comparable EE across various power levels with reduced computational complexity.

Digital Twin System of Substation Equipment

This paper adopts the following technical solutions to achieve: a digital twin system for substation, including: database, database configuration module, data warehousing module, background data processing module and front-end browsing module; The front-end browsing module is used to display the data sent by the background data processing module in the corresponding form according to the user's selection; The form includes building the three-dimensional model of the substation based on BIM technology, and displaying the corresponding real-time data and status in the three-dimensional components of the three-dimensional model. Compared with the existing technology, it organically integrates the three-dimensional model and panoramic monitoring data of the substation, realizes the three-dimensional panoramic display of substation assets and the acquisition of substation monitoring data, improves the spatial experience of daily operation and maintenance comprehensive monitoring, and provides a solid data platform for the subsequent in-depth mining and intelligent analysis of multidimensional data of the substation.

Participatory Design Thinking: A User-Centered Approach to Computer Science Innovation

Traditional design thinking, while valuable, has limitations in user involvement. This paper proposes Participatory Design Thinking (PDT), a novel approach that addresses these limitations by placing users at the core of the entire design process in computer science (CS) projects (2). In this study, the shortcoming of traditional design thinking in user centricity was discussed and core principles and benefits of PDT are presented. Then, the practical implementation of PDT in CS, including user selection, training, and integration throughout the design stages were investigated. Finally, the potential impact of PDT on CS innovation and propose directions for future research were explored.

Quantum-compliant users scheduling optimization in joint transmission mobile access networks

Joint Transmission (JT) is the dynamic coordination of transmission and/or reception at multiple geographically separated sites to improve end-user service quality. When user equipment receives signals from multiple sites, downstream performance improves. An optimization problem arises in selecting the best user subset for JT within a multiple-input–multiple-output (MIMO) system. Unfortunately, a pure brute-force approach is not feasible due to exponential time growth with user combinations, unsuitable for real-time selection in mobile networks with users continuously changing in time. This article proposes quantum-compliant heuristics using quadratic unconstrained binary optimization (QUBO) for JT user scheduling. QUBO handles initial user selection, followed by brute-force exploration for the solution. Numerical results indicate that quantum-compliant methods decrease solution time without substantial accuracy loss compared to brute-force methods.

Distillation-Based User Selection for Heterogeneous Feder-ated Learning

Distillation-Based User Selection for Heterogeneous Feder-ated Learning

Promotion of Sustainable Products: Can Sustainability Labels Promote User Selection of Environmentally Friendly Products?

Sustainable development is growing in importance in today’s climate crisis. With the percentage of sales via digital channels increasing annually and consumers becoming aware of the environmental impact of their choices, a huge opportunity presents itself for promoting sustainable goods online if designers can find an effective way to raise awareness in consumers. Using a simulated e-commerce site (webshop), we investigated whether the presence or absence of sustainability labels displayed next to product images influenced users’ product selections. There was a significant association between the presentation of sustainability labels and the number of selected sustainable products. Overall, participants were familiar with sustainability labels and indicated willingness to pay ‘extra’ for sustainable products, while there was more variation in the way they felt that sustainability labels influenced their product choices. The findings highlight the complexities of factors influencing purchasing decisions and the need for more design-inspired research in this area. Whilst user interface design may be an effective means to influence sustainable product choices, design should also enable consumers to make informed product choices, while still providing a ‘fair’ e-commerce environment.

DNN-based algorithm for joint SIC ordering and power allocation in downlink NOMA-enabled heterogeneous networks

In the heterogeneous network (HetNet) employing downlink non-orthogonal multiple access (NOMA), we focus on the non-convex optimization problem to optimize the spectral efficiency (SE) while the users satisfy the quality-of-service (QoS) requirement. In the previous work, the optimal joint successive interference cancellation and power allocation (JSPA) algorithm for maximizing SE is proposed to solve the mixed-integer non-linear programming (MINLP) problem in NOMA-enabled HetNet. However, the optimal solution requires exponential complexity by the number of base stations (BSs). Therefore, we present a deep neural network (DNN)-based algorithm for JSPA to reduce the complexity. In particular, to deal with the MINLP-based JSPA problem, we reformulate it into an equivalently simple problem that optimizes only the power consumption of BSs. Then, we introduce the unsupervised DNN-based method for JSPA to handle the simplified problem. The presented scheme yields improved SE and outage performance compared with traditional DNN-based methods. Additionally, we propose a user selection scheme with low complexity to enhance the SE of the proposed DNN-based power allocation. Through simulations, we illustrate that the suggested DNN-based scheme can attain SE performance similar to that of the optimal scheme.

Dynamic Scheduling and Power Allocation with Random Arrival Rates in Dense User-Centric Scalable Cell-Free MIMO Networks

In this paper, we address scheduling methods for queue stabilization and appropriate power allocation techniques in downlink dense user-centric scalable cell-free multiple-input multiple-output (CF-MIMO) networks. Scheduling is performed by the central processing unit (CPU) scheduler using Lyapunov optimization for queue stabilization. In this process, the drift-plus-penalty is utilized, and the control parameter V serves as the weighting factor for the penalty term. The control parameter V is fixed to achieve queue stabilization. We introduce the dynamic V method, which adaptively selects the control parameter V considering the current queue backlog, arrival rate, and effective rate. The dynamic V method allows flexible scheduling based on traffic conditions, demonstrating its advantages over fixed V scheduling methods. In cases where UEs scheduled with dynamic V exceed the number of antennas at the access point (AP), the semi-orthogonal user selection (SUS) algorithm is employed to reschedule UEs with favorable channel conditions and orthogonality. Dynamic V shows the best queue stabilization performance across all traffic conditions. It shows a 10% degraded throughput performance compared to V = 10,000. Max-min fairness (MMF), sum SE maximization, and fractional power allocation (FPA) are widely considered power allocation methods. However, the power allocation method proposed in this paper, combining FPA and queue-based FPA, achieves up to 60% better queue stabilization performance compared to MMF. It is suitable for systems requiring low latency.

A dynamic pricing scheme for secure offloading and resource allocation based on the internet of vehicles

With the development of sixth-generation (6G) wireless communication technology, billions of vehicles will access the network in the future, and the number of vehicle applications and user data will also increase dramatically. Traditional cloud computing faces serious problems of latency and energy consumption in handling massive data. Mobile edge computing (MEC) has emerged to dramatically improve computing efficiency and reduce energy consumption by placing computing servers at network edge locations close to vehicles. However, the service range of MEC servers is limited and cannot fully satisfy user requirements. In addition, the task offloading process has security risks. The current research focuses on how to reduce the energy consumption and latency overhead of task offloading and neglects the economic cost or data transmission security of vehicles. To solve the above problems, we propose a cooperative security offloading (CSO) scheme for auxiliary vehicles and MEC servers. Firstly, we propose a dynamic pricing mechanism for computing resources by considering the credibility of MEC servers and auxiliary vehicles, the urgency of the task, and the number of users competing for auxiliary vehicles. Secondly, to prevent malicious MEC servers and eavesdroppers from attacking, we employ homomorphic encryption to protect user privacy. Meanwhile, efficient and secure computing services are achieved by optimizing user selection decisions, offloading decisions, and resource allocation decisions. Finally, the optimal decisions are obtained by the dueling DQN-based resource allocation and pricing strategy (DDRP) and the cost-minimizing security offloading (CMSO) algorithm, which minimizes the economic cost of users while maximizing security. Simulation results show that, compared with some existing schemes, the CSO scheme effectively reduces the economic cost of users while ensuring the security of data transmission.

MEC-DA: Memory-Efficient Collaborative Domain Adaptation for Mobile Edge Devices

It is prevalent for a mobile edge device to conduct local inference using a compact machine learning model, which achieves lower latency and less compromise of data privacy as compared to cloud-based inference. To work in a new environment, the compact model needs to be adapted to the target data from the environment so as to maintain a high inference accuracy. However, directly applying domain adaptation to the compact model leads to a low inference accuracy. Hence, a scheme called memory-efficient collaborative domain adaptation (MEC-DA) is developed in this paper to boost the compact model's inference accuracy on the target data while preserving data privacy. It first deploys a large model to the mobile edge devices where domain adaptation is conducted to adapt the large model to the target data. This process requires training of the large model, which causes high memory consumption. A new method called lite residual hypothesis transfer (LRHT) is thus designed to achieve memory-efficient domain adaptation. The knowledge of the large model is then transferred to the compact model via knowledge distillation. To prevent the compact model from forgetting the knowledge of the source data, a collaborative knowledge distillation (Co-KD) method is developed to unify the source data on the server and the target data on an edge device to update the compact model. MEC-DA can protect data privacy and handle user mobility properly via secure aggregation and user selection, respectively. Extensive experiments on several tasks of object recognition show that MEC-DA improves the inference accuracy by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$12.5\%$</tex-math></inline-formula> , as compared to the state-of-the-art schemes.

Breathe Easy, an automated respiratory data pipeline for waveform characteristic analysis

Whole body barometric flow through plethysmography is used to study respiratory output in rodent genetic and disease models. Turn-key commercial and custom systems are both used to gather multiple data streams for respiratory and metabolic parameters. Comprehensive and accurate data analysis is crucial to modelling congenital, pathogenic, and degenerative diseases. However, such studies produce large amounts of data that are time-consuming and diffcult to analyze. The investigator may use cumbersome hand annotation subject to observer bias or a series of software products to manage raw data, calculated data, statistical analysis, and graphing. Commercial software, however, is prohibitively expensive or bundled with turn-key systems limiting options for bespoke respiratory measurement systems. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge. Here, we present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs, and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface (GUI) for uploading data files, setting waveform feature thresholds, and defining experimental variables. After loading the GUI a, python-based program, Signal Analysis Selection and Segmentation Integration (SASSI) takes the user-settings and selects all quantitatively defined, quality breaths from each period of interest in the signal files. After SASSI, the STatistics and Graph Generator (STAGG) takes those breaths and performs linear mixed effects modeling based on user selections for each dependent variable of interest. Furthermore, our software can integrate non-respiratory experimental outcomes including quantified behavioral or gene expression data to test for breathing related associations. To validate breath selection and generate optimized default values, results from Breathe Easy were compared to manual selection by experts, which represents the current standard in the field. We show high quality breath selection and a significant reduction in quality of breaths that were rejected by the software. We also demonstrate Breathe Easy’s utility and capacity by examining a 2-year longitudinal study of an Alzheimer’s Disease mouse model to assess contributions of forebrain pathology in disordered breathing (600+ files; 1TB data). We show that, at least with this model of AD, there does not seem to be an association between forebrain pathology and respiratory outcomes. Breathe Easy offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high-throughput studies and machine learning approaches on large data sets. NIH: 1F32HL160073-01A1, R01HL130249 44617-S4. BCM McNair Scholar Program, March of Dimes Basil O'Connor Research Award, Parker B. Francis Fellowship, CJ Foundation for SIDS. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Low-Complexity User Scheduling Framework for mmWave Hybrid Beam-forming OFDM System under Non-Ideal CSI

Introduction: User scheduling in millimeter-wave (mmWave) multi-user hybrid beam-forming Orthogonal Frequency Division Multiplexing (OFDM) systems involves the joint optimization of resource block group (RBG) allocation, beam pairing, and user selection. However, choosing the optimal scheduled User Equipment (UE), allocated RBGs, and communicating beams in practical mmWave hybrid beam-forming systems with non-ideal Channel State Information (CSI) remains challenging. Method: In this paper, we propose a low-complexity user scheduling framework. On one hand, two user classification methods are proposed under non-ideal CSI, assisted by beam search and RBG allocation respectively. On the other hand, in order to ensure both the throughput and fairness performance, a novel user selection scheme, called weighted user selection based on feedback threshold (WUSFT) scheme is proposed, and approximate closed-form expression of fairness index for both full feedback and feedback based on threshold are derived. Our proposed methods consist of three steps. First, RBGs are allocated based on the maximum received signal power (MRSP) of each user on each RBG, utilizing quasi-omnidirectional beams. In the second step, the communicating beams are further searched to achieve the MRSP with the allocated RBG. Result: Finally, the allocated RBG, or the determined beam information obtained through the beam search, is used to represent the correlation of user channels and classify the UEs into groups. Only UEs whose reported factor is not less than the feedback threshold will report received information. This simplifies the operation of user scheduling. Moreover, simulation results demonstrate that our proposed schemes can achieve more than 92.3% of the sum rate performance of exhaustive search methods while maintaining relatively low complexity. Conclusion: In addition, the user feedback overhead (FO) reduces obviously, especially with large UE number.