User Selection Research Articles

Proportional-Fair Resource Allocation for User-Centric Networks

Considering the computational complexity and fronthaul capacity requirements of network-wide centralized optimization for Cell-free (CF) massive multiple input multiple output (MIMO) networks, we focus on user-centric (UC) networks wherein each user is served by a subset of access points (APs) rather than all the APs. We study a proportional-fair resource allocation (RA) problem, including slot resource allocation and precoding design in UC networks over consecutive time-slots. We formulate the proportional-fair (PF) resource allocation problem as a series of weighted sum-rate maximization problems and develop a two-stage heuristic RA scheme to solve the problem. The scheme adopts a user grouping process to decompose resource allocation problem into intra-group orthogonal resource allocation sub-problems. A modularity-based user grouping algorithm upon a constructed weighted digraph is proposed. Then, relying on the grouping results, a parallel distributed PFRA algorithm consisting of user selection process and a Signal to Leakage plus Noise Ratio (SLNR)-based precoding design is proposed to fulfil intra-group resource allocation. Rather than global channel state information (CSI) collected from the whole network, local CSI obtained cooperatively within each AP subset is utilized in the proposed resource allocation scheme. Numerical results confirm that the proposed RA scheme outperforms in throughput while maintaining comparable fairness among users.

Evaluation of electric scooters as an alternative transportation mode for home-work trips in France

In France, the transportation sector is responsible for a third of greenhouse gas emissions. Moreover, in 2020, 72.8% of nationwide home-work trips were made by car. This presents a challenge on how to reduce the associated emissions and an opportunity to promote cleaner modes of transport, for example e-mobility options such as electric scooters. The assumption being that a modal shift of car-users towards e-scooters should result in improved environmental and societal benefits. The paper follows the methodology and actions taken to evaluate the program: a) selection of users for the experimentation, b) analysis of technical specifications and GPS monitoring of e-scooters, c) continuous user-surveys to understand travel habits, and d) the development of an analysis tool to assess the environmental impact compared to other forms of transportation based on their energy consumption (kWh), CO2, NOx and PM10 emitted. The paper presents the initial results of a pilot project in France, which aims to evaluate the influence of micro-mobility to reduce the environmental footprint of home-work trips, and identify user-behaviour challenges and opportunities in the shift of transportation modes.

Cache‐enabled reinforcement learning scheme for power allocation and user selection in opportunistic downlink NOMA transmissions

Non‐orthogonal multiple access (NOMA) allows multiple user equipment (UE) to simultaneously share the same resource blocks using varying levels of transmit power at the base station (BS) side. Proper allocation of transmission power and selection of candidate users for pairing over the same resource block are critical for an efficient utilization of the available resources. Optimal UE selection and power splitting among paired UEs can be made through an exhaustive search over the space of all possible solutions. However, the cost incurred by such approach can render it practically infeasible. Reinforcement learning (RL) deploying double deep‐Q networks (DDQN) is a promising framework that can be adopted for tackling the problem. In this article, an RL‐based DDQN scheme is proposed for user pairing in opportunistic access to downlink NOMA systems with capacity‐limited backhaul link connections. The proposed algorithm relies on proactive data caching to alleviate the throttling caused by backhaul bottlenecks, and optimized UE selection and power allocation are accomplished through the continuous interaction between an RL agent and the NOMA environment to increase the overall system throughput. Simulation results are presented to showcase the near‐optimal strategy achieved by the proposed scheme. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

How rating mechanisms shape user search, quality inference and engagement in online platforms: Experimental evidence

This paper experimentally tests three rating mechanisms on their ability to reduce the asymmetric information between users on the quality of content they evaluate in online platforms. Using a controlled laboratory environment, exogenously imposed variation in content quality and user preferences allows for the construction of a structural model over user search, inference, selection and engagement in the presence of a given rating mechanism. The results of this study suggest that increasing the granularity of the signals available to users increases their ability to make inferences into the quality of content, which causes improvements in user welfare. The experimental data directly revealed that a five-point likert scale rating mechanism improved user welfare by 10.8% over a single ‘like’ button, and counterfactual analysis from the structural model indirectly revealed it improved welfare by 16.2% over providing no rating mechanism at all. User engagement also increased with the granularity of the rating mechanism, which further aided in the reduction of the asymmetric information.

Demand-side Response User Selection Based on Improved BPSO

In order to use the demand-side response activities to stabilize the peak load, ensure the balance of power supply and demand, and promote the quality and efficiency of the power grid, this paper constructs the demand-side response user competition models in different stages. The improved BPSO algorithm is used to optimize the subsidy cost weighted model in the saturated user competition model, and the algorithm is implemented automatically based on the blockchain intelligent contract. Simulation and experimental results show that, compared with other user selection schemes, these models can improve the actual user response and reduce the subsidy cost of the demand response centre. It can be seen that in the case of saturated user competition, the improved BPSO algorithm is feasible in the application of demand-side response to user selection and optimization of demand-side management.

Top-k Socio-Spatial Co-engaged Location Selection for Social Users

With the advent of location-based social networks, users can tag their daily activities in different locations through check-ins. These check-in locations signify user preferences for various socio-spatial activities and can be used to improve the quality of services in some applications such as recommendation systems, advertising, and group formation. To support such applications, in this paper, we formulate a new problem of identifying top-k Socio-Spatial co-engaged Location Selection (SSLS) for users in a social graph, that selects the best set of k locations from a large number of location candidates relating to the user and her friends. The selected locations should be (i) spatially and socially relevant to the user and her friends, and (ii) diversified both spatially and socially to maximize the coverage of friends in the socio-spatial space. To address the NP-hard and challenging problem, we first develop an exact solution by designing some pruning strategies, and also develop an approximate solution by deriving relaxed bounds and advanced termination rules. To accelerate the efficiency, we further develop a fast exact approach and a meta-heuristic approximate approach. Finally, extensive experiments are conducted to evaluate the performance of our proposed algorithms against three adapted existing methods using four real-world datasets.

Combination of Auction Theory and Multi-Armed Bandits: Model, Algorithm, and Application

The multi-armed bandit (MAB) models have always received lots of attention from multiple research communities due to their broad application domains. The optimal selection problem with unknown rewards in advance, such as ad recommendation in social networks, spectrum access in the cognitive radio field, etc., can be efficiently solved by using MAB models. In an MAB model, given <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> arms whose rewards are unknown in advance, the player selects exactly one arm in each round, and his goal is to maximize the cumulative rewards over a fixed horizon. Further, a more general model called combinatorial MAB (i.e., CMAB), where <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> arms can be played simultaneously in each round, is put forward. However, the existing CMAB models neglect the strategic behaviors of the <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> arms, which indicates that one arm might report false information to increase its own profits. In fact, in many applications such as user selection in crowdsensing, the arms are not the feelingless machines but the rational individuals. To this end, we combine the upper confidence bound (UCB) with auction theory to develop a new algorithm called auction-based UCB (AUCB). We divide the auction-based CMAB problem into two sub-problems: winning arm selection and payment computation problems. For AUCB, we derive an upper bound on regret and prove the truthfulness in one round, individual rationality, and computational efficiency. In addition, we consider an extended situation that some arms may be unavailable in some rounds and the arms will bid inconsistently in different rounds. We devise another algorithm called eAUCB to solve this problem. Extensive simulations are conducted to show the significant performance of the proposed algorithms.

User Selection for Massive MIMO under Line-of-Sight Propagation

This paper provides a review of user selection algorithms for massive multiple-input multiple-output (MIMO) systems under the line-of-sight (LoS) propagation model. Although the LoS propagation is extremely important to some promising technologies, like in millimeter-wave communications, massive MIMO systems are rarely studied under this propagation model. This paper fills this gap by providing a comprehensive study encompassing several user selection algorithms, different linear precoders and simulation setups, and also considers the effect of partial channel state information (CSI). One important result is the existence of practical cases in which the LoS propagation model may lead to significant levels of interference among users within a cell; these cases are not satisfactorily addressed by the existing user selection algorithms. Motivated by this issue, a new user selection algorithm based on inter-channel interference (ICI) called ICI-based selection (ICIBS) is proposed. Unlike other techniques, the ICIBS accounts for the ICI in a global manner, thus yielding better results, especially in cases where there are many users interfering with each other. In such scenarios, simulation results show that when compared to the competing algorithms, the proposed approach provided an improvement of at least 10.9% in the maximum throughput and 7.7% in the 95%-probability throughput when half of the users were selected.

Cooperative Mode Switching-Based Cognitive NOMA With Transmit Antenna and User Selection

The paper presents performance analysis of a multiuser Cooperative Non-Orthogonal Multiple Access (CNOMA) network that is underlay to a primary network. A selected secondary Full-Duplex (FD) Near User (NU) cooperates with a selected secondary Far User (FU) by apportioning the Interference Temperature Limit (ITL) with an ITL Apportioning Parameter (ITLAP). This Cooperative Mode (CM) is exercised only when NU meets its own performance criteria, else the network switches to a Non-Cooperative Mode (NCM). The network is therefore made adaptive in such a way that performance of the NU is same as in a network without the FU. Performance of the network is further improved using Maximum Transmit Power-based Transmit Antenna Selection (MTP-TAS), Best NU-Best FU (BNBF) selection, and optimal power allocation, thereby also motivating NOMA even in power-constrained underlay environments where transmit powers are not always high. The selection mechanisms are designed in a way that additional overheads for Channel State Information (CSI) estimation and feedback are not imposed. Analytical expressions for outage probability are presented for both end users, assuming only statistical knowledge of channel gains under the assumption of imperfect Successive Interference Cancellation (SIC) at the NU. Outage minimizing optimal values of the NOMA Power Allocation Parameter (NPAP) and ITLAP are derived that minimize FU outage. Simulations validate the derived expressions, and demonstrate superiority of the proposed scheme over FD NU-based conventional NOMA as well as conventional OMA signalling.

User Recruitment Algorithm for Maximizing Quality under Limited Budget in Mobile Crowdsensing

In the mobile crowdsensing task assignment, under the premise that the data platform does not know the user’s perceived quality or cost value, how to establish a suitable user recruitment mechanism is the critical issue that this article needs to solve. It is necessary to learn the user’s perceived quality in the execution p. It also needs to try its best to ensure the efficiency and profit maximization of the mobile group intelligence perception platform. Therefore, this paper proposes a mobile crowdsensing user recruitment algorithm based on Combinatorial Multiarmed Bandit (CMAB) to solve the recruitment problem with known and unknown user costs. Firstly, the user recruitment process is modeled as a combined multiarm bandit model. Each rocker arm represents the selection of different users, and the income obtained represents the user’s perceived quality. Secondly, it proposes the upper confidence bound (UCB) algorithm, which updates the user’s perceptual quality according to the completion of the task. This algorithm sorts the users’ perceived quality values from high to low, then selects the most significant ratio of perceived quality to recruitment costs under the budget condition, assigns tasks, and updates their perceived quality. Finally, this paper introduces the regret value to measure the efficiency of the user recruitment algorithm and conducts many experimental simulations based on real data sets to verify the feasibility and effectiveness of the algorithm. The experimental results show that the recruitment algorithm with known user cost is close to the optimal algorithm, and the recruitment algorithm with unknown user cost is more than 75% of the optimal algorithm result, and the gap tends to decrease as the budget cost increases, compared with other comparisons. The algorithm is 25% higher, which proves that the proposed algorithm has good learning ability and can independently select high‐quality users to realize task assignments.

DELUXE: A DL-Based Link Adaptation for URLLC/eMBB Multiplexing in 5G NR

Ultra-Reliable and Low Latency Communications (URLLC) is an important use case in 5G NR that targets at 1-ms level delay sensitive applications. For fast transmission of URLLC traffic, a promising mechanism is to multiplex URLLC traffic into a channel occupied by enhanced Mobile BroadBand (eMBB) service through preemptive puncturing. Although preemptive puncturing can offer transmission resource to URLLC on demand, it will adversely affect throughput and link reliability performance of eMBB service. To mitigate such an adverse impact, a possible approach is to employ link adaptation (LA) through modulation and coding scheme (MCS) selection for eMBB users. In this paper, we study the problem of maximizing eMBB throughput through MCS selection while ensuring link reliability requirement for eMBB users. We present DELUXE – the first successful design and implementation based on deep learning to address this problem. DELUXE involves a novel mapping method to compress high-dimensional eMBB transmission information into a low-dimensional representation with minimal information loss, a learning method to learn and predict the block-error rate (BLER) under each MCS, and a fast calibration method to compensate errors in BLER predictions. For proof of concept, we implemented DELUXE through a link-level 5G NR simulator with GPU and MathWorks 5G toolbox. Through extensive experiments, we show that DELUXE can successfully choose MCS for eMBB transmissions to maintain the desired link reliability while striving for spectral efficiency. In addition, our implementation can meet the real-time requirement ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt; 125 \mu \text{s}$ </tex-math></inline-formula> ) in 5G NR.

An Web Service Recommendation Method Based on Location and Decomposition Machine Model

With the increasing number of Web services with similar functions on the Internet, traditional collaborative filtering service recommendation methods may encounter problems such as data sparseness, cold start, and poor scalability. To solve the above problems, this project proposes a new Web service recommendation method based on the decomposition machine model. The method decomposes the user trust relationship matrix and the product rating matrix while adding the geographic location information of the service, and transforms the correlation matrix of the calculated user feature vector and item feature vector into the same latent factor space by means of a decomposition machine. Optimize training model parameters to provide users with accurate prediction scores. The ultimate purpose of QoS prediction is to recommend high-quality services to users, improve the efficiency of users' discovery and selection of high-quality services, and ultimately promote the utilization of network Web services and promote service providers to release higher-quality services. Scientific significance, but also has better application value.

Cloud Computing Assisted Blockchain-Enabled Internet of Things

Recently, the term ‘Internet of Things’ (IoT) has garnered great attention. As a trusted, dependable, and decentralized approach, blockchain has already been used in IoT. However, the existing blockchain has a number of drawbacks that prevent it from being used as a generic platform for IoT. The nodes in IoT are heavily resource-limited, especially computing and networking resources. Unfortunately, they are necessary for the blockchain to solve complicated puzzles and propagate blocks. In this paper, we propose agent mining and cloud mining approaches to solve the above problem in the blockchain-enabled IoT. To be specific, miners act as mining agents for nodes in IoT, offload mining tasks to cloud computing servers, and use networking resources dynamically. Furthermore, in order to enhance the performance, the access selection of users, computing resources allocation, and networking resources allocation are formulated as a joint optimization problem. We then propose a dueling deep reinforcement learning approach to address this problem. Numerical results justify the effectiveness of our proposed scheme.

Балансування навантаження в розподіленій системі групової взаємопідтримки виконання поточних завдань

"The article is devoted to the development of a software platform for load balancing in a distributed system of group mutual support for the implementation of current tasks, approaches to visualizing progress and factors influencing the achievement of goals are determined. A comparative analysis of existing assistance services has been carried out. The basic requirements for the logical and functional components of the service being developed and the software for its implementation are substantiated. The architecture of the service being developed is designed, the structure of the personal profile of a user who has problems in terms of motivation, goal setting and planning is determined. A system of automatic selection of users, a calendar of tasks, a system of messaging has been developed. The programmatic result of the work is a web application with load balancing functionality. The developed web application consists of client and server parts and uses public cloud services. The purpose of the work is to develop a distributed system of service for group mutual support for performing personal tasks in the format of a web application using technologies for horizontal scaling of the load from the server side, and the development of the client part in the format of a single-page web application SPA (Single Page Application). The objectives of the research are to conduct a comparative analysis of modern software and instrumental platforms, to determine approaches to visualizing progress in setting up a group mutual support service and factors affecting the achievement of goals. Also, the main tasks should be noted the design of the architecture of the service being developed, the definition of the structure of the user's personal profile, the development of a subsystem for automatic selection of users, a calendar of tasks, a messaging subsystem, as well as the development of web applications, consisting of a client part in the form of SPA and distributed server software with load balancing functionality. To scale the software system, a preliminary division into separate microservices with their subsequent containerization was carried out, and a distributed architecture with placement schemes was designed."

FPGA-Based Hardware-in-the-Loop Simulation of User Selection Algorithms for Cooperative Transmission Technology Over LOS Channel on Geosynchronous Satellites

Distributed satellite clusters and cooperative transmission technology have been used to improve the communication capability of the geosynchronous orbit (GEO) satellites in recent years. To validate the effectiveness of distributed cooperative technology, the authors designed an information transmission system using FPGA-based hardware-in-the-loop simulation. The focus of this paper is to complete the implementation of the link layer user selection algorithm to verify that cooperative transmission techniques are feasible. This system uses a Xilinx ML605 FPGA for simulating the synchronization and exchange of data during coordinated transmission in a GEO window. The authors assumed a dual-satellite scenario in the simulation used to validate the efficacy of the satellite system. After testing the system optimized with pipelining technology, the number of clock cycles was reduced by 66&#x0025;. Furthermore, tests showed the system can process multi-user channel data with signal-to-noise ratios ranging from 110dB to 195dB and could select the optimal channel. Additionally, in a case study with 10 users and 2 collocated satellites, this optimized system&#x2019;s operation speed is approximately 2000 times faster-after hardware acceleration-than general-purpose processors, which verifies the efficacy of using distributed cooperative transmission algorithms. Because of this, the low-cost, and the low-power features, this system is a promising candidate for an embedded real-time simulation platform for GEO satellites.

Secure Mobile Edge Computing Networks in the Presence of Multiple Eavesdroppers

In this paper, we investigate a secure mobile edge computing (MEC) network in the presence of multiple eavesdroppers, where multiple users can offload parts of their tasks to the computational access point (CAP). The multiple eavesdroppers may overhear the confidential task offloading, which leads to information leakage. In order to address this issue, we present the minimization problem of the secrecy outage probability (SOP), by jointly taking into account the constraints from the latency and energy consumption. With the aim to improve the system secrecy performance, we then introduce three user selection criteria to choose the best user among multiple ones. Specifically, <i>criterion I</i> maximizes the locally computational capacity, while <i>criterion II</i> and <i>III</i> maximize the secrecy capacity and data rate of main links, respectively. For these criteria, we further analyze the system secrecy performance by deriving analytical and asymptotic expressions for the SOP, from which we can conclude important insights for the system design. Finally, simulation and analytical results are provided to verify the proposed analysis. The results show that the three criteria can efficiently safeguard the MEC networks, compared to the traditional local computing and fully offloading, especially with a large value of user number.

Files Delivery and Share Optimization in LEO Satellite-Terrestrial Integrated Networks: A NOMA Based Coalition Formation Game Approach

Non-orthogonal multiple access (NOMA) in satellite communication (SATCOM) system can bring high spectral efficiency and massive connectivity. In this paper, we investigate files delivery and share optimization in LEO satellite-terrestrial integrated networks (STINs). A NOMA based coalition formation game (CFG) approach is proposed for minimizing total cost, in which satellite transfer files to head users in each group via NOMA, and head users utilize device to device (D2D) communications to share files among users. Firstly, head users selection algorithm is proposed to choose each group&#x0027;s head users receiving files from satellite. Precoding vectors and transmit power optimization with imperfect channel state information (CSI) are derived to achieve successful NOMA transmission and files download cost minimization in each group. A graph theory based algorithm is proposed to find the optimal D2D links and minimize files share cost. Then, we formulate a CFG and propose a preference order (Group-Best order) for user grouping. Furthermore, we have proved that the CFG with Group-Best order is an exact potential game (EPG), which can get stable group partition and achieve global optimization, i.e., minimizing total cost. Finally, a best response algorithm for NOMA based CFG (NCFG) is proposed to find stable group partition. Simulation results verify that our proposed approach is better than other approaches.

CAD-BASED AUTOMATED G-CODE GENERATION FOR DRILLING OPERATIONS

The automated generation of G-code for machining processes is a valuable tool at the hands of every engineer and machinist. Nowadays, many software systems exist that provide automated functions related to G-code generation. Most free software require the import of a Drawing Exchange Format (DXF) file and cannot work directly on a 3D part. On the contrast, the equivalent commercially-available software systems are feature-rich and can provide a variety of automated processes, but are usually highly priced. The presented application aims to supplement the existing free Computer Aided Manufacturing (CAM) systems by providing a way of generating G-code for drilling operations, using already owned commercial 3D Computer Aided Design (CAD) systems such as SolidWorksTM. Thus, in the case of 3D part drilling, a standard 3D CAD system is sufficient since the code can be adopted by most modern CAD software with minor changes. Moreover, no specialized CAM software is required. In order to achieve this automation, the Application Programming Interface (API) of SolidWorks™ 2018 was utilized, which allows for the design of visualized User Interfaces (UI) and the development of code under the Visual Basic for Applications (VBA™) programming language. The available API methods are employed to recognize the features that were used to design the part, as well as extract the geometric parameters of each of these features. In addition, an embedded calculator automatically defines the cutting conditions (cutting speed, feed and tool) based on the user selections. Finally, the application generates the Computer Numerical Control (CNC) code for the summary of the discovered holes according to the standardized G-code commands; the output can be a typical TXT or NC file that can be easily converted to the preference of the user if necessary.

Budgeted influence and earned benefit maximization with tags in social networks

Influence Maximization Problem aims at identifying a limited number of highly influential users who will be working for diffusion agents to maximize the influence. In case of Budgeted Influence Maximization (BIM), the users of the network have a cost and influential user selection needs to be done within a given budget. In case of Earned Benefit Maximization (EBM) Problem, a set of target users along with their benefit value is given and the aim is to choose highly influential users within an allocated budget to maximize the earned benefit. In this paper, we study the BIM and EBM Problem under the tag-specific edge probability setting, which means instead of a single edge probability a set of probability values (each one for a specific context e.g., ‘games,’ ‘academics,’ etc.) per edge is given. The aim is to identify the influential tags and users for maximizing the influence and earned benefit. Considering the realistic fact that different tags have a different impact on different communities of a social network, we propose two solution methodologies and one pruning technique. A detailed analysis of all the solution approaches has been done. An extensive set of experiments have been carried out with three benchmark datasets. From the experiments, we observe that the proposed solution approaches outperform baseline methods (e.g., random node-random tag, high-degree node–high-frequency tag, high-degree node–high-frequency tag with community). For the tag-based BIM Problem the improvement is upto \(8\%\) in terms of number of influenced nodes and for the tag-based EBM Problem the improvement is upto \(15\%\) in terms of earned benefit.

Enhancing PHY-Security of FD-Enabled NOMA Systems Using Jamming and User Selection: Performance Analysis and DNN Evaluation

In this article, we study the physical-layer security (PHY-security) improvement method for a downlink nonorthogonal multiple access (NOMA) system in the presence of an active eavesdropper. To this end, we propose a full-duplex (FD)-enabled NOMA system and a promising scheme, called the minimal transmitter selection (MTS) scheme, to support secure transmission. Specifically, the cell-center and cell-edge users act simultaneously as both receivers and jammers to degrade the eavesdropper channel condition. Additionally, the proposed MTS scheme opportunistically selects the transmitter to minimize the maximum eavesdropper channel capacity. To estimate the secrecy performance of the proposed methods, we derive an approximated closed-form expression for secrecy outage probability (SOP) and build a deep neural network (DNN) model for SOP evaluation. Numerical results reveal that the proposed NOMA system and MTS scheme improve not only the SOP but also the secrecy sum throughput. Furthermore, the estimated SOP through the DNN model is shown to be tightly close to other approaches, i.e., the Monte-Carlo method and analytical expressions. The advantages and drawbacks of the proposed transmitter selection scheme are highlighted, along with insightful discussions.