Random User Research Articles

Evaluating energy harvesting UAV-NOMA network with random user pairing in the finite blocklength regime

This study proposes an integrated system that combines energy harvesting (EH) enabled unmanned aerial vehicles (UAVs) with non-orthogonal multiple access (NOMA) to enhance communication system performance within a cellular network. Addressing the limitations of existing analyses that often assume an infinite blocklength scenario, we explore EH-enabled UAV-NOMA systems within a cellular framework under a finite blocklength (FBL) scenario. The study investigates the complex interactions and advantages resulting from the integration of EH, NOMA, and UAV technologies, aiming to assess whether EH can sustain communication within this framework. The network model considers base stations (BSs), UAVs, and terrestrial devices distributed with independent Poisson point processes (PPPs) over a large area. In this network, BSs employ NOMA to serve cell center devices directly, while cell edge devices, which are nor in direct contact with BS, are served via simultaneous wireless information and power transfer (SWIPT) enabled UAVs. The study derives metrics including joint harvesting and decoding probability for a randomly selected UAV, coverage probability (CP) for cell devices, and end-to-end block error rate (BLER) probabilities for typical device pairs. The findings demonstrate that the proposed scheme effectively supplies all the necessary transmit power for communication purposes through EH, achieving reasonable reliability. Additionally, the study highlights the importance of considering a combination of blocklengths from different phases to achieve optimal performance, rather than solely relying on an increment in blocklength. Finally, the effects of parameter variations on network performance are examined.

Performance Analysis of Distributed Reconfigurable-Intelligent-Surface-Assisted Air–Ground Fusion Networks with Non-Ideal Environments

This paper investigates the impact of non-ideal environmental factors, including hardware impairments, random user distributions, and imperfect channel conditions, on the performance of distributed reconfigurable intelligent surface (RIS)-assisted air–ground fusion networks. Using an unmanned aerial vehicle (UAV) as an aerial base station, performance metrics such as the outage probability, ergodic rate, and energy efficiency are analyzed with Nakagami-m fading channels. To highlight the superiority of RIS-assisted air–ground networks, comparisons are made with point-to-point links, amplify-and-forward (AF) relay scenarios, conventional centralized RIS deployment, and fusion networks without hardware impairments. Monte Carlo simulations are employed to validate theoretical analyses, demonstrating that in non-ideal environmental conditions, distributed RIS-assisted air–ground fusion networks outperform benchmark scenarios. This model offers some insights into the improvement of wireless communication networks in emerging smart cities.

Faster or Cheaper: A Q-learning based cost-effective mixed cluster scaling method for achieving low tail latencies

The cloud elasticity allows users to acquire resources and release useless resources as needed. This feature has attracted more and more web service providers to deploy their latency-crucial, user-oriented applications on cloud platforms. For web service providers, in the case of fluctuating workloads, scaling their server clusters over time could save system expenditures without service quality violations. Therefore, a lot of cloud platforms begin to offer automatic scaling strategies based on threshold-based rules for helping web service providers to save system expenditures. However, building threshold-based rules requires expertise, and such reactive scaling strategies cannot guarantee low and consistent tail latencies. For those proactive scaling strategies depending on predictions, random user behaviors lead to declines in prediction accuracy. In this paper, we propose a reinforcement learning based proactive strategy for scaling a mixed cluster, which is composed of a variety of cloud instances. Ensuring the availability and quality of reward signals is the main problem to be solved for algorithms based on standard RL. We design a reward function which could balance service cost, service quality and other parameters which can affect decision-making. We assign different weights to parameters based on their effects on decision-making. In order to avoid the explosion of the state space caused by fluctuating workloads and various server status, we discretize the continuous state space of our model. Experimental results based on TailBench show that our Q-learning based scaling method can maintain low and consistent tail latencies while achieving fewer costs than three common baselines.

ИНТЕРНЕТ-МЕМ ЕРЕКШЕЛІКТЕРІ

The article analyzes one of the Internet communication tools that has recently become widespread on social networks – the Internet meme. An Internet meme is a unique communicative unit consisting of a two-level structure: linguistic (verbal) and visual (graphic), which is the creation of network users, offering a certain subtext to the online community. The work describes the path of its origin and development, and also studies the ideas developed in scientific works. Taking into account the media nature of Internet memes and distinguishing them from other types of online messages, the factors influencing their rapid spread and popularization are determined. It is shown that this depends on its quality and competitiveness. It is clear that creating such an Internet meme requires considerable effort on the part of the author: the Internet meme must be creative, interesting, and its idea must be fresh and original. In this regard, it was concluded that some memes «live» on the network for a long time, others are forgotten the next day, others are even ignored, and others are suddenly «reborn» thanks to a random user

Application of Markerless Augmented Reality on E-Catalog of Car Variations Using Natural Feature Tracking

Information technology is widely used by business people in promoting the products and services they sell, this is very important especially for businesses that sell products that are not common, for example car variations. Car variation business is already widely found in various regions in Indonesia, but the car variation business has a relatively small and specialized market. The main cause is the lack of promotion of product knowledge to the public and the lack of interesting and practical forms of promotional media that make car variation products rarely seen by vehicle owners. Therefore, in this study, attempted on the application of augmented reality technology into the e-catalog application of car variations using the natural feature tracking method to help the visual augmentation process of car variation products that want to be seen as a form of promotional media for car variation products in general, the e-catalog itself were made using unity engine. From this research, the results obtained are: the application of augmented reality was successfully carried out on the Android e-catalog application, the attractiveness of the application display with a good indicator of 100%, the attractiveness of augmented reality with a percentage of 88.9% on good indicators and the appropriateness of application development with a percentage of 100% on good indicators, these result are obtained from questionnaires that are distributed to both coworker and random users as sample.

Instagram, Depression, and Dark Flow - Using Social Media as a Maladaptive Coping Mechanism

Background: Instagram is a popular social media platform which uses algorithms to tailor content to the specific interests of individual users. We proposed that this tailored content induces a flow state where users become completely absorbed and time passes imperceptibly, creating a highly satisfying experience. For those who use Instagram to escape from depression, Instagram may provide the relief that they seek, but at a cost – neglecting real-life social supports and work/academic commitments. This neglect leads to further problems that prompt greater Instagram dependence. Methods: Using a repeated-measures design, 114 participants engaged in three conditions (Control, Generalized, and Personalized) for 10 minutes each. Participants answered questions about flow and positive affect (after each condition), as well as their Instagram use habits, depression, reasons for using Instagram, and time spent on Instagram. Results: We demonstrated that Instagram’s algorithms were effective. Participants experienced significantly greater flow and positive affect when using their tailored-content Instagram compared to a generalized account based on the interests of 30 random users. As evidence for our proposed cycle of dependence, we showed significant associations between depression, flow, and a measure of problematic Instagram use, as well as associations between being motivated to use Instagram to escape problems and problematic Instagram use. Conclusions: These results suggest that, at least for a subset of individuals with depression, Instagram serves as a maladaptive, escape-coping mechanism which induces flow and elevates mood, but ultimately leads to more problems due to overextended Instagram use.

Coded Caching in Networks With Heterogeneous User Activity

This work elevates coded caching networks from their purely information-theoretic framework to a stochastic setting, by exploring the effect of random user activity and by exploiting correlations in the activity patterns of different users. In particular, the work studies the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$</tex-math> </inline-formula> -user cache-aided broadcast channel with a limited number of cache states (i.e., the content stored at the cache of a certain user), and explores the effect of cache state association strategies in the presence of arbitrary user activity levels; a combination that strikes at the very core of the coded caching problem and its crippling subpacketization bottleneck. We first present a statistical analysis of the average worst-case delay performance of such subpacketization-constrained (state-constrained) coded caching networks, and provide computationally efficient performance bounds as well as scaling laws for any arbitrary probability distribution of the user-activity levels. The achieved performance is a result of a novel user-to-cache state association algorithm that leverages the knowledge of probabilistic user-activity levels. We then follow a data-driven approach that exploits the prior history on user-activity levels and correlations, in order to predict interference patterns, and thus better design the caching algorithm. This optimized strategy is based on the principle that users that overlap more, interfere more, and thus have higher priority to secure complementary cache states. This strategy is proven here to be within a small constant factor from the optimal. Finally, the above analysis is validated numerically using synthetic data following the Pareto principle. To the best of our understanding, this is the first work that seeks to exploit user-activity levels and correlations, in order to map future interference and design optimized coded caching algorithms that better handle this interference.

ICRB: An improved consensus‐based redactable blockchain considering contextual consistency

AbstractBlockchain is a distributed ledger proposed by Satoshi Nakamoto. It has become one of the most important networks for humans in the world. With the rapid development of blockchain technology, an increasing number of criminals are engaging in fraudulent activities, extortion and other financial crimes on the blockchain. Due to the immutability of blockchain, it is difficult for users to recover their losses. Some scholars have proposed the concept of the redactable blockchain. However, most redactable blockchain schemes fail to consider the impact of modification on the contextual economic relationships of the blockchain. We propose an improved redactable blockchain considering contextual consistency. Additionally, we utilize a security deposit mechanism to guarantee financial contextual consistency for the modified blockchain. We distribute users' security deposit account private keys to random users on the blockchain using Verifiable Secret Sharing, Verifiable Random Functions and other cryptographic primitives to achieve decentralized key management and modification. Experiments show that the performance of our scheme is acceptable in realistic situations.

FedSeq: A Hybrid Federated Learning Framework Based on Sequential In-Cluster Training

Federated learning (FL) enables multiple devices to collaboratively accomplish a machine learning task by iteratively exchanging their model updates instead of raw data with a parameter server (PS), thus protecting users' data privacy. Nevertheless, the communication efficiency and the nonindependent and identically distributed (non-IID) data are remaining intractable challenges in FL. This article proposes a hybrid FL framework called FedSeq, based on user clustering and sequential in-cluster training, to improve the communication efficiency and the test accuracy, especially on non-IID data. The FedSeq first divides users into multiple clusters and selects a cluster head (CH) on behalf on each cluster to upload the model updates to the PS, thus drastically reducing the uplink communication overhead. Within each cluster, a sequential training method is designed, which enables the CHs' models witness more categories of training data and go through more meta-updates per training epoch, thus promoting the test accuracy. Moreover, we also provide the convergence analysis of FedSeq on IID data with Random user clustering, with specific experiments validating our theoretical analysis from a simulation perspective. In the experiments, we compare our FedSeq with other baselines, including FedSGD, FedAVG, FedProx, FedCluster, and HierFAVG, of which the results demonstrate that FedSeq outperforms other FL paradigms in terms of model accuracy and training efficiency. We also test FedSeq with Random and Low-Energy Adaptive Clustering Hierarchy clustering strategies to demonstrate the robustness of FedSeq to different clustering strategies. The FedSeq offers a potential solution to cope with non-IID data and reduce uplink communication overhead in FL.

Impartial Cooperation in SWIPT-Assisted NOMA Systems With Random User Distribution

Impartial cooperation is considered in simultaneous wireless information and power transfer (SWIPT)-assisted downlink non-orthogonal multiple access (NOMA) systems with random user distribution. Unlike conventional SWIPT-assisted cooperative NOMA transmissions, we consider random user locations and investigate a transmission model in which both the near and far users of a NOMA system can serve as energy harvesting relays and forward information to each other. To assess the impact of user placement on the system performance, three user pairing schemes are designed. Closed-form expressions for the outage probability, diversity order, diversity-multiplexing trade-off (DMT), and system throughput are derived under different user location distributions, revealing that the novel SWIPT-assisted impartial cooperative NOMA improves these metrics of the near user, and does not degrade those of the far user, compared with the conventional schemes. Numerical results validate our analysis and confirm that our approach is more efficient in resource utilization, as compared to existing schemes.

Provisional drivers intend to speed less: The positive outcome for young drivers of a safe-driving app randomised trial

Speeding is a major contributor to young drivers' crashes and one of their most commonly observed risky behaviours. Practice-oriented research suggests that technology, such as smartphone safe-driving apps (apps), can help reduce risky behaviour like speeding. Some apps' effects (e.g. on speeding) have been investigated in simulator and field studies. Those studies on apps' effects seem challenging to replicate in the real world, do not control for general influences, or focus on short-term effects. Furthermore, the impact of a freely-released app on random users has not been investigated to date.This paper investigates the behavioural effects of one off-the-shelf app, Flo, in a training study with 210 young drivers aged 18 to 25, 84 intervention and 126 control participants. The app's impact on their speeding was assessed in a three-month randomised trial. ANCOVAs were performed to assess separately for changes in the Intervention participants' speeding intention and self-reported speeding behaviour relative to the Control group. The results suggest the intervention significantly affected the speeding intention of participants with provisional driving licences.These findings encourage discussion on the effectiveness of apps in safe-driving training and influencing behaviour, which can potentially be more effective for provisional drivers. We provide suggestion on improving smartphone apps' functionality to increase their potential for positive behavioural change.

Unsourced Multiple Access With Random User Activity

To account for the massive uncoordinated random access scenario, which is relevant for the Internet of Things, Polyanskiy (2017) proposed a novel formulation of the multiple-access problem, commonly referred to as unsourced multiple access, where all users employ a common codebook and the receiver decodes up to a permutation of the messages. In this paper, we extend this seminal work to the case where the number of active users is random and unknown <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> . We define a random-access code accounting for both misdetection (MD) and false alarm (FA), and derive a random-coding achievability bound for the Gaussian multiple access channel. Our bound captures the fundamental trade-off between MD and FA probabilities. It suggests that the lack of knowledge of the number of active users entails a small penalty in energy efficiency when the target MD and FA probabilities are high. However, as the target MD and FA probabilities decrease, the energy efficiency penalty becomes more significant. For example, in a typical IoT scenario with framelength 19200 complex channel uses and 25–300 active users in average, the required energy per bit to achieve both MD and FA probabilities below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , predicted by our bound, is only 0.5–0.7 dB higher than that predicted by the bound in Polyanskiy (2017) for a known number of active users. This gap increases to 3–4 dB when the target MD probability and/or FA probability is below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . Taking both MD and FA into account, we use our bound to benchmark the energy efficiency of slotted-ALOHA with multi-packet reception, of a decoder that simply treats interference as noise, and of some recently proposed unsourced multiple access schemes. Numerical results suggest that, when the target MD and FA probabilities are high, it is effective to estimate the number of active users, then treat this estimate as the true value, and use a coding scheme that performs well for the case of known number of active users. However, this approach becomes energy inefficient when the requirements on MD and FA probabilities are stringent.

Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning

Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of FL. In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users. To address this challenge, we introduce a secure aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of FL over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. Our experiments on MNIST, CIFAR-10 and CIFAR-100 datasets in the IID and the non-IID settings demonstrate the performance improvement over the baselines, both in terms of privacy protection and test accuracy.

Maximum Tolerated Number of Simultaneous BEV Charging Events in a Typical Low-Voltage Grid for Urban Residential Area

In this paper, we determine the maximum number of battery electric vehicles (BEVs) that can be charged simultaneously at full power during peak load hour without overloading transformer and lines or causing an unacceptable voltage drop in the low-voltage (LV) grid. In order to predict the BEVs charging demand, an application that takes into account the random user’s arrival time and the initial battery state of charge (SOC) was developed using the C++ programming language and the Qt toolkit. The network analysis was then carried out using the Quasi-Dynamic Simulation (QDS) toolbox in DIgSILENT PowerFactory on a typical German LV grid for a metropolitan urban area. The simulation findings indicate that the value of simultaneity factor (SF) plays an important role in identifying the most robust and weakest grid’s bottlenecks. There is currently no immediate threat of electromobility pushing the parameters of the grid to their unacceptable limits; however, it is essential to examine the LV grid’s bottlenecks and gradually prepare them for the ramp-up of BEVs. In the short term, the bottlenecks can be removed using conservative planning and operating principles; however, employing novel approaches will be crucial in the longer term.

Performance analysis of a NOMA-VLC system with random user location

In this paper, we investigate the performance of a downlink non-orthogonal multiple access (NOMA) based visible light communication (VLC) system. We consider that the users are randomly located following a binary Poisson point (BPP) Process. The performance of the considered system is evaluated by deriving novel closed-form expressions of the bit error ratio (BER) and ergodic sum rate. The impact of an error due to the successive interference cancellation (SIC) is also taken into consideration in the BER analysis. The obtained results with respect to different system parameters are instrumental to provide useful insights into the system performance.

Numerical Evaluation of the MU-MIMO Beamforming Performance with User Selection Algorithm

This paper presents the numerical evaluation of the ZF beamforming algorithm using the user selection in the multiuser multiantenna (MU-MIMO) downlink system. Two user selection algorithm – semiorthogonal user selection and greedy user selection are numerically evaluated based on the open source MIMO channel model. The sum rate performance depending on number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. The selection of users is based on the orthogonality of the channels among selected users. For MIMO channel simulation the QUADRIGA channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder using user selection (G ZF) outperforms the ZF precoder with random user selection in spectral efficiency. The greedy user selection in spatially correlated channel has advantage to semi-orthogonal user selection. It isobserved that as the increasing the number of served users used for selection the greedy user selection gives better performance than semi-orthogonal algorithm.

Multi-agent deep reinforcement learning for user association and resource allocation in integrated terrestrial and non-terrestrial networks

Integrating the terrestrial network with non-terrestrial networks to provide radio access as anticipated in the beyond 5G networks calls for efficient user association and resource allocation strategies. In this work, a weighted sum single objective optimization problem that maximizes the total network data rate while minimizing the mobility-induced handoffs and prioritizing service provisioning of mission-critical users in the integrated terrestrial and non-terrestrial network is formulated. The problem’s complexity is reduced by solving the defined problem in two phases; the user association followed by the resource distribution phase. Several proposed approaches to the user association sub-problem utilize a central node that requires nearly-complete information, which may not be available in real time. On the contrary, this paper proposes a centralized training and distributed execution multi-agent dueling double deep Q network (MA3DQN) solution. Each user collects the channel state and access node loading information in this approach and makes an association decision that considers its quality of service requirements. The algorithm’s performance is validated through comparison with the genetic algorithm (GA), the integer linear programming (ILP), a heuristic approximation-based solution, the greedy approach, and the random user association (RUA) algorithm. Moreover, the paper simulates the multi-agent deep Q network solution as an additional benchmark algorithm. Simulation results reveal that as the number of users in the network increases, the acquired data rate of the MA3DQN is within 0.48% and 0.4% of that achieved by the GA and ILP, respectively, and outperforms all other algorithms. Notably, the proposed MA3DQN algorithm presents the best running time, attaining a gain of 99.9% over the GA algorithm, which performs the poorest among the algorithms characterized by polynomial worst-case time complexity. Besides, the MA3DQN approach maintains a handoff probability of zero, unlike the ILP, the approximation-based, greedy, and RUA solutions.

A TDMA approach for OFDM-based multiuser RadCom systems

This paper is focused on the multiuser implementation of fusion of radar and communication (RadCom) in internet-of-vehicles (IoV) scenarios. Traditional time-division multiple access (TDMA) technology degrades the velocity detection performance of orthogonal frequency-division multiplexing (OFDM)-based RadCom systems. We propose a new TDMA approach for OFDM-based RadCom systems, where multiuser communication and radar detection are completed synchronously. We consider a continuous-wave TDMA OFDM structure in which random user data or Zadoff-Chu (ZC) sequences are transmitted in one symbol duration to ensure detection performance. As an application of interference cancellation method, user data demodulation and environment sensing can be simultaneously accomplished by our proposed approach. We carry out numerical evaluation and show wireless communication and radar detection performance over the continuous-wave TDMA OFDM-based RadCom approach.

FlexBeamOpt: Hybrid solution methodologies for high‐throughput GEO satellite beam laydown and resource allocation

SummaryModern satellite communication systems are required to serve heterogeneous and geographically dispersed user demands with limited resources. In this paper, we investigate methodologies for dynamic resource allocation in Geosynchronous Earth Orbit (GEO) High‐throughput Satellite (HTS) systems. We designed three solution approaches FlexBeamOpt v1, FlexBeamOpt v2, and FlexBeamOpt v3, each as a hybridization of custom heuristics, integer linear programming, and/or constraint programming. We test the performance of the three approaches on 12 test instances that vary in user distribution (realistic, random, and clustered), user numbers (500 vs. 5000 users), and demand distribution (uniform vs. random). We observed that FlexBeamOpt v1 consistently outperformed FlexBeamOpt v2 and FlexBeamOpt v3 in terms of demand coverage and number of users covered for realistic and random user distribution test instances but at the cost of computation time. FlexBeamOpt v3 is the fastest in these instances. For clustered user distribution instances, FlexBeamOpt v3 performed better in terms of demand coverage and number of users covered, at the cost of using more beams. For these test instances, FlexBeamOpt v2 is the fastest in terms of computation time while providing a comparable solution quality.

Optimal User Pairing Approach for NOMA-Based Cell-Free Massive MIMO Systems

This study investigates a non-orthogonal multiple access (NOMA)-assisted cell-free massive multiple-input multiple-output (MIMO) system, considering the impact of both individual and linear-combination channel estimations. To make the best use of NOMA as an enabler for cell-free massive MIMO systems, user pairing should be employed effectively. Random user pairing naturally leads to a non-optimal solution, whereas an exhaustive search approach is unfavorable for practical systems owing to the high complexity. In this study, we propose an optimal user pairing strategy to group users that jointly optimize the minimum downlink rate per user and power allocation at an acceptable cost of complexity. To address this problem, we first relax the binary variables to continuous variables and then develop an iterative algorithm based on the inner approximation method, yielding at least one locally optimal solution. Numerical results show that the proposed user pairing algorithm outperforms existing counterparts, such as conventional beamforming, random pairing, far pairing, and close-pairing strategies, while it can be performed dynamically, that is, two arbitrary users satisfying the formulated problem can be paired regardless of geographical distance. Finally, our approach demonstrates that the combination channel estimation-based NOMA-assisted cell-free massive MIMO achieves the best result in terms of the downlink rate per user when associated with the proposed algorithm.