Digital twin (DT) can help create a digital representation of a physical system, thereby reflecting its real-time status. The digital object, often called cyber twin (CT), facilitates real-time monitoring and control of the physical object, i.e., the so-called physical twin (PT). Owing to this ability, CTs can optimize the PTs and simulate their status, without interrupting the physical world. Given the various CT use cases, one can identify two distinct types of DT tasks: 1) update tasks for PT-CT synchronization and 2) inference tasks for obtaining real-time testing responses. The diverse real-time requirements for update/inference tasks raise the task scheduling problem that has been neglected in previous studies. In this article, the real-time DT task scheduling problem is investigated. In particular, a new approach for evaluating the performance of real-time scheduling of DT tasks is introduced considering the relationship between update/inference tasks and fairness among CTs. Moreover, offline and online DT task scheduling schemes are proposed with the goals of maximizing the DT freshness ratio and minimizing task rejections. In particular, the DT freshness ratio maximization problem is formulated as an offline task scheduling scheme. The proposed offline solution can significantly reduce the solution space without losing optimality. Furthermore, the scheduling policies for achieving the maximal DT freshness ratio are established using which an online scheduling algorithm is designed. Simulation results show that the proposed offline/online schemes increase the DT freshness ratio by at least 16% and 11%, respectively, compared to benchmarks. The results also show that the task rejection ratio of the proposed online algorithm is within 8% of the lower bound.

Reconfigurable intelligent surface (RIS) is a promising solution to support a large volume of data traffic and massive connectivity for future wireless mobile networks. Especially, RIS can mitigate the drawbacks in massive multiple-input multiple-output (MIMO) systems, such as the blockage caused by obstacles and the signal processing overhead by constructively and passively reflecting the incident wave toward the destination. In this paper, we provide an asymptotic analysis of the distribution of sum rate (SR) in RIS-aided massive MIMO systems. Using the asymptotic distribution of SR, the achievable scheduling gain and the optimal number of users are determined. In addition, we examined the channel hardening effect and outage probability through the achievable scheduling gain, and the optimal number of users is utilized to develop a low-complexity scheduling algorithm. Simulation results reveal that the SR obtained from our analysis closely aligns with the actual SR. The results also show that the channel hardening effect can vanish with many users thereby achieving the multiuser diversity gain, and an RIS-aided system is more reliable than a conventional massive MIMO system in terms of the outage probability. Furthermore, the proposed scheduling algorithm is shown to reduce computational complexity compared to the conventional scheduling algorithm.

In the Internet of Things (IoT) environment, edge computing can be initiated at anytime and anywhere. However, in an IoT environment, edge computing sessions are often ephemeral, i.e., they last for a short period of time and can often be discontinued once the current application usage is completed or the edge devices leave the system due to factors such as mobility. Therefore, in this paper, the problem of ephemeral edge computing in an IoT is studied by considering scenarios in which edge computing operates within a limited time period. To this end, a novel online framework is proposed in which a source edge node offloads its computing tasks from sensors within an area to neighboring edge nodes for distributed task computing, within the limited period of time of an ephemeral edge computing system. The online nature of the framework allows the edge nodes to optimize their task allocation and decide on which neighbors to use for task processing, even when the tasks are revealed to the source edge node in an online manner, and the information on future task arrivals is unknown. The proposed framework essentially maximizes the number of computed tasks by jointly considering the communication and computation latency. To solve the joint optimization, an online greedy algorithm is proposed and solved by using the primal-dual approach. Since the primal problem provides an upper bound of the original dual problem, the competitive ratio of the online approach is analytically derived as a function of the task sizes and the data rates of the edge nodes. Simulation results show that the proposed online algorithm can achieve a near-optimal task allocation with an optimality gap that is no higher than 7.1% compared to the offline, optimal solution with complete knowledge of all tasks.

Reconfigurable intelligent surfaces (RISs) have recently emerged as a promising technology that can achieve high spectrum and energy efficiency for future wireless networks by integrating a massive number of low-cost and passive reflecting elements. An RIS can manipulate the properties of an incident wave, such as the frequency, amplitude, and phase, and, then, reflect this manipulated wave to a desired destination, without the need for complex signal processing. In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations. In particular, a passive beamformer that can achieve the asymptotic optimal performance by controlling the incident wave properties is designed, under a limited RIS control link and practical reflection coefficients. In order to increase the achievable system sum-rate, a modulation scheme that can be used in an RIS without interfering with existing users is proposed and its average symbol error rate is asymptotically derived. Moreover, a new resource allocation algorithm that jointly considers user scheduling and power control is designed, under consideration of the proposed passive beamforming and modulation schemes. Simulation results show that the proposed schemes are in close agreement with their upper bounds in presence of a large number of RIS reflecting elements thereby verifying that the achievable rate in practical RISs satisfies the asymptotic optimality.

Digital twin (DT) can help create a digital representation of a physical system, thereby reflecting its real-time status. The digital object, often called cyber twin (CT), facilitates real-time monitoring and control of the physical object, i.e., the so-called physical twin (PT). Owing to this ability, CTs can optimize the PTs and simulate their status, without interrupting the physical world. Given the various CT use cases, one can identify two distinct types of DT tasks: 1) update tasks for PT-CT synchronization and 2) inference tasks for obtaining real-time testing responses. The diverse real-time requirements for update/inference tasks raise the task scheduling problem that has been neglected in previous studies. In this article, the real-time DT task scheduling problem is investigated. In particular, a new approach for evaluating the performance of real-time scheduling of DT tasks is introduced considering the relationship between update/inference tasks and fairness among CTs. Moreover, offline and online DT task scheduling schemes are proposed with the goals of maximizing the DT freshness ratio and minimizing task rejections. In particular, the DT freshness ratio maximization problem is formulated as an offline task scheduling scheme. The proposed offline solution can significantly reduce the solution space without losing optimality. Furthermore, the scheduling policies for achieving the maximal DT freshness ratio are established using which an online scheduling algorithm is designed. Simulation results show that the proposed offline/online schemes increase the DT freshness ratio by at least 16% and 11%, respectively, compared to benchmarks. The results also show that the task rejection ratio of the proposed online algorithm is within 8% of the lower bound.

Reconfigurable intelligent surface (RIS) is a promising solution to support a large volume of data traffic and massive connectivity for future wireless mobile networks. Especially, RIS can mitigate the drawbacks in massive multiple-input multiple-output (MIMO) systems, such as the blockage caused by obstacles and the signal processing overhead by constructively and passively reflecting the incident wave toward the destination. In this paper, we provide an asymptotic analysis of the distribution of sum rate (SR) in RIS-aided massive MIMO systems. Using the asymptotic distribution of SR, the achievable scheduling gain and the optimal number of users are determined. In addition, we examined the channel hardening effect and outage probability through the achievable scheduling gain, and the optimal number of users is utilized to develop a low-complexity scheduling algorithm. Simulation results reveal that the SR obtained from our analysis closely aligns with the actual SR. The results also show that the channel hardening effect can vanish with many users thereby achieving the multiuser diversity gain, and an RIS-aided system is more reliable than a conventional massive MIMO system in terms of the outage probability. Furthermore, the proposed scheduling algorithm is shown to reduce computational complexity compared to the conventional scheduling algorithm.

In the Internet of Things (IoT) environment, edge computing can be initiated at anytime and anywhere. However, in an IoT environment, edge computing sessions are often ephemeral, i.e., they last for a short period of time and can often be discontinued once the current application usage is completed or the edge devices leave the system due to factors such as mobility. Therefore, in this paper, the problem of ephemeral edge computing in an IoT is studied by considering scenarios in which edge computing operates within a limited time period. To this end, a novel online framework is proposed in which a source edge node offloads its computing tasks from sensors within an area to neighboring edge nodes for distributed task computing, within the limited period of time of an ephemeral edge computing system. The online nature of the framework allows the edge nodes to optimize their task allocation and decide on which neighbors to use for task processing, even when the tasks are revealed to the source edge node in an online manner, and the information on future task arrivals is unknown. The proposed framework essentially maximizes the number of computed tasks by jointly considering the communication and computation latency. To solve the joint optimization, an online greedy algorithm is proposed and solved by using the primal-dual approach. Since the primal problem provides an upper bound of the original dual problem, the competitive ratio of the online approach is analytically derived as a function of the task sizes and the data rates of the edge nodes. Simulation results show that the proposed online algorithm can achieve a near-optimal task allocation with an optimality gap that is no higher than 7.1% compared to the offline, optimal solution with complete knowledge of all tasks.

Reconfigurable intelligent surfaces (RISs) have recently emerged as a promising technology that can achieve high spectrum and energy efficiency for future wireless networks by integrating a massive number of low-cost and passive reflecting elements. An RIS can manipulate the properties of an incident wave, such as the frequency, amplitude, and phase, and, then, reflect this manipulated wave to a desired destination, without the need for complex signal processing. In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations. In particular, a passive beamformer that can achieve the asymptotic optimal performance by controlling the incident wave properties is designed, under a limited RIS control link and practical reflection coefficients. In order to increase the achievable system sum-rate, a modulation scheme that can be used in an RIS without interfering with existing users is proposed and its average symbol error rate is asymptotically derived. Moreover, a new resource allocation algorithm that jointly considers user scheduling and power control is designed, under consideration of the proposed passive beamforming and modulation schemes. Simulation results show that the proposed schemes are in close agreement with their upper bounds in presence of a large number of RIS reflecting elements thereby verifying that the achievable rate in practical RISs satisfies the asymptotic optimality.

The concept of a large intelligent surface (LIS) has recently emerged as a promising wireless communication paradigm that can exploit the entire surface of man-made structures for transmitting and receiving information. An LIS is expected to go beyond massive multiple-input multiple-output (MIMO) system, insofar as the desired channel can be modeled as a perfect line-of-sight. To understand the fundamental performance benefits, it is imperative to analyze its achievable data rate, under practical LIS environments and limitations. In this paper, an asymptotic analysis of the uplink data rate in an LIS-based large antenna-array system is presented. In particular, the asymptotic LIS rate is derived in a practical wireless environment where the estimated channel on LIS is subject to estimation errors, interference channels are spatially correlated Rician fading channels, and the LIS experiences hardware impairments. Moreover, the occurrence of the channel hardening effect is analyzed and the performance bound is asymptotically derived for the considered LIS system. The analytical asymptotic results are then shown to be in close agreement with the exact mutual information as the number of antennas and devices increase without bounds. Moreover, the derived ergodic rates show that hardware impairments, noise, and interference from estimation errors and the non-line-of-sight path become negligible as the number of antennas increases. Simulation results show that an LIS can achieve a performance that is comparable to conventional massive MIMO with improved reliability and a significantly reduced area for antenna deployment.

In digital twin (DT) systems, maintaining precise synchronization between the physical twin and the cyber twin is important for accurate system operation. However, synchronization errors (SE) accumulate over time, leading to substantial performance degradation. In this paper, the optimal inference task length that minimizes SE while maximizing the number of successfully processed inference task bits is analyzed. In previous studies, the synchronization status was evaluated using age of information (AoI), but AoI has a limitation that it cannot sufficiently reflect synchronization errors that may increase exponentially due to cumulative inaccuracies. To address this limitation, a novel performance metric, synchronization error per bit (SE/bit), is introduced, and an optimization problem is formulated based on this metric. A mathematical framework is developed, leveraging an update task model that follows a Poisson arrival process to derive the optimal inference task length that minimizes SE/bit. The analysis demonstrates that the optimal task length is independent of the system's update interval and delay time, instead being determined by the synchronization sensitivity parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex>. Furthermore, simulation results validate the effectiveness of the proposed model, showing that the optimal inference task length is associated with minimized SE and maximized successfully processed inference task bits. In this study, a scheduling and optimization framework is established to enhance inference task bit processing while minimizing synchronization errors in DT environments, making it applicable to various realtime DT applications.

In this paper, optimization of update period is investigated in a digital twin (DT) environment. In particular, we analyze the relationship between the synchronization error - caused by periodic update tasks - and the total number of processed bits. Based on this analysis, we propose a method to determine the optimal update period. In DT systems, accurate synchronization between the physical twin (PT) and the cyber twin (CT) is critical, as synchronization errors can degrade prediction accuracy, which in turn leads to a decline in overall system performance. Therefore, optimizing the update period is a key factor in maintaining DT system performance. To address this challenge, we mathematically modeled the effect of the update period on data processing capacity and synchronization accuracy, considering the exponential growth of synchronization error over time. Through this analysis, we derived the optimal update period that minimizes the ratio of total synchronization error to the number of processed bits. Our approach reformulates the update period optimization problem into a monotonically increasing function, demonstrating that the optimal update period can be efficiently determined while satisfying specific data processing capacity.

외로움은 현대 사회의 중요한 심리적 문제 중 하나로, 장기간 지속될 경우 정신건강에 부정 적인 영향을 미칠 수 있으며 비자살적 자해를 영향을 미칠 수 있는 요인으로 주목받아 왔다. 그러나 외로움이 비자살적 자해로 이어지는 경로를 설명하는 요인들에 대한 탐색은 미비한 상황이다. 이에 본 연구에서는 두 변인과 관련이 있는 것으로 나타난 자기비난과 반추가 외 로움과 비자살적 자해의 관계를 매개하는지 순차적 매개모형을 통해 살펴보고자 하였다. 이 를 위해 전국의 만 19세 이상 성인 중 일생에 한 번이라도 비자살적 자해 경험이 있는 300명 을 대상으로 자기보고식 설문지를 통해 자료를 수집하였다. SPSS 29.0과 Process Macro 4.3을 활용하여 분석한 결과는 다음과 같다. 첫째, 외로움, 비자살적 자해, 자기비난, 반추 간의 유 의한 정적 상관이 나타났다. 둘째, 외로움과 비자살적 자해의 간에 자기비난, 반추의 순차적 매개효과가 유의하였다. 외로움과 비자살적 자해의 관계에서 자기비난이 간접 매개하는 경로 는 유의하지 않았으나, 반추를 매개한 경로와 자기비난과 반추를 순차적으로 거치는 경로는 유의하였다. 이러한 결과는 외로움이 자기비난과 반추를 거쳐 비자살적 자해로 이어진다는 점을 지지한다. 본 연구 결과는 외로움을 경험하는 사람들을 대상으로 자기비난과 부정적 사 고에 대한 반추에 개입하는 것이 비자살적 자해 예방에 중요하다는 점을 시사한다.

The demand for satellite communication is increasing to provide connectivity in areas without terrestrial network coverage. Specifically, multibeam low-Earth orbit (LEO) satellite communication is emerging as a key solution to providing high-capacity, wide-area coverage to remote regions such as oceans, mountains, and deserts. In this paper, an opportunistic resource scheduling scheme is analyzed to efficiently utilize LEO satellite resources. An opportunistic resource scheduling algorithm is proposed that optimally allocates and schedules based on the time-varying channel conditions of each user. Moreover, the proposed algorithm is designed to maximize total average capacity while satisfying the minimum capacity requirement for each user, thereby ensuring user-specific quality of service (QoS). An algorithm utilizing a Lagrangian dual approach was developed to find the optimal beam allocation. The simulation results demonstrate that the proposed algorithm achieves capacity convergence while satisfying the QoS requirements of all users.