SIC Aided K-Repetition for Mission-Critical MTC in Cell-Free Massive MIMO

<div>In this paper, a successive interference cancellation (SIC) aided K-repetition scheme is proposed to support contention-based mission-critical machine-type communication (MTC) in cell-free (CF) massive multiple-input and multipleoutput (MIMO) systems. With the assistance of a tailored deep neural network (DNN) based preamble multiplicity estimator, the proposed SIC in K-repetition is capable of fully cancelling the interference signals, which leads to the reliability improvement in CF massive MIMO. Simulation results show the accuracy of preamble multiplicity estimation by the proposed DNN, and</div><div>demonstrate that, compared to the existing schemes, the proposed SIC scheme can achieve an improvement of two orders of magnitude in terms of block error rate (BLER) under a given latency constraint. Moreover, when the number of access points (APs) is sufficiently large, employing the proposed SIC scheme provides a great potential to meet ultra-reliable and low-latency requirements, e.g., 10^-5BLER and 1 ms access latency, for crowd mission-critical applications, which is far beyond the capabilities of the existing schemes.</div>

SIC Aided K-Repetition for Mission-Critical MTC in Cell-Free Massive MIMO

<div>In this paper, a successive interference cancellation (SIC) aided K-repetition scheme is proposed to support contention-based mission-critical machine-type communication (MTC) in cell-free (CF) massive multiple-input and multipleoutput (MIMO) systems. With the assistance of a tailored deep neural network (DNN) based preamble multiplicity estimator, the proposed SIC in K-repetition is capable of fully cancelling the interference signals, which leads to the reliability improvement in CF massive MIMO. Simulation results show the accuracy of preamble multiplicity estimation by the proposed DNN, and</div><div>demonstrate that, compared to the existing schemes, the proposed SIC scheme can achieve an improvement of two orders of magnitude in terms of block error rate (BLER) under a given latency constraint. Moreover, when the number of access points (APs) is sufficiently large, employing the proposed SIC scheme provides a great potential to meet ultra-reliable and low-latency requirements, e.g., 10^-5BLER and 1 ms access latency, for crowd mission-critical applications, which is far beyond the capabilities of the existing schemes.</div>

Ultra-Reliable Communication for Critical Machine Type Communication via CRAN-Enabled Multi-Connectivity Diversity Schemes

This paper focuses on edge-enabled cloud radio access network architecture to achieve ultra-reliable communication, a crucial enabler for supporting mission-critical machine-type communication networks. We propose coordinated multi-point transmission schemes taking advantage of diversity mechanisms in interference-limited downlink cellular networks. The network scenario comprises spatially distributed multiple remote radio heads (RRHs) that may cooperate through silencing, or by using more elaborated diversity strategies such as maximum ratio transmission or transmit antenna selection to serve user equipment in the ultra-reliable operation regime. We derive an exact closed-form expression for the outage probabilities and expected values of signal-to-interference ratio for silencing, transmit antenna selection and maximum ratio transmission schemes. We formulate rate control and energy efficiency under reliability constraints to test the performance and resource usage of the proposed schemes. Furthermore, we study the impact on average system sum throughput with throughput-reliability trade-off under cooperative communication. Extensive numerical analysis shows the feasibility of ultra-reliable communication by implementing diversity schemes with RRHs cooperation.

Predicting Heart Disease at Early Stages using Machine Learning: A Survey

Cardiovascular disease diagnosis is the most difficult task in medicine. The diagnosis of heart disease is complicated because it requires the grouping of massive volumes of clinical and pathological data. As a result of this dilemma, researchers and clinical professionals have developed a strong interest in the efficient and exact prediction of heart disease. When it comes to heart disease, it is critical to obtain an accurate diagnosis at an early stage because time is of the essence. Heart disease is the largest cause of death worldwide, and early detection of heart disease is critical. Machine learning has evolved as one of the most progressive, dependable, and supportive tools in the medical field in recent years, providing the greatest assistance for disease prediction when properly trained and tested. The primary objective of this research is to evaluate several algorithms for heart disease prediction.

Study of Diebond Machine Platform on Theta Rotation Performance

Theta rotation on die during diebond process is one of the critical machine responses especially for land grid array (LGA) device with tight tolerances requirement. The paper focuses on the die theta rotation tolerance capability with critical design for LGA device evaluated on two different diebond machine platforms. The evaluation was narrowed down into two main diebond machines with the objective of attaining the best performance in terms of die theta rotation tolerance capability. The study used a side-by-side comparison analysis in terms of theta rotation on the two machines and presented the effect of machine selection on the theta rotation response. Theta rotation was monitored and both machines satisfied the specification of 1 degree of maximum rotation, though diebond Machine 1 was able to produce a more stable diebonding with only around less than 0.15 degree of theta rotation variation. For future works, the selected diebond machine could be used for devices with critical requirement.

Optimal Contract for Machine Repair and Maintenance

Maintenance outsourcing is quite common in industries that rely on complex and critical equipment. Instead of investing in the maintenance facilities, firms outsource maintenance activities to specialized companies. However, it may be hard for firms (i.e., principal) to observe whether maintenance companies (i.e., agent) put sufficient resources into providing the best service, which gives rise to agency issues. In a dynamic environment in which an agent is responsible for both maintenance and repair of a critical machine, how the principal uses payments and termination to tackle agency issues is a challenging problem. In “Optimal Contract for Machine Repair and Maintenance,” F. Tian, P. Sun, and I. Duenyas provide theoretical guidance on designing the optimal contract to induce efforts from an agent to efficiently operate a machine. Although they consider the very general contract forms, the optimal contracts demonstrate simple and intuitive structures, making them easy to describe and implement in practice.

An Embedded Online Hydraulic Fluid CM and RUL-System for Industry 4.0 Manufacturing Machines

Production machines and devices, especially those that operate continuously in multi-shift operation or are critical for the production process, must be equipped with an intelligent condition monitoring system for critical machine components. This is the only way to ensure high availability and prevent downtimes in critical phases of the production processes, affecting customer delivery times. This has become especially important in the context of the strategy Industry 4.0, wherein information technology, telecommunications, and manufacturing are united when the means of production are becoming more independent. This also applies to hydraulic fluid, an important component of most heavy machinery. The chapter presents the design and advantages to be achieved by the implementation of a comprehensive online condition monitoring (OCM) and remaining useful lifetime (RUL) system of built-in hydraulic fluid. The presented OCM-RUL system is designed conceptually for Industry 4.0 and focuses on the remote monitoring and self-diagnosis function of health condition for the fluid.