ABSTRACT This work presents a comprehensive study on the performance of an asymmetric dual‐hop radio frequency (RF)/free‐space optical (FSO) transmission system that utilizes a fixed gain amplify‐and‐forward protocol. The RF path is characterized by the Beaulieu–Xie model, and the FSO link is affected by Málaga () turbulence in the presence of pointing errors. In this study, we introduce novel and precise analytical expressions for the probability density function, the cumulative distribution function (CDF), and the moment generating function (MGF) of the overall signal‐to‐noise ratio (SNR). Using the derived expressions, we proceed to obtain accurate infinite series representations for the outage probability (OP), the average bit‐error rate (BER), and the ergodic capacity (EC). In addition, we conduct asymptotic analysis for the CDF, the MGF, the OP, the average BER, and the EC in the high SNR regime. Numerical results are compared with the Monte Carlo simulations to validate the accuracy of these derived expressions.

In multiunit industrial plant-wide processes, how to simultaneously model the dynamic correlations within units and the graph-structured interactions between units has not been explored by existing distributed monitoring methods. To address this issue, this work proposes a novel hybrid-order graph embedded distributed encoder-decoder (HGDED) for plant-wide process monitoring. Firstly, the whole process is decomposed into multiple operation units and characterized as a directed graph based on process knowledge. Then, distributed node encoder and decoder are developed under the sequence-to-sequence framework to capture the intra-unit temporal dependence. Between the encoder and decoder, a novel hybrid-order graph convolutional network (HGCN) is designed to simultaneously embed the inter-unit spatial dependence. Through multireceptive field graph convolution and spatial recurrent updates, HGCN can effectively exploit hybrid-order neighbor information to capture multi-cascaded interactions between units. Moreover, a spatial order optimizer is innovatively proposed to automatically learn the optimal discrete orders for different nodes, which helps HGCN better capture significant features from higher-order neighbor nodes. With concurrent analysis of temporal and spatial dependencies, the representation learning ability of HGDED is enhanced, thereby improving the monitoring performance. Finally, the effectiveness of the proposed method is demonstrated through the Tennessee Eastman process and a real-world air separation process.Note to Practitioners—The complex process characteristics and topology structure have imposed considerable challenges on monitoring multiunit industrial plant-wide processes. In practice, it is necessary to extract both the intra-unit temporal dependence and the inter-unit spatial dependence for distributed monitoring. Existing studies fail to consider these two important characteristics at the same time, which may lead to information loss and performance degradation. In this work, the proposed HGDED not only develops distributed node encoder and decoder to model the dynamic correlations within units, but also incorporates a HGCN to capture the interactions between units. In particular, HGDED explicitly models the whole process into a directed graph, which can characterize structural relationships in a more reasonable and fine-grained way. Considering the multi-cascaded information transmission between units, the HGCN is designed to exploit hybrid-order neighbor information for graph embedding, in which the optimal discrete orders are automatically determined by the spatial order optimizer. As a consequence, HGDED can better model process behaviors and extract more representative features to improve monitoring performance. In addition, HGDED follows an end-to-end training fashion, which is convenient for engineers to implement. The proposed method has been validated on two industrial cases, and it is suitable for monitoring various practical industrial plant-wide processes.

In multiunit industrial plant-wide processes (MIPPs), data-driven distributed process monitoring methods have played an important role in ensuring process safety and reliability. However, the existing studies fail to leverage the relational knowledge about the underlying process structure of MIPPs, which may lead to inaccurate process modeling and degradation of the monitoring performance. To tackle these issues, this work proposes a novel knowledge-enhanced distributed graph autoencoder for plant-wide process monitoring. Firstly, a posterior graph structure learning module (PGSL) is designed for relational knowledge discovery, which explicitly characterizes the dependencies between operation units in MIPPs into a directed graph. Prior knowledge is introduced into PGSL as a learning bias that steers the posterior graph to adhere to the underlying physics. Then, a novel distributed graph autoencoder (DGAE) is developed to encode both the local information within each unit and the global information between units for distributed process monitoring. The discovered posterior knowledge is embedded as a relational inductive bias in DGAE to enhance the capability of unsupervised representation learning, thereby improving the monitoring performance. Finally, the effectiveness of the proposed method is demonstrated through the Tennessee Eastman process and a real-world air separation process.

Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information. However, many existing methods focus on static graphs while ignoring evolving graph patterns. Inspired by the success of graph convolutional networks(GCNs) in static graph embedding, we propose a novel k-core based temporal graph convolutional network, the CTGCN, to learn node representations for dynamic graphs. In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity while simultaneously capturing graph dynamics. In the proposed framework, the traditional graph convolution is generalized into two phases, feature transformation and feature aggregation, which gives the CTGCN more flexibility and enables the CTGCN to learn connective and structural information under the same framework. Experimental results on 7 real-world graphs demonstrate that the CTGCN outperforms existing state-of-the-art graph embedding methods in several tasks, including link prediction and structural role classification. The source code of this work can be obtained fromhttps://github.com/jhljx/CTGCN.

This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters. By leveraging the geometric structure of the linear rewards, a collaborative algorithm called Fed-PE is proposed to cope with the heterogeneity across clients without exchanging local feature vectors or raw data. Fed-PE relies on a novel multi-client G-optimal design, and achieves near-optimal regrets for both disjoint and shared parameter cases with logarithmic communication costs. In addition, a new concept called collinearly-dependent policies is introduced, based on which a tight minimax regret lower bound for the disjoint parameter case is derived. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.

With the development of next generation of mobile communications, the access of massive Internet of Things (IoT) devices need a more intelligent and secure network. Blockchain has become an emerging technology due to its characteristics of decentralization, stability and transparency. Thus, the combination of blockchain and IoT has attracted the focus of researches. However, the conventional blockchain based on terrestrial network are limited to the scalability and latency. With the support of the wide coverage of satellite, satellite-based Internet of Things (S-IoT) can solve the defect of large consensus latency of blockchain in terrestrial networks. In this paper, considering the limitation of system power and to improve the freshness of information, i.e., the age of information (AoI), we propose a power allocation scheme to accelerate the blockchain consensus over the S-IoT network. Then, we derive the closed-form expressions to the latency and power consumption of blockchain consensus over the S-IoT network. Moreover, we formulate an average AoI (AAoI) optimization problem subject to the total power constraints and solve it by genetic algorithm. Simulation results show that the proposed power allocation scheme has a superior performance in terms of AAoI and throughput compared to the conventional blockchain schemes.

Satellite-based Internet of Thing (S-IoT) is generally considered as a potential solution for the ubiquitous coverage broadband access in the next generation of mobile network. Considering the limited onboard resource of satellites and massive machine type communications (mMTC) requirement, we propose a fairness-improved resource allocation scheme in Quality of Service (QoS)-guaranteed S-IoT non-orthogonal multiple access (NOMA) downlink network. To ensure all the downlink NOMA user terminals' (UTs') QoS and approach the maximization energy efficiency simultaneously, we formulate a joint energy efficiency and fairness optimization problem. Then, we construct three virtual queues to record the power consumption, queue backlog and transmission delay, respectively, and utilize the Lyapunov optimization framework for the purpose of coping with the joint optimization problem. Simulation results validate our proposed NOMA-QoS scheme outperforms existing optimization works in terms of fairness performance while approaching the maximum energy efficiency. Furthermore, the NOMA-QoS scheme also has higher satisfaction and lower outage probability than the existing optimization works.

In this paper, we investigate the impact of context diversity on stochastic linear contextual bandits. As opposed to the previous view that contexts lead to more difficult bandit learning, we show that when the contexts are sufficiently diverse, the learner is able to utilize the information obtained during exploitation to shorten the exploration process, thus achieving reduced regret. We design the LinUCB-d algorithm, and propose a novel approach to analyze its regret performance. The main theoretical result is that under the diverse context assumption, the cumulative expected regret of LinUCB-d is bounded by a constant. As a by-product, our results improve the previous understanding of LinUCB and strengthen its performance guarantee.

In this paper, we investigate an energy efficiency (EE) millimeter-wave (mmWave) band non-orthogonal multiple access (NOMA) downlink multi-relay system for integrated terrestrial-satellite networks (ITSN), where multiple terrestrial relay nodes decode and forward (DF) the NOMA signal from a high throughput satellite (HTS) to multiple destination nodes. We first define a common framework in which the HTS system and different terrestrial networks coexist in millimeter-wave (mmWave) band NOMA system. Then, the EE expressions of the system is obtained and the optimization problem of maximizing EE is proposed. To solve this non-convex problem, we address the user scheduling and power allocation problem and a new iterative algorithm to jointly optimize the user scheduling and power allocation. Eventually, simulation results are carried out to show the benefits of the proposed scheme and discuss the influence of the key system parameters on the EE mmWave NOMA downlink multi-relay system.

In this paper, we study the timely transmission of status updates from multiple sources to an interested receiver. The sources generate updates independently at Poisson rate, which are then coded and transmitted through a shared block fading channel. Age of information (AoI) is an effective indicator to describe the timeliness of transmission by depicting the freshness of information the receiver knows about the interested source. An intuitively age-oriented transmission scheme is sequentially independent transmission, i.e., the transmitter submits the updates one-by-one without waiting. However, this scheme does not always minimize the age. Aiming to improve the age performance of the considered system, we propose a waiting-and-batching transmission scheme. Specifically, earlier generated updates are waiting for late ones and then batching into a long packet before being coded and transmitted. We derive the close-formed expression of the average AoI of the proposed scheme, and conduct extensive numerical comparisons with the independent transmission scheme. Results reveal that our waiting-and-batching scheme can be more beneficial to AoI if: (1) the update generation rate is high; (2) the propagation delay is non-trivial.

For the primary mirror of a large-scale telescope, an electro-hydraulic position control system (EHPCS) is used in the primary mirror support system. The EHPCS helps the telescope improve imaging quality and requires a micron-level position control capability with a high convergence rate, high tracking accuracy, and stability over a wide mirror cell rotation region. In addition, the EHPCS parameters vary across different working conditions, thus rendering the system nonlinear. In this paper, we propose a robust closed-loop design for the position control system in a primary hydraulic support system. The control system is synthesized based on quantitative feedback theory. The parameter bounds are defined by system modeling and identified using the frequency response method. The proposed controller design achieves robust stability and a reference tracking performance by loop shaping in the frequency domain. Experiment results are included from the test rig for the primary mirror support system, showing the effectiveness of the proposed control design.