This paper considers a downlink cell-free network under limited fronthaul capacity, where the fronthaul constraint is modeled as rate-distortion theory. Our objective is to maximize the spectral efficiency (SE) by jointly optimizing user association (UA) and power control (PC). To this end, we first represent the cell-free network as a heterogeneous undirected bipartite graph and then develop a message passing-enhanced heterogeneous Graphormer network (MP-HGraphormer) algorithm. In this algorithm, three graph structural encoding mechanisms are introduced to effectively capture both global and local graph structural information. These mechanisms are specifically designed to exploit the constructed graph, and consequently the computational complexity of these mechanisms is reduced. Furthermore, UA and PC are optimized by applying a threshold-based decision rule according to the joint node and edge outputs of MP-HGraphormer. Simulation results show that the proposed algorithm outperforms traditional Transformer and graph neural network models in improving the SE while achieving better generalization across different numbers of user equipments.

The booming development of 5G and Internet of Things (IoT) technologies significantly promotes the revolution of industrial IoT systems. Age of Information (AoI) is expected to play a critical role in industrial IoT systems, especially for time-sensitive monitoring and control applications. In addition, edge computing (EC) will be leveraged to effectively support industrial tasks in the limited communication and computing resources environment, bringing threefold benefits of shorter end-to-end delay, improving information timeliness, and reduced communication burden. Thus, we propose an edge-assisted co-design architecture of sampling-scheduling-control to improve the overall system performance. Under this architecture, a new definition, Age of Task (AoT), is proposed first to measure the timeliness of multielement and compute-intensive monitoring tasks in the industry. By analyzing the coupling relationship between AoT and control performance, an analytical expression of estimation error based on AoT is derived. Furthermore, we prove that the optimal control law could be expressed in a certain equivalent form, making it possible to decompose the design of control and network resource allocation (sampling, scheduling). According to the relation between AoT and estimation error, a co-design method, event-triggered sampling and max-age-reduce-first scheduling (ETMA), is proposed to minimize the system cost, including control cost and network energy consumption. The simulation results show that our co-design scheme has the optimal system cost among the four state-of-the-art schemes.

This paper investigates the edge caching in maritime wireless networks to cope with traffic surge issue in offshore region. A buoy equipped with wireless cache servers is introduced to provide wireless communication services for ships and users to relieve traffic load of onshore base station(OBS). Due to limited cache capacity and transmit power on the buoy, the transmission delay for ships and users can be increased. To tackle this problem, a joint cache placement and power allocation algorithm is proposed based on the Dinkelbach method and successive convex approximation. Simulation results show that the proposed algorithm can improve the cache hit rate with reducing the system cost.

Edge sensing can achieve high-performance state estimation in industrial IoT systems by supporting task offloading and data processing at powerful edge estimators. Accurate edge sensing depends on low offloading delay. However, it is challenging to decrease offloading delay due to the harsh industrial environment and limited communication-and-computation resources. In this article, a closed-form expressing of estimation error with respect to offloading delay is derived to indicate that adjusting offload delay on demand is necessary for estimation error reduction. Then, we propose an adaptive edge sensing scheme, aiming to minimize estimation error by jointly optimizing task offloading and sensor scheduling. The required optimization is formulated as a mixed-integer nonlinear programming problem and solved by the designed decomposition and approximation methods. Specifically, the maximum matching is used for sensor scheduling to assign the optimal edge estimator for each sensor. The task offloading algorithm is designed based on the inner approximation method to reduce the offloading delay. Finally, simulation results demonstrate that the proposed scheme has superiorities in reducing estimation error compared with centralized sensing and distributed sensing schemes. Moreover, we find an interesting result that estimation error is delay sensitive when the offloading delay is large.

Multivariate-information and computation-intensive tasks play an important role in Wireless Sensor Network Systems (WSNSs), where information freshness has an important impact on the system performance of state analysis. Recently, the Age of Information (AoI) has been studied extensively as a promising metric to evaluate the freshness of state packets. However, most of the existing research focuses on optimizing the average AoI of a single information source, which can not be directly applied to the scenario with multi-source tasks. In this paper, we firstly present a novel definition of the Age of Task (AoT) for edge-assisted industrial WSNSs. Furthermore, the expressions of the sensing time and arrival time that determine the AoT tail distribution are given in detail. Then, we propose an AoT tail violation probability minimization problem to find the optimal sampling rate and give the feasible region of the sampling rate. Since it is difficult to obtain the exacted expression of the formulated problem, the Upper Bound Minimization Problem (UBMP) and the more tractable α-relaxed UBMP are proposed to obtain the near-optimal sampling rate. Finally, simulation results show that the sampling rate obtained by α-relaxed UBMP is nearly optimal for the AoT tail violation probability minimization problem.

In smart ocean, unmanned surface vehicles (USVs) are deployed to monitor the marine environment in a coordinated manner. The ubiquitous situation awareness of marine environment can be achieved by state estimation with the sensory data collected by USVs. Therefore, the transmission performance in terms of packet loss and delay of sensory data plays an important role in the state estimation of marine IoT systems. However, it is challenging to achieve the high-reliable and low-latency transmission for sensory data due to the path loss, spectrum scarcity and transmit power limitation. In this article, we introduce the Age of Information (AoI) to mathematically characterize the impacts of packet loss and transmission delay on the state estimation error. We first explore the relationship between the state estimation error and the AoI of sensory data. We then investigate the co-design of state estimation and sensory data transmission for marine IoT systems. Specifically, a mother ship (MS)-assisted cooperative transmission scheme is proposed to mitigate the impact of limited resources and path loss on the estimation performance. Then, the MS location, channel allocation, and transmit power are jointly optimized to minimize the mean-square error of state estimation, which is achieved by formulating a constrained minimization problem and solving it with the decomposition method. Simulation results demonstrate that the proposed scheme has superiorities in reducing the estimation error and the power consumption.

In smart ocean, multiple unmanned surface vehicles (USVs) are deployed, which generally perform multiple monitoring missions with different requirements of transmission performance. For the monitoring mission, the transmission latency is quite important for marine IoT systems to achieve the ubiquitous situation awareness. However, it is quite challenging due to the location-depended path loss and battery-powered sensors. To address this issue, this paper adopts the Age of Information (AoI) to mathematically express the impact of transmission delay on state estimation, and proposes a mothership assisted cooperative transmission scheme to enhance the estimation performance with limited energy. Moreover, the locations of mother-ships is optimized to minimize the mean squared error of state estimation, which is achieved by formulating a constrained minimization problem and solving it with the decomposition method. Simulation results demonstrate that the proposed scheme could achieve smaller the estimation error.

Sensing data and control commands are frequently exchanged over communication networks for remote data acquisition and distributed control in industrial network control systems. The system performance relies on the design of sensing, transmission, and control. Due to the harsh environment in industrial field and the limited network resources, it is very challenging to meet the high requirement on transmission reliability for remote feedback control. In order to enhance the transmission ability for this kind of systems, an edge-assisted system architecture is proposed for the sensing and control processes. The parameter estimation for the sensing process is executed in the so-called edge estimator, and the controller is designed in a remote control center. Under this architecture, in this article an on-demand transmission scheme is designed by characterizing the overall effects of transmission reliability on the estimation and control performance. The overall system is optimized by formulating a revenue-cost maximization problem subject to the constraints of system stability, estimation convergence, spectrum utilization, and energy budget. The formulated mixed-integer nonlinear programming problem can be effectively solved with the block coordinate descent method. It is decomposed into two subproblems over disjoint variable sets. Simulation results demonstrate the advantages on both network-wide revenue and control-transmission cost.

State estimation plays an important role for the situation monitoring in industrial network systems, where multiple sensors observe a dynamical process and deliver state information to the remote center over wireless channels. However, the lossy wireless channels make the state information received by remote center be intermittent. Moreover, the scarcity of radio resources makes it challenging to simultaneously schedule a large number of sensors. In practice, different sensors usually have distinct contributions on state estimation, thus this paper firstly characterizes the integrated impact of sensing ability and transmission capacity on the state estimation performance, based on which a sensing aware opportunistic transmission scheme is then proposed. At each discrete time instant, the remote center determines which sensors to schedule based on the estimation demand and radio resources, and each sensor decides whether to participate the data transmission according to its residual energy. In order to further enhance the estimation performance and resource efficiency, the transmission scheduling and the sensor participation are jointly optimized by formulating a network-wide revenue maximization problem. This mix-integer nonlinear programming problem is effectively solved with the Dinkelbach method and heuristic algorithm. Finally, numerical simulation results verify the scheme efficiency.

In industrial automation, monitoring information is critical and expected to be received with ultra- high reliability and low latency. On the other hand, different industrial monitoring applications usually have diverse requirements on the transmission quality. This paper investigates the deadline aware reliable transmission in mission-critical machine-type communications (MTCs) to satisfy the service requirements for different monitoring applications in terms of reliability and latency. Specifically, a hybrid non-orthogonal multiple access (NOMA) framework is firstly introduced for improving spectrum utilization as well as meeting the diverse requirements of different applications. Under this framework, a NOMA-assisted small-packet transmission scheme is proposed for mission- critical MTCs, and the related performance is then mathematically formulated as a constrained optimization problem with the objective to maximize the network-wide revenue. The formulated non- trivial problem is effectively solved by considering the diverse requirements of different applications. Simulation results are provided to demonstrate both the impact of small packet size and the superiority of NOMA technique on the network-wide revenue improvement.

This paper considers the multi-loop wireless control system (WCS), where control command is delivered from the remote controller to multiple actuators over shared wireless channels. However, different system dynamics of multiple loops make each loop usually have different demands on the success probability of receiving control commands. Thus, the control performance of overall system is affected by both the transmission reliability and the dynamics of each loop. In this paper, we propose a demand-driven and energy-efficient transmission strategy to adaptive to wireless channels and system dynamics. In order to improve the control performance without burdening the scarce spectrum resources, the remote controller is equipped with multiple antennas, and the transmit beamforming design with power control is adopted to improve the success probability of control commands. In particular, we firstly characterize the control performance of each loop with a pre-defined Lyapunov function, which would decrease exponentially in expectation if the packet loss rate meets the stability condition of each loop. Then, a control stability constrained optimization problem is formulated to minimize the overall cost including energy consumption and linear quadratic Gaussian control cost. The non-trivial probabilistic constraint is effectively handled with the differential accumulation and difference-convex methods. Finally, simulation results verify that the proposed strategy has superiority on reducing control cost and energy consumption without considerations of system dynamics or joint design of transmit beamforming and power control.

Driven by mission-critical applications in modern industrial systems, the 5th generation (5G) communication system is expected to provide ultra-reliable low-latency communications (URLLC) services to meet the quality of service (QoS) demands of industrial applications. However, these stringent requirements cannot be guaranteed by its conventional dynamic access scheme due to the complex signaling procedure. A promising solution to reduce the access delay is the pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, which however may lead to low spectrum utilization if the allocated resource blocks (RBs) are not used. In this paper, we aim to address this issue by developing DPre, a predictive pre-allocation framework for uplink access scheduling of delay-sensitive applications in industrial process automation. The basic idea of DPre is to explore and exploit the correlation of data acquisition and access behavior between nodes through static and dynamic learning mechanisms in order to make judicious resource per-allocation decisions. We evaluate the effectiveness of DPre based on several monitoring applications in a steel rolling production process. Simulation results demonstrate that DPre achieves better performance in terms of the prediction accuracy, which can effectively increase the rewards of those reserved resources.

The wide application of Internet of Things (IoT) in industrial automation encourages the emergence of a new paradigm of industrial IoT systems, wireless control system (WCS), where the system and/or control information is delivered over wireless channels. In practical systems, WCSs would consist of multiple control-loops in general, the resource competition among which would seriously increase mutual interferences and transmission collisions, making it is difficult to provide the required transmission reliability for the control strategy. To address this issue, we design the control strategy together with the hybrid cooperative transmission scheme for multiloop WCSs in a proactive way. We first define the overall system cost function to explore the impacts of standard linear quadratic regulator control cost and wireless transmission reliability on the control performance. In order to further minimize the overall system cost while guaranteeing the control stability, we then propose a control performance aware cooperative transmission scheme, which is formulated as a constrained optimization problem. Decomposition method and heuristic algorithms are designed based on the feature of network structure to solve the formulated mixed integer nonlinear programming problem efficiently. Finally, simulation results demonstrate that by using the proposed strategy, the overall system cost is significantly reduced, decreasing by 78% and 82% compared to the cases without considerations of system dynamics and without cooperative transmission, respectively.

In industrial automation, the state of process control could be monitored by spatially distributed sensors and 5G machine-type communication (MTC) enabled industrial Internet of things (IIoT). Thus, ultra-reliable MTC and high-accurate state estimation play important roles for ensuing system stabilization. However, it is challenging due to complex industrial wireless environments and limited communication resources. To address this issue, this paper first presents a transmission-estimation codesign framework to lay down the foundation for guaranteeing the prescribed estimation accuracy with limited communication resources. Under this framework, a hierarchical transmission-estimation approach is proposed to improve the transmission reliability and estimation accuracy according to system dynamics. The proposed approach is then optimized by formulating a constrained minimization problem, which is mixed integer nonlinear programming and solved efficiently with a block-coordinate-descent-based decomposition method. Finally, simulation results demonstrate that the proposed approach has superiorities in improving both the estimation accuracy and the energy efficiency.

The wide application of Internet of Things (IoT) in industrial automation leads to the emergence of a new paradigm of industrial IoT systems, namely wireless control system, where control commands are transmitted from the remote controller to multiple actuators over shared wireless channels. Considering system stability, distinct subsystems usually have different requirements on the transmission quality of control commands due to different system dynamics. In this paper, we aim to simultaneously guarantee the stability of all subsystems and minimize the weighted sum of control cost and transmission cost. To this end, the maximum tolerated packet loss rate of each subsystem is first characterized by a pre-defined Lyapunov function. Then, based on channel conditions and system dynamics, a beamforming-assisted hierarchical coordinated transmission strategy is proposed to alleviate the impact of unreliable transmission on the control performance. The control performance and energy efficiency are further optimized by formulating an overall cost minimization problem constrained by the system stability. Both the differential accumulation and the difference-convex methods are employed to effectively deal with the constraint that is expressed in an implicit probabilistic form. Finally, simulation results demonstrate that the proposed strategy has the advantages of reducing control cost and energy consumption.

In the performance analysis and control of wireless control systems, the high accuracy state estimate is a necessary prerequisite for feedback control. However, it is challenging due to the server interference wireless environment as well as limited spectrum and energy resources. To address this issue, this paper firstly presents a hierarchical framework to lay the foundation to guarantee the prescribed estimation accuracy for WCSs with minimal resource consumption. Under this framework, a resource-efficient hierarchical transmission-estimation co-design approach is proposed to adapt to the system dynamics and communication resources for improving the transmission reliability and reducing the energy consumption. Then, a joint optimization problem subject to constraints of estimation convergence, transmission reliability and resource limitation is formulated to minimize the overall estimation-energy cost for WCSs in terms of estimation error and energy consumption. The solution of mixed integer nonlinear programming problem is got efficiently and effectively with decomposition methods. Finally, simulation results demonstrate that the developed hierarchical transmission-estimation co- design approach has superiorities on improving the estimation accuracy and the resource efficiency. The estimation-energy cost caused with the proposed approach is about 51% and 77% of that with two compared ones without awareness of wireless environment variations and system dynamics, respectively.

In industrial cyber-physical systems (ICPSs), state estimation provides the best possible approximation for the unmeasurable system state based on the received measurements from sensors via lossy wireless channels. As a result, the estimation performance heavily depends on the transmission reliability. In this paper, a cognitive radio assisted cooperative transmission scheme is proposed to improve the accuracy of state estimation by delivering necessary redundant measurements to the remote estimator. The relationship between the accuracy of multi- sensor state estimation and the arrival rate of measurements is explored. Based on this, an optimization problem is formulated to minimize the state estimation error by jointly allocating the harvested licensed channels and the ISM channels with power control and admission control. A sub-optimal decomposition scheme is proposed to solve this intractable problem efficiently. Numerical results demonstrate that the proposed scheme significantly outperforms existing schemes by reducing more than 73% packet loss rate and 56% estimation errors.

State estimation, which computes the best possible approximation for the system state based on the perceived information transmitted from sensors to the estimators, is vital for control system performance in industrial cyber-physical systems (ICPSs) with the integrated techniques of control, communication and computing. Thus the performance of state estimation relies on the communication reliability. In order to improve the reliability, redundant channels/slots are reserved for the data transmission in industrial wireless techniques, such as WirelessHART. However, the redundancy scheme burdens the increasingly over-crowded ISM spectrum band due to the envisioned emerging ubiquitous industrial wireless monitoring in the architecture of ICPS in the near future. The cognitive radio (CR) technology can intelligently explore the available spectrum opportunities on licensed channels, and it motivates this paper to consider the redundant transmission through the opportunistically available licensed channels to guarantee the transmission reliability for state estimation. Unfortunately, spectrum sensing takes extra energy consumption, thus it is necessary to take into account the energy efficiency for the battery-powered IWSN. Then a CR enabled energy- efficiency maximization problem is formulated by regarding the convergence of state estimation as a constraint of the resource allocation problem. In order to solve the non-convex and mixed integer programming, the Dinkelbach and Lagrangian relaxation techniques are adopted to transform the problem into a convex programming and furthermore reduce the computational complexity. Numerical results demonstrate that the CR technology can significantly release the spectrum for the redundancy design from the ISM band while guarantee the reliability for the effective state estimation.