The coming industrial internet of things (IIoT) era is anticipated to see the proliferations of goal-oriented applications in real-time wireless control systems. In such systems, a low processing latency is required to guarantee the timely transmission of control information. To achieve this goal, this paper proposes a new integrated sensing, computing, communication, and control$(\text{ISC}^{3})$architecture, where semantic communications are adopted to make sensible semantic inference (SI) for the control information over time. In particular, we introduce a new criterion, i.e., mutual information (MI), for control performance evaluation by drawing from the field of wireless communications. We calculate the MI by designing a semantic feature extractor (SFE) module at the transmitter (Tx) to identify the semantic correlation among its sensed control information over time, thereby adjusting the frequency of its control information transmission. Meanwhile, a semantic feature reconstructor (SFR) module is employed at the receiver (Rx) to predict the current control information based on its previously received information if there is no control information transmission from the Tx. Finally, on-site experimental results are provided, showing that our proposed scheme can significantly reduce the communication overhead while improving the control performance significantly.

By providing cost-efficient flexibility beyond wired control systems, real-time wireless control systems (WCSs) are pivotal in industrial internet of things (IIoT), which can enable massive emerging applications in IIoT, e.g., autonomous driving, remote medical, teleoperation, etc. To guarantee good control performance over wireless networks, ultra-reliable and low-latency communications (URLLCs) are required from communication perspective. However, this would result in frequent high-rate data transmission, leading to extremely high resource consumption or even network congestion, which impedes the application of WCSs in IIoT. This paper proposes a novel co-design method for communication and control in WCSs, which offers a new strategy to address the challenges introduced by URLLC. Specifically, we first formulate an optimization problem aiming to minimize the communication and control cost by jointly optimizing transmission trigger, communication power, and Lyapunov cost, which, however, is NP-hard. To address this challenge, we employ a two-stage strategy by first defining a new metric, i.e, state-to-error ratio (SER), as a trigger condition to evaluate control performance. Based on this metric, we analyze the relationship between SER and signal-to-noise-ratio (SNR) and show their hidden consistency in evaluating both communication and control performance, thus facilitating our communication and control co-design. Subsequently, we establish a closed-form relationship between the control convergence rate and communication reliability and thereby obtain the optimal transmit power to ensure the overall system performance. Finally, simulation results are provided to demonstrate the efficacy of our proposed method.

By driving automation devices to achieve allocated tasks, real-time wireless feedback control systems can be treated as a key enabler for task-orientated applications in Industrial Internet of Things (IIoT). In such a system, timely information from wireless communication aspect is one of the most important components affecting control performance, e.g., control stability and tracking error, where the effect is determined by information update policy. In this article, we propose a novel information update scheduling policy for the aforementioned systems. Specifically, based on the existing research on the Age of Information (AoI) evaluating the timeliness of the information, we analyze the relationship between AoI and control performance, where we find that minimizing AoI is not always equivalent to maximizing control performance. Then, we adopt two metrics, i.e., Age of Stale Information (AoSI) and the Value of Information (VoI), to evaluate the timeliness of information update for stochastically stable control and VoI is for completely stable control, respectively. More importantly, we analyze the relationship between AoSI and VoI, where minimizing AoSI is a subset of maximizing VoI. Then, VoI is adopted as the unique metric for both the aforementioned systems. Finally, we propose an$\alpha $-wait policy for maximizing VoI. In such a policy, an optimal waiting time interval$\alpha $can be obtained to maximize control performance and meanwhile reduce communication resource consumption. The simulation results show that the proposed method can reduce more than 68% channel occupied probability from communication perspective and meanwhile reduce almost 20% mean-square-error (MSE) from control perspective compared with conventional methods.

Due to the ultra-high transmission rate and resolution, integrated sensing and communications (ISAC) is promising to be achieved in terahertz (THz) frequency, which is treated as one of the most important technologies to enabled the development of autonomous systems in the coming sixth generation cellular communications (6G). In this paper, we investigate orthogonal time frequency space (OTFS) waveform-based ISAC in THz multi-input multi-output (MIMO) communications. To deal with the extremely high complexity in the traditional methods, we propose a tensor decomposition based THz channel estimation method in OTFS for ISAC with significantly low algorithm complexity. Then, the real-time requirement for the decision making based on ISAC in autonomous systems can be guaranteed. Specifically, we consider the THz downlink transmission from a base station (BS) to a mobile station (MS), where both of them are equipped with large-scale antenna arrays. OTFS modulation waveform is adopted for synchronous communication and sensing. Considering the sparse THz channel and the multidimension of the received multichannel signals, we propose an OTFS channel parameter estimation method based on CANDECOMP/PARAFAC (CP) decomposition, where the received signal is expressed as a third-order tensor. The tensor has a form of a low-rank CP decomposition, and satisfies the uniqueness of CP decomposition. Then, the estimated value of the channel parameter estimation can be obtained by the factor matrix obtained with CP decomposition. In addition, we analyze the algorithm complexity of the proposed method. Simulation results show the performance of the proposed method.

Terahertz (THz) communications are expected to provide ultra-high transmission rate to enable connected automatic guided vehicles (C-AGV) in future smart factories for the industrial internet of things (IIoT). However, beam alignment is extremely challenging due to its narrow beam-width and the fast mobility of C-AGV. To deal with this issue, we propose a novel communication-aware motion control scheduling method for THz beam alignment in this paper. Specifically, the accuracy state-feedback of the C-GAV’s motion control is very high, which has the potential to provide the necessary information for beam alignment, e.g., the location of the receiver. However, the feedback needs to be carefully designed not to increase the control consumption and meanwhile to maintain the beam alignment for the required transmission data rate. Thus, this paper adopts transmission data rate as the activation criterion for motion control of C-AGV, where the feedback is synchronous with the motion control activation. Based on that, the beam alignment maintaining the required transmission data rate can be maintained. Meanwhile, the control consumption can be significantly reduced, and control stability can be guaranteed. Finally, we obtain a closed-form expression for the threshold of the transmission data rate. Simulation results show remarkable performance gain of the proposed method.

In Industrial Internet of Things (IIoT), the conventional event-triggered control method requires sensors continuously monitoring control states in periodical time. This would consume a huge amount of energy resource, which impedes the application of such conventional methods since the sensors are powered by batteries in most cases. To deal with this issue, this article proposes a new state-to-noise-ratio (SNR)-based transmission scheduling method from a wireless communication perspective. Specifically, we first adopt an additive white Gaussian noise (AWGN) channel model to represent the event-triggered control model, by which we can convert the original event-triggered control problem into a wireless transmission scheduling problem from the sensor to the controller. Based on that, the mutual information is proposed to measure the value of the control states. More importantly, a new control SNR is defined based on mutual information. Using control SNR as the transmission activating criterion, the perfect state observation assumption and sensor’s monitoring with periodical time in traditional event-triggered control are no longer needed. Furthermore, we provide an analytical expression of dynamic SNR thresholds to determine whether the transmission should be activated. Finally, we prove the mean-square stability of our proposed method. Numerical comparisons are provided to demonstrate the effectiveness of our proposed method.

As promising candidate frequency bands, millimeter wave (mmWave) and terahertz (THz) communications can provide ultrahigh transmission rate to enable vehicle-to-everything (V2X) networks for connected autonomous vehicles (CAVs). However, beam alignment is extremely challenging in mmWave/THz communications due to its narrow beam width and fast mobility of CAV. In this article, we propose a new joint communication and control algorithm for beam alignment, where the mutual positive effect of communications and motion control of CAV on each other is discussed. Specifically, we first provide a framework to show the interaction between motion control of CAV and beam alignment of transmission from base station (BS) to CAV. Then, we analyze the effect of CAV control on beam alignment in communications, where a theorem is obtained to show the closed-form expression of their relationship. Finally, we discuss the CAV control design affected by beam alignment. Simulation results show remarkable performance of the proposed method.

By providing ultra-high transmission data rate, millimeter wave (mmWave) and terahertz (THz) communications are promising to enable backhaul data transmission in cellular connected unmanned aerial vehicle (UAV) networks. In such networks, with little or no human assistance, connected autonomous UAVs (CA-UAV) can build air-ground networks and achieve seamless wide-area coverage. With the usage of high frequency (i.e., mmWave/THz), radio/radar sensing function is expected to be achieved in wireless networks, which can be used to track UAV for beam tracking in mmWave/THz communications and motion control of UAV. However, it is extremely difficult to jointly design sensing, communication, and motion control since they have been developing in relatively parallel with limited intersections. In this paper, we propose a new integrated scheduling method of sensing, communication, and control for mmWave/THz communications in UAV networks to enable data transmission of the backhaul from UAV to the ground base station (BS). In the proposed method, we first analyze the interactions among sensing, communication, and motion control, where sensing and motion control are strongly coupled to form the sensing-control pattern. Then, we provide a new definition from motion control perspective, i.e., state-to-noise-ratio, which links the relationship between sensing-control pattern activation and data rate determined by beam alignment in mmWave/THz communications. Finally, a closed-form expression is obtained for data rate triggered sensing-control pattern activation design, where both data rate requirement in mmWave/THz communications and motion control performance of UAV are guaranteed. Simulation results show remarkable performance of the proposed method.

In industrial internet of things (IIoT), ultra-reliable and low-latency communication (URLLC) is proposed to guarantee the requirement of real-time wireless control systems in worst case, so as to maintain the system working in all cases. However, it is extremely challenging to maintain URLLC throughout the whole control process due to the scarcity of wireless resource. This paper develops an autonomous device-to-device (D2D) communication scheme by jointly considering reliability in URLLC and control requirement. In the proposed scheme, we consider the actual control requirement, i.e., control convergence rate, into communication design, where we find that it can be converted into a constraint on communication reliability. Then, the communication reliability constraint comes from control aspect, instead of URLLC, which leads to that the system does not need to guarantee worst case in URLLC. Second, the sensors autonomously decide whether to be activated with optimal probabilities to participate in the control process, which can maintain the communication reliability requirement with significantly less resource consumption. Simulation results show remarkable performance gain of our method. For instance, compared with fixed activation probability 40% only considering URLLC, the average power consumption of the proposed method can be reduced by at most about 100%.

In real-time feedback control systems, the freshness of the packet is crucial to control performance, where packet management is vital to keep data fresh. Recently, age of information (AOI) has been used to measure the freshness of the update information, where minimizing AOI becomes popular in system designs. In this paper, we find that minimizing AOI is not always equivalent to maximizing the control performance. In particular, we define a metric, called the age of stale information (AOSI), to link the instability of the control system to AOI. By minimizing AOSI, we can maximize the control performance, and also reduce the communication cost.

Ultra-reliable low-latency communication (URLLC) is critical to wireless control systems. In this paper, we propose a communication-control co-design method to deal with stringent reliability requirements. In particular, we adopt relay-assisted method to improve communication reliability. More importantly, packetized predictive control (PPC) is adopted to solve the problem of relay system, where the data transmission rate of the relay link is twice as that of the direct link, and this leads to the packet loss probability increasing. Furthermore, we explore the tradeoff between energy consumption of relay and packet length of PPC to maximize reliability. Simulation results verify the performance of the proposed method.

Ultra-reliable and low-latency communication (URLLC) is promising to enable real-time wireless control systems for tactile internet. In such a system, it is difficult to maintain extremely high quality-of-service (QoS) in URLLC for real-time control. In this paper, we develop a probability-based device-to-device (D2D) scheme to deal with this issue, where communication and control are jointly considered. In our scheme, the transmitters autonomously decide whether to be active to participate in the control process of the receiver based on a certain probability, which can significant reduce the interacting communication latency between them, lower the transmission power consumption, and improve communication reliability. Compared with traditional D2D transmission method, simulation results show remarkable performance gain of our method.

As one of the most important communication scenarios in the comingfifth generation (5G) cellular networks, ultra-reliable and low-latency communication (URLLC) is promising to enable real-time wireless control systems. However, one of the biggest challenges is that how to integrate URLLC and control performance together to maximize the overall system performance. In this paper, we investigate the resource allocation for URLLC uplink in real-time wireless control systems. Specifically, we first discuss the relationship between communication and control performance. Based on that, we convert the hybrid co-design problem into a regular wireless resource allocation problem. Then, we propose an iteration algorithm to obtain the optimal wireless resource allocation. Simulation results indicate the performance of our method.

Ultra-reliability and low-latency will be two topics of most concern in the wireless communication to realize the industrial automation during the next few decades. In this paper, we consider a uplink transmission model where massive machine-type-communication (MTC) devices aim to transmit their generated data to the BS under a strict Quality-of-Service (QoS) constraint on reliability and latency. Guaranteeing the strict constraints on transmission reliability and latency is the most significant thing we consider during the data transmission process. The achievable rate based on finite blocklength channel coding is adopted to characterized the decoding reliability at BS. We see the delay-sensitive area spectral efficiency (DASE) as a performance metric, and our goal is to find a optimal resource allocation policy to maximize the DASE while guaranteeing the strict QoS constraint on reliability. Numerical results show that the optimization strategy proposed in this paper can improve the DASE performance to a great extent than the optimization strategy which minimizes the total bandwidths allocated to devices.

Since it is challenging to position the noncooperative receiver (Rx), especially when the backward frequency band of the Rx cannot be obtained by the anchors. In this paper, we propose a novel noncooperative Rx positioning method using full-duplex relay technique, where the anchors act as full-duplex relays for the Rx. It can trigger the close-loop-power-control (CLPC) between the transmitter (Tx) and the Rx, which contains the information of the Rx's location. By measuring the received signal from the Tx, the anchors can estimate the location of the Rx. Simulation results demonstrate the performance of the proposed Rx positioning method, and the root-mean-square-error (RMSE) can reach about 30%, which is similar to the conventional Tx positioning using received-signal-strength (RSS).

In frequency duplex division systems, it is very challenging to position the receiver (Rx) that belongs to the third-party system since the anchors usually do not know which frequency band the Rx uses for transmission. As a result, they can only use the received signal from the third-party transmitter (Tx) to position the Rx. In this paper, we propose a relay-based positioning method to estimate the location of the third-party Rx. In our method, we let each anchor alternatively act as a full-duplex amplify-and-forward (AF) relay for the Rx, which artificially creates a relay path. By estimating the time-difference-of-arrival (TDOA) between the direct and relay paths, the anchors can estimate the Rx location based on the received signal from the third-party Tx. Simulation results indicate the effectiveness of the proposed method.

In cognitive radio networks, the location information of the primary receiver is critical for underlay spectrum sharing. However, positioning the primary receiver in frequency division duplex (FDD) systems is very challenging. In this paper, we propose a novel method to position the primary receiver using the full-duplex amplified-and-forward (AF) relay technique. Simulation results indicate that the proposed method can obtain the same level of estimation error compared with the conventional received signal strength (RSS)-based transmitter positioning methods.

In wireless positioning, estimating the location of a receiver is very challenging since the receiver does not transmit signals. The difficulty is to obtain the distance between the anchors and the silent receiver. To deal with the issue, this paper proposes to use the full-duplex relay technique to estimate the anchor-receiver distance. Then, we can obtain the location of the silent receiver. With the proposed method, the location-aware applications are not limited to the conventional transmitter positioning. Instead, they can be extended to the receiver positioning. Simulation results demonstrate the performance of the proposed method.