In recent decades, cyber-physical systems (CPSs) have received great attention due to their broad applications. This paper investigates CPS deployment in remote areas, specifically focusing on a digital twin-empowered unmanned robotic system. The system consists of a multifunctional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that connects sensors and actuators—forming reflex-arc-like sensing-communication-computing-control (SC3) loops. A digital twin is integrated into the EIH to emulate the system’s behavior and assist in the decision-making. To alleviate resource limitations in remote areas, we propose a goal-oriented closed-loop optimization scheme. The proposed scheme takes the SC3loop as an integrated structure and jointly optimizes uplink and downlink (UL&DL) communication and computing resources to minimize the total linear quadratic regulator (LQR) cost. To address the non-convex optimization problem, we derive the closed-form solution for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate the superiority of the proposed scheme, which achieves a two-tier task-level balance within and across the SC3loops.

In unmanned space exploration, the cooperation among space robots requires advanced communication techniques. In this paper, we propose a communication optimization scheme for a specific cooperation system named the “mother-daughter system”. In this setup, the mother spacecraft orbits the planet, while daughter probes are distributed across the planetary surface. During each control cycle, the mother spacecraft senses the environment, computes control commands and distributes them to daughter probes for actions. They synergistically form sensing-communication-computing-control ($\mathbf {SC^{3}}$) loops. Given the indivisibility of the$\mathbf {SC^{3}}$loop, we optimize the mother-daughter downlink for closed-loop control. The optimization objective is the linear quadratic regulator (LQR) cost, and the optimization parameters are the block length and transmit power. To solve the nonlinear mixed-integer problem, we first identify the optimal block length and then transform the power allocation problem into a tractable convex problem. We further derive the approximate closed-form solutions for the proposed scheme and two communication-oriented schemes: the max-sum rate scheme and the max-min rate scheme. On this basis, we analyze their power allocation principles. In particular, for time-insensitive control tasks, we find that the proposed scheme demonstrates equivalence to the max-min rate scheme. These findings are verified through simulations.

This paper investigates an emergency rescue system, which comprises a multi-functional unmanned aerial vehicle (UAV) and multiple robots. The UAV carries sensing, communication, and computing modules. It senses system states, calculates commands, and instructs field robots to take actions. In this way, the UAV and robots form multiple sensing-communication-computing-control $(\mathbf{SC} ^{3})$ loops, which could finish many mission-critical tasks without human participation. To activate these $\mathbf{SC} ^{3}$ loops, we propose a closed-loop optimization scheme. Unlike traditional studies that primarily focus on the communication link, the proposed scheme emphasizes the $\mathbf{SC} ^{3}$ loop and adopts the linear quadratic regulator (LQR) cost as the objective. Focusing on the UAV-robot downlink, we model the data transmission in the finite block length regime and take the transmit power and block lengths as optimization variables. We solve the nonlinear integer problem by exploiting the monotonicity and convexity of the objective rate-cost function. The closed-form solution of the transmit power is derived in the assure-to-be-stable region. On this basis, we compare the proposed scheme with the max-sum rate scheme. Through comparisons, the fairness-minded nature of the proposed scheme is revealed.

The sixth-generation (6G) network is envisioned to integrate communication and sensing functions, so as to improve the spectrum efficiency and support explosive novel applications. Although the similarities of wireless communication and radio sensing lay the foundation for their combination, there is still considerable incompatible interest between them. To simultaneously guarantee the communication capacity and the sensing accuracy, the multiple-input and multiple-output (MIMO) technique plays an important role due to its unique capability of spatial beamforming and waveform shaping. However, the configuration of MIMO also brings high hardware cost, high power consumption, and high signal processing complexity. How to efficiently apply MIMO to achieve balanced communication and sensing performance is still open. In this survey, we discuss joint communication and sensing (JCAS) in the context of MIMO. We first outline the roles of MIMO in the process of wireless communication and radar sensing. Then, we present current advances in both communication and sensing coexistence and integration in detail. Three novel JCAS MIMO models are subsequently discussed by combining cutting-edge technologies, i.e., cloud radio access networks (C-RANs), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs). Examined from the practical perspective, the potential and challenges of MIMO in JCAS are summarized, and promising solutions are provided. Motivated by the great potential of the Internet of Things (IoT), we also specify JCAS in IoT scenarios and discuss the uniqueness of applying JCAS to IoT. In the end, open issues are outlined to envisage a ubiquitous, intelligent, and secure JCAS network in the near future.

Current fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are sparsely distributed over the vast ocean. The nearshore network also shares the spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, and different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting max-min optimization, the auxiliary function method, and the successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown to be effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.

Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial fifth-generation networks. However, satellites are restricted by their high latency and relatively low data rate. Consequently, the integration of terrestrial and satellite components has been widely studied to take advantage of both sides and enable the seamless broadband coverage. Due to the significant differences between satellite communications (SatComs) and terrestrial communications (TerComs) in terms of channel fading, transmission delay, mobility, and coverage performance, the establishment of an efficient hybrid satellite-terrestrial network (HSTN) still faces many challenges. In general, it is difficult to decompose an HSTN into a sum of separate satellite and terrestrial links due to the complicated coupling relationships therein. To uncover the complete picture of HSTNs, we regard the HSTN as a combination of basic cooperative models that contain the main traits of satellite-terrestrial integration but are much simpler and thus more tractable than the large-scale heterogeneous HSTNs. In particular, we present three basic cooperative models, i.e., model X, model L, and model V, and provide a survey of the state-of-the-art technologies for each of them. We discuss future research directions toward establishing a cell-free, hierarchical, decoupled HSTN. We also outline open issues to envision an agile, smart, and secure HSTN for the sixth-generation ubiquitous Internet of Things.