In this article, we study the system-level performance of cell-free massive multiple-input multiple-output (CF-mMIMO) systems with reciprocity calibration errors (RCEs) and imperfect phase synchronization due to the local oscillator (LO) phase drift. Considering the practical implementation of a remote radio unit (RRU) and the nonreciprocity of downlink–uplink channels due to RCEs and LO phase drift, we model the downlink channel with a random phase rotation. Then, we study the system-level performance of both centralized and distributed CF-mMIMO architectures with maximum ratio transmission (MRT) and zero-forcing (ZF) precoding schemes. We derive the closed-form expressions for the signal-to-interference-plus-noise ratio of the downlink precoding schemes, and obtain the closed-form expressions for the average spectral efficiency (SE) per user with locations of both RRU and user equipment (UE) following the Poisson point process. Simulation results show that the closed-form expressions are accurate when compared with Monte Carlo results. The results demonstrate that the impact of RCEs and LO phase drift on system performance is more severe for centralized ZF precoding than for MRT and distributed ZF precoding. In addition, we reveal the SE performance concerning antenna configuration of RRU, RRU density, UE density, and LO phase drift range.

Mission engineering (ME) is an emerging approach to designing and analyzing configurations of system-of-systems (SoS) for accomplishing one or more missions. ME seeks to flexibly leverage SoS capabilities and dynamically adapt their configuration to meet evolving mission needs. SoS configurations today, however, remain static and are carefully designed to accomplish a mission. The problem we address in this article is developing modeling and analysis techniques for flexible integration and adaptive selection of potential SoS configurations to achieve multiple missions with an overarching agility in the execution space. We propose a foundational framework for ME, complete with semantics and grammar to represent the ME design space (MEDS), along with a set of logical and mathematical modeling approaches that lends the MEDS to robust SoS analytical methods. Specifically, the framework proposes development of a mission-focused ontology and domain-specific language to enable consistent semantic representation of MEDS, which is then logically evaluated for spatial and temporal consistency in forming SoS configurations using set-based design principles and Allen's interval algebra. The resulting feasible SoS configurations are then evaluated for mission success using graph theory and multiattribute utility theory. The application of the framework is demonstrated on a simplified and notional sense-decide-effect problem for flexibly accomplishing multiple missions with SoS.

Light detection and ranging (LiDAR) technology's expansion within the autonomous vehicles industry has rapidly motivated its application in numerous growing areas, such as smart cities, agriculture, and renewable energy. In this article, we propose an innovative approach for enhancing aerial traffic monitoring solutions through the application of LiDAR technology. The objective is to achieve precise and real-time object detection and tracking from aerial perspectives by integrating unmanned aerial vehicles with LiDAR sensors, thereby creating a potent Aerial LiDAR (A-LiD) solution for traffic monitoring. First, we develop a novel deep learning algorithm based on pointvoxel-region-based convolutional neural network (RCNN) to conduct road user detection. Then, we implement advanced LiDAR fusion techniques, including raw data fusion and decision data fusion, in an endeavor to improve detection performance through the combined analysis of multiple A-LiD systems. Finally, we employ the unscented Kalman Filter for object tracking and position estimation. We present selected simulation outcomes to demonstrate the effectiveness of our proposed solution. A comparison between the two fusion methods shows that raw point cloud fusion provides better detection performance than decision fusion.

Reuse of cyber-physical modules is critical to accelerate the development times and reduce design costs of cyber-physical systems (CPS). Successful module reuse depends on the ability to mitigate interoperability problems. Over the years, different communities have worked to understand and mitigate these problems leading to different interoperability models, oftentimes tied to particular domains. One important question, tackled in this article, is whether these models can help with the design of CPS. Specifically, this article evaluates a real-world example of a CPS against the levels of conceptual interoperability model (LCIM). The approach followed leverages natural language processing techniques to narrow an initial set of system changes down to a subset of problems, some of which are interoperability problems. This effort enables a comparison between the empirical evidence found and the interoperability model considered. The results obtained show that the observed interoperability problems fit well under the multilevel categories proposed by the LCIM. Furthermore, the LCIM seems to provide a complete description for the interoperability problems found, checking for fit and completeness. A significant impact of the results obtained is the enabling of more focused discussions of the key attributes of said problems and subsequent identification and assessment of the effectiveness of existing mitigation approaches.

The demand for various types of services is growing rapidly with the development of beyond 5G/6G networks, network slicing (NS) is considered as an effective technology to cope with the multiple services and large traffic demand. In this article, a NS resource allocation optimization algorithm based on multiactor-attention-critic (MAAC) joint with bidding is proposed to guarantee the service satisfaction rate (SSR) while increasing the profit of mobile virtual network operator (MVNO) in heterogeneous integrated networks. First, a pricing and bidding strategies are designed for users with different service requirements and service indexes, and the resource allocation is modeled to maximize the sum of utility of all MVNO subjected to MVNO's pricing and bandwidth constraints accordingly. Second, the optimization problem is analyzed based on the augment Lagrange method, relaxed and has been proved as a convex optimization, and then, the alternating direction multiplier method is adopted to obtain the theoretical upper bound with 32.047 of the network utility. Meanwhile, the gradient descent method with different learning rates is used to accelerate the convergence rate. Third, the MAAC-based algorithm is proposed and the resource allocation procedures are transformed into a partially observable Markov decision process, in which the interactions with multiagent environment are performed accurately. Finally, the simulation results indicate that the network utility of the proposed algorithm can be improved by 25.074% while ensuring the users' SSR. Compared with multiagent deep determination strategy gradient and dueling deep Q network, the network utility by the proposed algorithm can be improved by 6.265% and 39.791%, respectively, up to 27.664, which can be closest to the theoretical upper bound at the greatest extent.

The Internet of Things is shaping the next generation of cyber–physical systems to improve the future industry for smart cities. It has created novel and essential applications that require specific network performance to enhance the quality of services. Since network performance requirements are application-oriented, it is of paramount importance to provide tailored solutions that seamlessly manage the network resources and orchestrate the network to satisfy user requirements. In this article, we propose ELISE, a reinforcement learning (RL) framework to optimize the slotframe size of the time slotted channel hopping protocol in IIoT networks while considering the user requirements. We primarily address the problem of designing a framework that self-adapts to the optimal slotframe length that best suits the user's requirements. The framework takes care of all functionalities involved in the correct functioning of the network, while the RL agent instructs the framework with a set of actions to determine the optimal slotframe size each time the user requirements change. We evaluate the performance of ELISE through extensive analysis based on simulations and experimental evaluations on a testbed to demonstrate the efficiency of the proposed approach in adapting network resources at runtime to satisfy user requirements.

In this article, we study the secure communication in unmanned aerial vehicle (UAV) aided and reconfigurable intelligent surface (RIS) empowered multiuser networks. In the proposed configuration, we consider UAV-mounted RIS to reflect the signal between the base station and the legitimate users in the presence of eavesdroppers. The problem is formulated as secrecy rate maximization with channel uncertainty constraints, which is nonconvex. Therefore, we decouple it into beamforming, phase shift, and UAV trajectory subproblems which are jointly optimized. For the beamforming and phase shift subproblems, we apply S-procedure and general sign-definiteness to transform them into tractable forms. And for the UAV trajectory, we unroll the constraints and apply first-order Taylor transform and the difference of two convex functions (DC) to obtain a relaxed convex problem that can be numerically solved. The proposed solution show effectiveness and improved the secrecy rate of the network.

Recently, the energy crisis occurred more frequently, which makes it urgent to develop resilient operating strategies for the integrated electricity-natural gas system (IEGS) by using alternative fuel resources during extreme natural disasters. As an economic solution to the transportation of hydrogen using pipelines, hydrogen enriched compressed natural gas (HCNG) has great potential value in resilience enhancement for the IEGS. This article proposed a novel resilient operating strategy of the IEGS using HCNG to provide fuel support during the emergency. First, a resilient operating model of the HCNG-integrated natural gas system is developed with the consideration of securing the gas quality and safety operation of gas turbines with insufficient gas supply. Different from the operating models formulated for the normal state, both hydrogen and methane injection are considered and their volume fractions are to be optimized in the proposed model. Furthermore, a tractable resilience-oriented operating model is reformulated using appropriate approximation techniques and it is effectively solved using the alternating optimization-integrated alternating direction method of multipliers algorithm. Case studies, performed on two test systems, are conducted to demonstrate the effectiveness of enhancing the energy crisis-resistance with the proposed strategy.

This article concerns the issue of bipartite leader-following consensus for a class of nonlinear switched multiagent systems. The novel model-depended distributed dynamic event-triggered protocols are constructed. Compared to the existing event-triggered rules, different event-triggered functions are designed for different system models to decrease the amount of calculation and communication, and nonnegative model-depended dynamic auxiliary variables are introduced to further enhance the triggering performance. Based on the event-triggered protocols, the novel distributed model-depended bipartite event-triggered control laws are presented, in which different models correspond to different controller gains to ensure better control performance. By employing the Lyapunov theory and the average dwell time method, bipartite leader-following consensus is guaranteed with a global exponential convergence rate. Besides, the Zeno phenomenon is ruled out. Finally, several numerical examples are performed to validate the feasibility and superiority of the proposed theory.