Massive multiple-input multiple-output (MIMO) wireless communication technology with the characteristics of hyperconnectivity is an ideal channel to connect the industrial Internet of Things (IIoT) and the cyber physical system (CPS). It provides stable and reliable connectivity from the data center to distributed user terminals and the IIoT. However, traditional massive MIMO suffers from high power consumption and fabrication cost. The design of energy-efficient massive MIMO technology is essential for larger scale industrial deployments. We design three types of non-linear RF chain structures, which not only reduce the power consumption of massive MIMO systems but also save fabrication costs. Information theoretic analysis demonstrates the power efficiency performance of our non-linear system design. Our non-linear MIMO system designs can increase the power efficiency by up to 2.3 times compared with the traditional MIMO system. We have demonstrated that our systems can achieve the same uplink rate as traditional MIMO by increasing the number of receiving antennas but with less overall power consumption. We also proposed an algorithm to overcome the problem of low computational efficiency due to high-dimensional integration when calculating the uplink achievable rate of non-linear MIMO. Moreover, we reveal that when the skew-normal distribution is used as signaling, the non-linear MIMO systems can achieve better performance than the Gaussian distribution.

Integrated sensing and communications (ISAC) enables efficient spectrum utilization and reduces hardware costs for beyond 5G (B5G) and 6G networks, facilitating intelligent applications that require both high-performance communication and precise sensing capabilities. This survey provides a comprehensive review of the evolution of ISAC over the years. We examine the expansion of spectrum across radio-frequency (RF) and optical ISAC, highlighting the role of advanced technologies, along with key challenges and synergies. We further discuss the advancements in network architecture from single-cell to multi-cell systems, emphasizing the integration of collaborative sensing and interference mitigation strategies. Moreover, we analyze the progress from single-modal to multi-modal sensing, with a focus on the integration of edge intelligence to enable real-time data processing, reduce latency, and enhance decision-making. Finally, we extensively review standardization efforts by 3GPP, IEEE, and ITU, examining the transition of ISAC-related technologies and their implications for the deployment of 6G networks.

Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. This paper expands the control domain to a large area with multiple intersections. In particular, a Multi-intersection Vehicular Cooperative Control (MiVeCC) is proposed to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. Under the framework, a two-stage reinforcement learning is implemented to obtain the optimal policy for vehicle control. To support RL in the proposed framework, a multi-vehicle state representation method and a safety-oriented value representation method are designed. The former structures the collected vehicle information, and the latter evaluates the traffic condition. A multi-intersection simulation platform is developed to evaluate the proposed scheme. Simulation results show that the proposed MiVeCC can improve travel efficiency at multiple intersections by up to 4.59 times without collision compared with benchmark methods.

Along with the commercialization of evolved multimedia broadcast multicast services (eMBMS), the number of mobile broadcasting users is growing notably. Previous works reveal that the accuracy of mobile channel estimation will significantly impact the quality of broadcasting services. Motivated by this fact, we apply machine learning (ML) to the fifth-generation Radio Access Network (5G RAN) slicing in this paper for the estimation and the prediction of the channel status in mobile scenarios. More specifically, a cascaded convolutional neural network (CNN)-long short term memory network (LSTM) architecture is developed to achieve channel estimation for mobile broadcasting users. The energy efficiency of the base station (BS) is modeled mathematically, and the sub-optimal solution is achieved by deep Q-Network (DQN) based on the available channel status. Finally, we present the simulation results to justify the performance of our proposed schemes.

Vehicle position prediction (VPP) is of great significance for navigation planning and traffic safety of intelligent vehicles. In general, particle filtering (PF) uses global navigation satellite system (GNSS) to implement VPP. However, it does not consider geographic layer information (GLI) and its particle weight is not combined with the real-world geographic position information, which leads to insufficient prediction preparation. To resolve this problem, we propose a novel PF-based VPP method by using three-dimensional convolutional neural network and long short-term memory (3D CNN-LSTM) network model. First, for data preprocessing, we extract kinematic information features from GNSS, and evenly divide the area around each GNSS point into multiple grids and calculate the probability of grids center belonging to each GLI type. In addition, in order to better reflect the relationship between two consecutive positions due to the factors such as the conversion angle, we construct tilted cells to represent possible positions of each vehicle at any time. Second, a novel 3D CNN-LSTM model is designed to calculate the vehicle occurrence probability (VOP) in each tilted cell by processing the GLI and GNSS data, which can optimize the PF weight of each particle, and then improve PF to make more precise position prediction. Finally, the experimental results demonstrate that the proposed VPP method can improve the cell prediction accuracy, and then significantly improve the position prediction precision.

The increasing complexity and varying operational conditions of today’s rotating machinery present significant challenges for automated fault diagnosis. While data-driven fault diagnosis methods have grown in popularity, they often rely heavily on full-cycle data, making them resource-intensive and less adaptive to diverse working conditions. Addressing this gap, our proposed system avoids the dependency on fullcycle data, employing an efficient two-stage methodology. In the initial stage, an Isolation Forest (iForest) module operates in an unsupervised mode, isolating operational anomalies indicative of potential faults. These identified anomalies are then channelled into the second stage, where an Adversarial Discriminant Domain Adaptation (ADDA) module performs an in-depth fault diagnosis. By streamlining the diagnostic process, our approach not only accelerates fault identification but also reduces the reliance on extensive datasets that are often a staple in conventional diagnostics. Performance evaluations with the XJTU-SY and CWRU bearing datasets shows our system reaches an accuracy of 95.67%, affirming its superiority as a cost-efficient, data-lean solution in machinery fault diagnostics.