Fluid antenna systems (FAS) have recently emerged as a promising solution for sixth-generation (6G) ultra-dense connectivity. These systems utilize dynamic radiating and/or shaping techniques to mitigate interference and improve spectral efficiency without relying on channel state information (CSI). The reported improvements achieved by employing a single dynamically activated radiating position in fluid antenna multiple access (FAMA) are significant. To fully realize the potential of FAMA in multi-user multiplexing, we propose leveraging the unique fast-switching capabilities of a single radio-frequency (RF)-chain meta-fluid antenna structure to achieve multi-activation. This allows for a significantly larger set of independent radiating states without requiring additional signal processing. Simulations demonstrate that multi-activation FAMA enables robust multi-user multiplexing with a higher signal-to-interference ratio (SIR) under various Rayleigh-fading environments compared to other single RF-chain technologies. We further show that the SIR can be optimized within a 15~$μs$ timeframe under a multi-user Rayleigh-fading channel, making the proposed scheme highly suitable for fast-changing wireless environments. Verified through the theoretical Jakes' model, full three-dimensional (3D) electromagnetic (EM) simulations and experimental validation, multi-activation FAMA enables effective CSI-free, multi-user communication, offering a scalable solution for high-capacity wireless networks.

The fast-growing Internet of Things (IoT) have generated a vast number of IoT tasks, and these tasks are usually featured by strict response latency requirements. To cater for the time-sensitive IoT application scenarios, vehicular fog computing (VFC) can be adopted to serve the offloading requests from the IoT devices. However, current works in VFC seldom consider the task execution failures that are actually inevitable owing to limited computing resources in VFC compared to cloud computing. Hence, we strive to enhance the VFC system by incorporating the failures for task execution into our system model, which makes task offloading more general and practical. We formulate our energy consumption optimization as a mixed integer nonlinear programming problem and further put forward an iterative algorithm to solve it. We validate our approach by extensive simulation and the experimental results have proven its advantages in terms of the optimal values.

Since the birth of narrowband Internet of Things (NB-IoT), the Internet of Things (IoT) industry has made a considerable progress in the application for smart cities, smart manufacturing, and healthcare. Therefore, the number of UEs is increasing exponentially, which brings considerable pressure to the efficient resource allocation for the bandwidth and power constrained NB-IoT networks. In view of the conventional algorithms that cannot dynamically adjust resource allocation, resulting in a low resource utilization and prone to resource fragmentation, this paper proposes a double deep Q-network (DDQN)-based NB-IoT dynamic resource allocation algorithm. It first builds an NB-IoT environment model based on the real environment. Then, the DDQN algorithm interacts with the NB-IoT environment model to learn and optimize resource allocation strategies until it converges to the optimum. Finally, the simulation results show that the DDQN-based NB-IoT dynamic resource allocation algorithm is better than the traditional algorithm in the resource utilization, average transmission rate, and UE average queuing time.

Due to the large coverage of 5G NB‐IoT networks, a more realistic mobility model for a macroscopic scene will greatly facilitate the development of optimal radio resource management algorithms. However, models devised for a random motion scene are no longer applicable in circumstances. Therefore, in this paper, a city‐level mobility model is proposed based on the feature mining of the real trajectory of vehicles in the city of Shenzhen. The proposed model is separately designed in the motion trajectory to reduce the mutual influence between the time and spatial sequence. Simulation results show that it can better present specific node motions with the physical constraints of the city layout, which are motivated with a high degree of fit in terms of self‐similarity, hotspots, and long‐tail features.

Due to the respective developments of various radio access technologies, some geographical areas may be covered simultaneously by different wireless networks. The cooperation of these networks will not only meet the diverse quality of service requirements of mobile users but also efficiently utilize the resources of systems. As an important component of radio resource management, call admission control (CAC) is to decide whether a new call or a handover request can be accepted into a resource-constrained network. In such heterogeneous wireless environment, it needs more accurate metrics to measure the performances of mobility especially inside a cellular. In this paper, we propose a new equivalent cost ring based model to represent the complex heterogeneous wireless environment in a simplified and unified way. The coverage of base station is divided into regions based on the basic data rate. Using this model, we derive the mobility parameters such as new call blocking and handover failure probabilities. The system performances of heterogeneous networks are validated by simulations.

In overlay heterogeneous wireless networks, many mobile terminals may operate vertical handover simultaneously which is defined as Group Vertical Handover (GVHO). Conventionally, handover decision schemes designed for a single user may cause network congestion and increasing handover blocking probability in GVHO scenario. To provide seamless handover with QoS guarantee, a fuzzy clustering method (FCM) is proposed by combining the concurrent traffic attributes of GVHO users and the status of available candidate networks. After the combination, users are able to select the proper network based on the dynamic diagram, which is generated by FCM. The collision probability can also be decreased through allocating handover request properly in a specific time window. Compared to the other two schemes, the simulation results show that the proposed scheme reduces the handover blocking probability about 17% and 25% (the maximum rate) respectively.

With the increase of science project in size and complexity, it is necessary to integrate and share global research resources for distributed experimentation and simulation. This paper presents an architecture of Cooperative Remote Laboratories (CRLab) with scalability, flexibility and heterogeneity. It consists of a central Web server, simulation servers, instrument servers and experiment devices, which provide four logical functions including Parallel Computing, Virtual Instrument, Collaborative Simulation and Web 2.0. Based on these functions with interactive online features, the research can be more efficient and convenient just via a simple Web browser.

Given that the conventional Selected Mapping (SLM) scheme needs to multiply pseudo-random phase sequences to the transmitted data meanwhile transmitting the whole side information, an exhaustive entropy and chaotic phase sequence based SLM method was proposed for reduction the peak-to-average power ratio (PAPR) of Orthogonal frequency division multiplexing (OFDM) in the paper. In the novel algorithm, exhaustive entropy was used to evaluate the randomness of phase sequence, chaotic sequences to reduce the large side information and improved Lorenz sequence digitalization method to enlarge sequence entropy, expanding the number of the candidate phase sequences vector space with the lower transmitted parameters of phase sequence. Simulation results show that different quantization methods of chaotic sequence have different exhaustive entropy and PAPR values. Furthermore, compared with the traditional SLM method, the new scheme proposed in the paper has better performance in reducing PAPR of OFDM system by about 0.1-0.5dB.