In the evolving landscape of global telecommunications, this study proposes the integration of Satellite direct-to-device (D2D) communication into a federated multiagent deep reinforcement learning (DRL) framework, enhanced by blockchain technology. This approach aims to optimize network slicing capabilities for Satellite D2D communication through low-carbon sustainable computing techniques. The federated multiagent-DRL framework dynamically manages network slices, ensuring optimal resource allocation and superior Quality of Service (QoS) for direct satellite links within 6G nonterrestrial networks (NTNs). Satellite D2D addresses connectivity challenges in remote areas, reducing latency while improving network reliability and coverage. The blockchain component ensures security and transparency, fostering a decentralized learning environment with strong data integrity. By incorporating low-carbon computing methods, the framework not only reduces energy consumption and carbon emissions but also achieves sustainability goals. Comprehensive simulations show significant improvements in latency, throughput, and network resilience, optimizing resource utilization and paving the way for efficient and robust 6G NTN architectures.

With the continuous evolution of emerging technologies such as mobile network, machine learning (ML), 5G, etc., digital twins (DT) bursts out great potential by its capacity of data analysis, data tracking, data prediction, etc, building a bridge between the physical and information world. Meanwhile, mobile network is moving towards data-driven paradigm, the issue of data privacy and data security seem to be a bottleneck. As a result, federated learning (FL) and mobile network are deeply converging. However, the mobile network is time-varying and the parameters of FL-empowered mobile network is huge and continue to increase with exponential growth of wireless terminals, result in the failure of traditional modeling. In the mobile networks, DT is conducive to prototyping, testing, and optimization, enabling mobile networks to be modelled more efficiently in a virtual environment and thus providing guidance for practical application. To this end, a communication-assisted sensing scenario is considered in this paper with FL in DT-empowered mobile networks. More specifically, two communication-assisted sensing architectures are proposed to improve communication efficiency of mobile network, namely, centralized architecture of federated transfer learning (FTL) and decentralized architecture of FTL. For centralized architecture of FTL, feature extraction of sensing information is conducted by FL between partial nodes and central server while the remaining nodes are used to train the fully connected layers at the central server. Considering data safety during the communication between sensing nodes, a decentralized architecture is designed based on FTL and Blockchain, where the feature extraction module is obtained by the fusion of sharing model (by Blockchain) and local model. The performance of proposed schemes is evaluated and demonstrated by the simulations.

Recently, significant advances have been made in the autonomous driving field, with vehicles capable of traveling vast areas independently. Meanwhile, the operators face several uncertainties such as volatility in charging demand, intrinsic intermittency of green energy supply, etc....Accordingly, this study proposes an integrated green transportation system based on 6G Internet of Things (IoT) and big data technology, aiming at integrating state grid, electric vehicles and renewable energy to address these concerns. Furthermore, this study analyzes the performance of the proposed system using both actual and simulation data. The numerical results justify that the suggested techniques greatly enhance the charging index of electric vehicles compared to the benchmark method.

The world is entering a new era with ubiquitous connectivity among billions of humans and machines, i.e., the sixth-generation (6G) massive Internet of Things (IoT). Radar and communication need to be integrated into this system to achieve target detection and massive connectivity. In this work, we first develop a shape-adaptive antenna array composed of multiple subarrays spaced at regular intervals. The shape-adaptive antenna array uses flexible material that enables changes in the physical structure to achieve adaptive gain. Subsequently, we investigate the micromovement of IoT targets, such as the micro-motion characteristics and micro-Doppler effects of rotary-wing drones. The simulation results demonstrate the effectiveness and efficiency of the proposed schemes for detecting unmanned aerial vehicles (UAVs) in an IoT scenario.

Non-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly notifying the utilized NOMA signatures causes large signaling cost, blind signature classification naturally becomes a low-cost option. To accomplish signature classification for NOMA, we study both likelihood- and feature-based methods. A likelihood-based method is firstly proposed and showed to be optimal in the asymptotic limit of the observations, despite high computational complexity. While feature-based classification methods promise low complexity, efficient features are non-trivial to be manually designed. To this end, we resort to artificial intelligence (AI) for deep learning-based automatic feature extraction. Specifically, our proposed deep neural network for signature classification, namely DeepClassifier, establishes on the insights gained from the likelihood-based method, which contains two stages to respectively deal with a single observation and aggregate the classification results of an observation sequence. The first stage utilizes an iterative structure where each layer employs a memory-extended network to explicitly exploit the knowledge of signature pool. The second stage incorporates the straight-through channels within a deep recurrent structure to avoid information loss of previous observations. Experiments show that DeepClassifier approaches the optimal likelihood-based method with a reduction of 90% complexity.

The volatility of green energy power generation and the randomness of electric vehicle's charging will affect the safe operation of the grid seriously. Therefore, the joint scheduling of green energy and electric vehicles is of great significance, however, the existing charging scheduling algorithms have problems such as the single optimization objective and the complex calculation. Applying the Internet of Things technology to the traditional power industry can improve the management level of the grid effectively. Based on the prediction of green energy power, this paper established the multi-objective optimization model for the joint scheduling of green energy and electric vehicles and designed an online charging scheduling algorithm. Then the charging behavior of electric vehicles in urban scenarios is analyzed, user's charging behavior simulation method based on Monte Carlo is designed, the effectiveness of the scheduling algorithm is verified by processing the simulation data.

In recent years, unmanned aerial vehicle (UAV) technology has developed rapidly. Currently, UAVs are widely used in IoT deployment and smart agriculture. At present, the main method for radar to identify UAV is analyzing the Micro-Doppler effect. Passive radar technology based on 5G base stations can identify UAV in complex urban environments. In this paper, we propose a method combining micro Doppler effect and pattern recognition technology. Firstly, we processed the radar echo to get the Micro-Doppler feature image. Then the Micro-Doppler feature image will be classified by pattern recognition in order to judge the number of UAV rotors. The simulation results show that the method combining the Micro-Doppler effect and pattern recognition can identify the UAV in real time and accurately.

In this paper, digital terrestrial multimedia broadcast (DTMB) signals and long term evolution (LTE) signals are used for hybrid positioning in urban environment. This paper mainly introduces the time of arrival (TOA) estimation of DTMB by using the method of PN sequence correlation. In the multipath environment, constant false alarm rate (CFAR) detector and matched pursuit (MP) algorithm are used to estimate the direct path. With the pseudo-range of DTMB and LTE signals, we used extended Kalman filter (EKF) to fix a position. In the experiment, the DTMB signals can be used for the supplement of LTE signals to achieve the hybrid positioning in urban environment. Compared to positioning method with LTE only, the hybrid positioning system with DTMB-LTE improved the availability of positioning by 27% over a trajectory of 600m. The 2-D position root-mean squared error (RMSE) of DTMB-LTE system was improved by 4.55m compared to the LTE positioning only over a trajectory of 438m. Furthermore, the proposed system achieved an RMSE of 26.26m over a trajectory of 162m when only 1 LTE and 1 DTMB stations were used. The hybrid positioning system with DTMB-LTE provided an RMSE of 20.45m over a trajectory of 600m.

For the past few years, the 5G + Internet of Things (IoT) technology and big data mining and analysis applications have gradually entered various areas of people's lives.. The rapid expansion of 5G + IoT and automation technology is the basis for the formation and construction of the smart dairy cattle pasture production. Big data and artificial intelligence have greatly improved the management level and economic benefits of dairy farms. The practical application of Blockchain + 5G IoT and big data in the production of dairy products can ensure the quality and safety of milk, and is expected to bring greater social and ecological benefits. The smart dairy farm is proposed to effectively improve the production and economic benefits of the pasture. This article aims to propose an intelligent way of identifying individual cattle and precise feeding of dairy cows based on 5G IoT technology. Within the frame of smart pasture management, the cow image identification makes feasible the timely identification of abnormal individuals in order to take suitable treatment for a different situation. The application of 5G image processing technology could save labor and promote efficient management. The combination of this technology and intelligence can effectively improve the economic benefits and production efficiency of the cattle farm through image recognition technology.

Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth‐generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high‐speed and low‐latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real‐time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real‐time tracking of UAV targets, we employed the tracking‐by‐detection mode in machine learning, with the network‐modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real‐time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high‐precision real‐time UAV target tracking at a reduced cost.

Technological advances have enabled the concept of the Internet of things to be put into practice in many fields from industry to agriculture. A large number of devices connected to the Internet of things pose a huge challenge to data processing and transmission. Terahertz band is a promising solution because of its huge spectrum resource and high transmission rate, but its high space loss limits the communication distance. In this paper, a MIMO channel model for terahertz communication is proposed, which can increase the communication distance by beam shaping and improve the communication capacity by spatial multiplexing. According to the results of simulation analysis, MIMO channel precoding technology can improve the signal-to-noise ratio of communication system.

With the emergence of Internet-of-Things (IoT) and ever-increasing demand for the newly connected devices, there is a need for more effective storage and processing paradigms to cope with the data generated from these devices. In this study, we have discussed different paradigms for data processing and storage including Cloud, Fog, and Edge computing models and their suitability in integrating with the IoT. Moreover, a detailed discussion on low latency and massive connectivity requirements of future cellular networks in accordance with machine-type communication (MTC) is also presented. Furthermore, the need to bring IoT devices to Internet connectivity and a standardized protocol stack to regulate the data transmission between these devices is also addressed, while keeping in view the resource-constraint nature of IoT devices.

In 5G communications, small cells are one of the main approaches to achieve data diversion and improve network capacity. The problem of blind area is partially solved by this way, because the distances between small base stations and users are cut short. However, the intensive deployment of small base stations will bring about complex disturbance and a large amount of energy consumption. To overcome this challenge, we propose a new approach of dual-antenna passive repeater, which consists of a four-element patch antenna array, a feeding network and an improved planar Yagi-Uda antenna with added parasitic patches. It can be used in cooperation with small base stations to replace the function of the small base stations in a certain point, change the beam pointing, and achieve wide-angle scattering to realize the blind area signal coverage. The genetic algorithm which is a branch of machine learning is used to optimize the antenna parameters. Simulation results show that our proposed passive repeater can effectively reduce the path loss and improve the signal power of the receiving end.

With the rapid evolution of smart city all over the world, the appealing services of IoT and big data analytics have prompted the design of more reliable assurance mechanism for network quality. It has been a crucial issue of network operation that once multiple links fail simultaneously, the transmission of real-time services cannot be guaranteed. Therefore, rapid locating of faults is the premise for network to recover quickly. However, current faults location methods can't satisfy the requirement due to the expansion scale of wireless and optical networks and the growing demands of customers. In this paper, we propose an efficient multi-link faults location algorithm based on Hopfield Neural Network (HNN). We make full use of the information of network topology and the services transmitted to model the relationship between fault set and alarm set. HNN is used as an optimization method to analyze the uncertainty of faults and alarms and to find where the faults most likely occur by constructing a proper energy function. It has been proved by experiments that this method can achieve real-time faults location while ensuring positioning accuracy, which provides a good solution for smart city service assurance.

Nowadays, IoT video applications are in a sharp rise, various real-time video streaming of video surveillance systems transmitted via Internet are widely investigated. The real-time video surveillance can actively monitor and detect the abnormal events in time. In 5G HetNets, we specifically develop a compressed sensing based linear predictor to predict the traffic load at the next moment. The results justify that our proposed method can forecast the traffic load and improve system performance.

Ambient backscatter employs existing radio frequency (RF) signals in the environment to support sustainable and independent communications, thereby providing a new set of applications that promote the Internet of Things (IoT). However, nondirectional forms of communication are prone to information leakage. In order to ensure the security of the IoT communication system, in this paper, we propose a machine learning based antenna design scheme, which achieves directional communication from the relay tag to the receiving reader by combining patch antenna with log-periodic dual-dipole antenna (LPDA). A multiobjective genetic algorithm optimizes the antenna side lobe, gain, standing wave ratio, and return loss, with a goal of limiting the number of large side lobes and reduce the side lobe level (SLL). The simulation results demonstrate that our proposed antenna design is well suited for practical applications in physical layer security communication, where signal-to-noise ratio of the wiretap channel is reduced, communication quality of the main channel is ensured, and information leakage is prevented.

UAVs (Unmanned Aerial Vehicles), also called drones, have drawn the attention of researchers owing to its flexibility, threatening and enormous application value. The construction of 5G network brings a new direction of detecting, identifying, and managing UAVs based on the native cloud architecture. In 5G end-to-end network slices, rotor UAVs are detected and identified by deploying 5G millimeter waves and using a joint algorithm, the improved short-time Fourier transform (STFT) and based on Bessel function base. For one-rotor UAV, the use of STFT following conjugation of sinusoidal frequency modulation (SFM) radar echo data based on millimeter wave doubles the recognition effect compared with the unconjugated processing. For multi-rotors UAV, the number of rotors and the length and rotational speed of each rotor are effectively identified through projection on the SFM data and the introduction of k order Bessel function. According to the results of automatic identification of UAVs by 5G native cloud architecture, the high bandwidth and low delay of 5G network provide a reliable basis for the resolution. Because of good robustness of the Bessel function, it provides an effective solution for the detection, identification and management of UAVs by 5G millimeter wave radar.

In recent years, with the energy consumption and environmental degradation, science and technology embarked on a path of sustainable development. In this situation, 5G green communication system has been widely used. This paper introduces the application of RF reflectors to 5G mmWave, where line‐of‐sight (LoS) blockage is a major hindrance for the coverage. In particular, we investigate the lab measurement of RF reflectors, which is a critical step from the theory to the practice. Furthermore, through the lab measurement, a 3D near‐field range migration (RM) imaging algorithm for MIMO array configuration is proposed, and the sampling scheme is improved to save the computation time while providing high‐quality images.