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 integration of multidimensional networks such as space, air and ground is the future trend of the IoT. In this article, we introduce the SAG IoT network paradigm, including its composition and network architecture. Network slicing, the core 5G technology, will be applied to the SAG IoT network. The wide use of mmWave and UAVs is to produce many new scenarios, and it is necessary to study the effects of UAVs on mmWave channels in these new scenarios. In addition, mmWave imaging technology can provide a basis for this research. Machine learning is an important new technology that can be widely used in the SAG IoT. Simulation and measurement are both important means of evaluating the performance of communication systems. To cope with the emerging new applications and technologies, a cloud-based modular simulation system is introduced for 5G and future IoTs; this system is characterized by high efficiency, flexible configuration and high precision.

This paper develops a new approach of large language model (LLM) enhanced reconfigurable intelligent surface (RIS), in a bid to achieve energy-efficient and reliable communication in 6G Internet of Vehicles (IoV). It is well known that RIS offers an innovative solution to improve signal quality by intelligently adjusting the propagation path of radio waves. However, configuring RIS in the dynamically changing vehicular environment remains a challenge. This study leverages the analytical capabilities of LLM, combined with key IoV data such as channel status, vehicle movement patterns, and quality of service requirements, to model and optimize RIS-based IoV communication systems. The work focuses on constructing a realtime model of the RIS-based IoV wireless transmission system and proposes an optimal strategy for wireless resource allocation. Through comprehensive simulation results, this paper justifies the significant performance advantages of LLM-enhanced RIS, demonstrating a viable technical pathway for the development of 6G IoV.

With the continuous evolution of emerging technologies such as mobile network, machine learning (ML), 5G, etc., digital twin bursts out great potential by its capacity of data analysis, data tracking, data prediction, etc, building a bridge between the physical and information world. In the mobile networks, digital twin 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. In this paper, a communication-assisted sensing scenario is considered with Federated Learning (FL) in digital twin-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 effectiveness of proposed schemes is demonstrated by the simulation results.

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.

UAV technology is a basic technology aiming to help realize smart living and the construction of smart cities. Its vigorous development in recent years has also increased the presence of unmanned aerial vehicles (UAVs) in people’s lives, and it has been increasingly used in logistics, transportation, photography and other fields. However, the rise in the number of drones has also put pressure on city regulation. Using traditional methods to monitor small objects flying slowly at low altitudes would be costly and ineffective. This study proposed a real-time UAV tracking scheme that uses the 5G network to transmit UAV monitoring images to the cloud and adopted a machine learning algorithm to detect and track multiple targets. Aiming at the difficulties in UAV detection and tracking, we optimized the network structure of the target detector yolo4 (You Only Look Once V4) and improved the target tracker DeepSORT, adopting the detection-tracking mode. In order to verify the reliability of the algorithm, we built a data set containing 3200 pictures of four UAVs in different environments, conducted training and testing on the model, and achieved 94.35% tracking accuracy and 69FPS detection speed under the GPU environment. The model was then deployed on ZCU104 to prove the feasibility of the scheme.