Space-Air-Ground Integrated Networks (SAGINs) are a cornerstone of future 6 G systems, promising ubiquitous connectivity by amalgamating satellite, aerial, and terrestrial communication resources. However, these networks traditionally operate in isolated silos, leading to inefficient resource utilization and limited adaptability. To address these challenges, this paper proposes a collaborative access framework that allows cross-layer communication, enabling user equipment from one domain to access network infrastructure in another. The key innovations of this work lie in three aspects: first, it breaks the fixed hierarchy of traditional SAGINs and integrates access selection into the optimization problem, allowing dynamic adjustment of useraccess point associations; second, it establishes a holistic mathematical model that simultaneously considers access selection, frequency resource block allocation, and power control, capturing the complex interference interactions between different layers; third, it provides a theoretical benchmark for evaluating the performance of practical algorithms by formulating the problem as a unified MINLP model.

We present a novel deep reinforcement learning framework for optimizing telecom product portfolios. Our proposed Telecom Product Portfolio Optimization (TPPO) algorithm formulates portfolio consolidation as a sequential decision-making problem, integrating multi-dimensional product feature representation and a specially designed multi-objective reward function. The framework employs deep Q-learning to balance portfolio reduction with revenue preservation and user experience. Experimental results demonstrate that TPPO achieves a 33.8% portfolio reduction while increasing revenue by 5.47%, significantly outperforming traditional rule-based and clustering approaches. The framework demonstrates strong practical value for telecom operators seeking to streamline their product portfolios while enhancing business performance.

Abstract The sixth-generation (6G) mobile network implements the social vision of digital twins and ubiquitous intelligence. Contrary to the fifth-generation (5G) mobile network that focuses only on communications, 6G mobile networks must natively support new capabilities such as sensing, computing, artificial intelligence (AI), big data, and security while facilitating Everything as a Service. Although 5G mobile network deployment has demonstrated that network automation and intelligence can simplify network operation and maintenance (O&M), the addition of external functionalities has resulted in low service efficiency and high operational costs. In this study, a technology framework for a 6G autonomous radio access network (RAN) is proposed to achieve a high-level network autonomy that embraces the design of native cloud, native AI, and network digital twin (NDT). First, a service-based architecture is proposed to re-architect the protocol stack of RAN, which flexibly orchestrates the services and functions on demand as well as customizes them into cloud-native services. Second, a native AI framework is structured to provide AI support for the diverse use cases of network O&M by orchestrating communications, AI models, data, and computing power demanded by AI use cases. Third, a digital twin network is developed as a virtual environment for the training, pre-validation, and tuning of AI algorithms and neural networks, avoiding possible unexpected losses of the network O&M caused by AI applications. The combination of native AI and NDT can facilitate network autonomy by building closed-loop management and optimization for RAN.

Unmanned aerial vehicles (UAVs) are being used extensively in various fields. UAVs provide various services to users, including monitoring, logistics, and sensing, because of their flexible deployment and dynamic reconfigurability. However, UAV networks have become more susceptible to malicious threats because of their multiconnectivity and openness. A great effort has been made to develop an effective intrusion detection system (IDS) based on machine-learning approaches for UAVs. Unfortunately, existing methods were unable to identify real time and zero-day attacks for UAV networks. This is due to that existing methods have still used obsolete data sets and past knowledge-based detection. Also, the shortcomings of standalone IDS render them unsuitable for defending UAV networks from potential security risks. Further, the lack of precise identification for compromised UAV nodes in UAV networks poses a critical security gap, risking the entire network’s integrity with the compromise of a single node. Therefore, in this work, we propose an autonomous collaborative IDS (UAV-CIDS) with a feedforward convolutional neural network (FFCNN), which accurately identifies zero-day with high accuracy. The proposed solution takes into account encoded Wi-Fi traffic logs of three popular UAVs types: 1) DBPower UDI; 2) parrot Bebop; and 3) DJI spark. Evaluation results indicate that our FFCNN model has produced outstanding results based on the UAVIDS data set with 98.23% accuracy compared to existing models. After the detection of attacks, their mitigation is equally significant. In addition, we also design and implement real-time incident response handling against cyber-attacks on UAV Networks. The incident response handling will assist in minimizing the effects of a security breach, remediate vulnerabilities and systematically secure the entire UAV networks.

To address the three main challenges in Digital Twin Network (DTN), we propose the Elastic Digital Twin Network Modeling (EDiTNetMdl) fitting in network life cycle to restrain resource occupation. The challenges our solution aims to tackle are: fulfilling differentiated requirements across Network Life Cycle (NLC) stages with a single DTN; loosening the decision delay caused by current DTNs, hampering utilization; and the prohibitive costs of fully replicating physical telecom infrastructure and systems. To tackle these challenges, EDiTNetMdl constructs DTN modeling in a hierarchical, multilayered abstraction. In this framework, Telecom Network Elements (NE) are represented as Classes and inheritance hierarchies. NE functions and connections are transformed into class methods along these inheritance chains. NLC stages are detected through probes which are specialized methods used to deduce the stage and needs. Complete data collection occurs at the bottom NE instances, while higher stages obtain well-abstracted metadata and functional descriptions. We enumerate 7 NLC use cases as inputs for numerical evaluations. Both the number of DTN Instances built and methods invoked are significantly decreased in EDiTNetMdl compared to contrasting solutions. Consequently, the resource occupation is significantly diminished, further highlighting the efficiency gains achieved with EDiTNetMdl.

With the growing demand for mobile communication services and continuous advances in communication technologies, ubiquitous coverage and Internet of Everything have become basic capabilities required for 5G, 6G and future networks. On-orbit autonomy enables satellites to perform tasks, process data, adjust parameters, update software, diagnose faults and recover functions autonomously in orbit. This enhances intelligence, efficiency and reduces operation costs and risks, especially for large low earth orbit (LEO) constellations. On-orbit autonomy is an important trend with great significance and value. Although some research and tests have been conducted as well as application requests and standardization gathered, modeling constellation autonomy from an on-orbit communication and computing perspective has received little attention. This paper reviews related standards, research, and tests as a foundation for modeling. Key technologies, networking and management are studied as major concerns in modeling. The Constellation Autonomy Modeling (CAM) framework for agile on-orbit communication and computing is proposed as a reference model and method for on-orbit autonomy.

As a key role in the future network functionalities, artificial intelligence (AI) has been studied widely. To match the envisioned traffic evolution trend and communication requirement, Network Data Analytic Function(NWDAF) is a new proposed 5G component to provide analytics for any Network Functions (NFs). Considering sending all data to a central NWDAF instance is extremely time-consuming and it raises concerns about security vulnerabilities and data overload, it's expected to design a distributed NWDAF architecture which is able to enhance NF data localization, improve security, reduce control overhead during model training, shorten the training time, and finally, enhance the accuracy of the trained models by virtue of local testing on a real-time network. Federated learning is considered in future NWDAF design, while its detailed structure and processes are not standardized yet. This paper studies the effectiveness of distributed federated learning based traffic characteristics analysis for NWDAF. Simulation shows that the learning accuracy is nolinear with data loss probability. Under the simulation assumption, the solution could endure 30% data loss, while the learning accuracy and the retransmission dramatically rise when data loss probability is greater than 45%.

With the continuous development of the 5th Generation Mobile Communication Technology (5G), various forms and demands of the Internet of Things (IoT) business have emerged. There are high requirements about the latency and reliability in some IoT services, especially for the ultra reliable low latency Communication (URLLC) services of IoT. One important factor for URLLC is time-frequency resource allocation. In this paper, a joint delay and channel quality based proportional fair scheduling (JDCPF) algorithm is proposed, which considers scheduling delay and channel conditions to calculate scheduling priorities for different IoT services with different communication requirements. Moreover, an adjustment factor is introduced based on the latency sensitivity of different services. According to the provided simulation results, the proposed JDCPF algorithm effectively could help to reduce the scheduling latency for latency-sensitive services.

The maturity of 5G technology has promoted the rapid development of vehicular communications and intelligent transportation systems. As an important part of vehicular communications, vehicle‐to‐infrastructure (V2I) is used for the huge data transmission between vehicles and roadside units. Aiming to analyse the V2I wireless channel distance‐dependent characteristics, this study presents two 5.9 GHz measurements in urban and suburban propagation scenarios. The distance‐based amplitude distribution, delay and Doppler spreads, path loss and shadowing are extracted from the raw data. It is noted that the characteristics of V2I wireless channel vary with the distance between the transmitter and receiver. Rich multi‐path components in the urban area will make more influence than that in the suburban area. Similar with the 5G pico‐cell, the coverage distance, signal‐to‐noise ratio and channel capacity are calculated from the measurement data. The proposed results indicate that the coverage ranges of urban and suburban V2I wireless communication are about 250.3 and 80.04 m when the average power received from a single reference signal is . Rich reflection effect from surroundings in the urban propagation scenario will lead to a faster attenuation of wireless signal. The proposed results can provide a meaningful reference for future research of 5G vehicular communications.

Mobile service will be more various and handover have to adapt different quality-of-service (QoS) requirements of all kinds of service in 4G mobile communication systems. In the paper, a dynamic priority queue handover scheme is proposed. With the scheme, a common queue manager (CQM) is appended and the priority in the queue can be changed according to the variety of waiting delay. Furthermore, the resource is graded and reserved, which can insure the access of high priority service. The proposed handover scheme, which is adapted to different QoS requirements, can significantly improve the system performance, and reduce the drop rate and the block rate.