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.

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%.

In this paper, we address the problem of power consumption minimization in Heterogeneous networks (HetNets) of China Mobile by implementing dynamic base stations (BSs) switching operation. Particularly, considering the minimal rate requirements of users as well as the realistic power consumption model of BSs, we formulate the problem as an integer linear programming which is NP-complete. Then, to efficiently solve this problem, the Power Efficient Base Station Operation with User Association scheme (PEBUA) and Adaptive Multi-cell Coordination Algorithm for Energy Saving (AMCES) have been developed, which can be efficiently adopted in different network conditions, respectively. Simulation results verify the validity of our analysis and additionally, compared with the existing method in practice network operation, show the effectiveness of the proposed schemes. It should be noted that both of the proposed algorithms can be applied in real HetNets to save the overall power consumption without decreasing users' traffic rates, which makes our study more feasible.

The rapid growth in the energy consumption of mobile networks has become a major concern for mobile operators. Today's mobile networks' usage is dominated by over-the-top (OTT) applications, and operators are keen to determine the network energy consumed by these OTT applications. With a recent shift in user behavior toward a preference for instant messaging (IM) applications over conventional mobile services, operators are interested in exploring what impact OTT IM applications such as WeChat will have on the energy consumption of a network when compared with a corresponding conventional mobile service. Here, we present for the first time energy assessment models for mobile services based on real network and service measurements to address this need. Using WeChat as an OTT IM application example, our results show that WeChat consumes more network energy than conventional mobile services for both light users and heavy text users due to the network signaling energy overhead. In comparison, for heavy voice users, WeChat consumes less network energy since voice messages are first recorded and then sent in packet bursts. Our findings provide a quantitative analysis of the energy consumption of mobile services, which should be valuable for mobile operators and OTT application developers to improve the energy-efficiency of mobile applications and services.

In this paper, by jointly considering subchannels, power, and Modulation and Coding Scheme (MCS) allocation, we address the sum-rate maximization problem in OFDMA downlink systems. We formulate the problem as an integer linear programming (ILP), which maximizes the system sum-rate subject to the minimum rate requirements of users and total transmit power constraint of base station. To solve the formulation with low complexity, we propose a two-level iterative Subchannels, Power, and MCS allocation Algorithm (SPMA) by exploiting Tabu Search (TS). At each iteration, the SPMA firstly assigns MCS to users and then allocates subchannels and power based on a SubChannels and Power allocation Algorithm (SCPA). Particularly, the SCPA maximizes the system sum-rate by first satisfying the minimum rate requirements with the least transmit power. Simulation results show that the SPMA outperforms the existing algorithms in terms of sum-rate and average rate per user, as well as demonstrate that the sum-rate is distributed flexibly among users in instantaneous channel conditions with the SPMA.

In this paper, we address the energy conservation problem with the quality of service (QoS) requirements taken into account for the physical downlink shared channel (PDSCH) in LTE/LTE-Advanced networks. By jointly allocating the modulation and coding schemes (MCS), resource blocks (RB), and power, we first propose a standards-compliant QoS-oriented power control algorithm (SQPC) for realistic systems to save energy. Specifically, with an appropriate MCS allocation, the proposed algorithm can tailor the power to match the QoS requirements. However, the SQPC saves energy at the cost of RB utilization. To this end, we further devise an Enhanced SQPC algorithm (ESQPC) to strike a balance between RB allocation and energy consumption. Finally, simulation results show that the proposed algorithms have the advantage of reducing nearly half of energy consumption compared to the existing algorithm, as well as demonstrate that the ESQPC can improve RB utilization against the SQPC.