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.

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.