Dynamic Beyond 5G and 6G Connectivity: Leveraging NTN and RIS Synergies for Optimized Coverage and Capacity in High-Density Environments

The increasing demand for reliable, high-capacity communication during large-scale outdoor events poses significant challenges for traditional Terrestrial Networks (TNs), which often struggle to provide consistent coverage in high-density environments. This paper presents a novel 6G radio network planning framework that integrates Non-Terrestrial Networks (NTNs) with Reconfigurable Intelligent Surfaces (RISs) to deliver ubiquitous coverage and enhanced network capacity. Our framework overcomes the limitations of conventional deployable base stations by leveraging NTN architectures, including Low Earth Orbit (LEO) satellites and passive RIS platforms seamlessly integrated with Beyond 5G (B5G) TNs. By incorporating advanced B5G technologies such as Massive Multiple Input Multiple Output (mMIMO) and beamforming, and by optimizing spectrum utilization across the C, S, and Ka bands, we implement a rigorous interference management strategy based on a dynamic SINR model. Comprehensive calculations and simulations validate the proposed framework, demonstrating significant improvements in connectivity, reliability, and cost-efficiency in crowded scenarios. This integration strategy represents a promising solution for meeting the evolving demands of future 6G networks.

Flexible Payload Configuration for Satellites using Machine Learning

Satellite communications, essential for modern connectivity, extend access to maritime, aeronautical, and remote areas where terrestrial networks are unfeasible. Current GEO systems distribute power and bandwidth uniformly across beams using multi-beam footprints with fractional frequency reuse. However, recent research reveals the limitations of this approach in heterogeneous traffic scenarios, leading to inefficiencies. To address this, this paper presents a machine learning (ML)-based approach to Radio Resource Management (RRM). We treat the RRM task as a regression ML problem, integrating RRM objectives and constraints into the loss function that the ML algorithm aims at minimizing. Moreover, we introduce a context-aware ML metric that evaluates the ML model's performance but also considers the impact of its resource allocation decisions on the overall performance of the communication system.