The field of satellite and wireless networking is rapidly evolving, with a focus on developing more efficient, sustainable, and autonomous systems. Recent research has explored the use of artificial intelligence (AI) and machine learning (ML) to optimize network performance, improve resource allocation, and enhance decision-making. One notable trend is the integration of Large Language Models (LLMs) into network architectures, enabling more intelligent and adaptive control. Another key area of research is the development of novel networking paradigms, such as vehicular cloud computing and edge computing, which aim to reduce latency and improve quality of service. Furthermore, there is a growing interest in exploring new frequency bands, such as millimeter wave and terahertz, to support the increasing demand for high-speed and low-latency communications. Overall, the field is moving towards more dynamic, flexible, and autonomous networking systems that can efficiently support a wide range of applications and services. Noteworthy papers include: The Free Will Equation, which proposes a theoretical framework for endowing AGI agents with adaptive stochasticity in decision-making; NetIntent, which introduces a unified framework for intent-based SDN automation using LLMs; and Symbiotic Agents, which presents a novel paradigm for trustworthy AGI-driven networks by combining LLMs with real-time optimization algorithms.
Advances in Satellite and Wireless Networking
Sources
Diffraction and Scattering Modeling for Laser Power Beaming in Lunar Environment
The Free Will Equation: Quantum Field Analogies for AGI
A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction
From Cell Towers to Satellites: A 2040 Blueprint for Urban-Grade Direct-to-Device Mobile Networks
A Fault-Tolerant Architecture for Urban and Rural Digital Connectivity: Synergizing SDWMN, Direct-to-Mobile Broadcasting, and Hybrid Cloud Streaming
White paper: Towards Human-centric and Sustainable 6G Services -- the fortiss Research Perspective
Intent-Based Network for RAN Management with Large Language Models
NetIntent: Leveraging Large Language Models for End-to-End Intent-Based SDN Automation
Dora: A Controller Provisioning Strategy in Hierarchical Domain-based Satellite Networks
Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches
Enhancing Sustainability in HAPS-Assisted 6G Networks: Load Estimation Aware Cell Switching
Beyond Visual Line of Sight: UAVs with Edge AI, Connected LLMs, and VR for Autonomous Aerial Intelligence
Assessing the Benefits of Ground Vehicles as Moving Urban Base Stations
Advancing Lunar Communication through Inter-domain Space Networks and Dynamic Orchestration
On the Role of AI in Managing Satellite Constellations: Insights from the ConstellAI Project
Vehicular Cloud Computing: A cost-effective alternative to Edge Computing in 5G networks
Dynamic Activation and Assignment of SDN Controllers in LEO Satellite Constellations
Custody Transfer and Compressed Status Reporting for Bundle Protocol Version 7
Symbiotic Agents: A Novel Paradigm for Trustworthy AGI-driven Networks
Optimizing Edge Gaming Slices through an Enhanced User Plane Function and Analytics in Beyond-5G Networks
ARCADE: A RAN Diagnosis Methodology in a Hybrid AI Environment for 6G Networks
Talk with the Things: Integrating LLMs into IoT Networks
TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks
AI/ML Life Cycle Management for Interoperable AI Native RAN