Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks
arXiv:2606.28339v1 Announce Type: cross Abstract: Industrial 6G networks require ultra-reliable, low-latency, and energy-efficient connectivity in dynamic and blockage-prone environments, where conventional terrestrial deployments often fail to ensure stable coverage. Hence, in this paper, we propose a RIS-enabled Open-RAN framework for integrated terrestrial/non-terrestrial (TN/NTN) industrial 6G networks, in which UAVs-mounted reconfigurable intelligent surfaces (RISs) cooperate with ground radio units and a high-altitude platform (HAP) to enhance connectivity for dense industrial IoT device
The increasing demands of industrial automation, coupled with the limitations of current communication infrastructures in dynamic, blockage-prone environments, necessitate advanced solutions like those leveraging 6G and AI.
This development indicates a critical step towards ultra-reliable and energy-efficient connectivity for future industrial operations, integrating terrestrial and non-terrestrial networks with AI for optimization.
The proposed framework introduces AI-driven optimization to 6G networks, enabling robust connectivity in challenging industrial settings through the cooperative deployment of RISs, ground units, and HAPs.
- · Industrial IoT manufacturers
- · Telecommunication providers
- · AI software developers
- · Aerospace and defence contractors
- · Legacy industrial network providers
- · Countries with limited space infrastructure
Enhanced connectivity and efficiency for industrial operations across diverse and challenging environments.
Increased adoption of automation and advanced robotics in industries due to reliable communication infrastructure.
The acceleration of new industrial paradigms that rely on pervasive, intelligent, and highly reliable connectivity, potentially reshaping global supply chains and manufacturing capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI