ATSC 3.0 Multicast RAN Topology with ML Spectrum Sharing
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Solution Overview
Problem
Current broadcast networks face challenges in efficiently sharing radio spectrum resources among multiple network operators, which limits the optimization of radio spectrum usage and hinders the adoption of advanced technologies like ATSC 3.0.
Innovation Solution
The proposed solution involves a method and system that utilize machine learning algorithms to optimize the sharing of radio spectrum resources across multiple radio topologies. This is achieved by assigning radio spectrum resources based on policy guidance from network operators, generating baseband packets, and scheduling their transmission over a fronthaul, ensuring compatibility across various radio technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If radio spectrum resources are shared among multiple network operators, then resource utilization efficiency is improved, but interference management and resource allocation complexity increase
Solution Approach 1:
The patent introduces a centralized controller as an intermediary entity that manages radio spectrum resource allocation and interference coordination among multiple network operators. This controller receives resource requests from different operators, performs centralized optimization calculations, and distributes allocation decisions, thereby simplifying the complexity at individual operator levels while achieving efficient spectrum sharing.
Solution Approach 2:
The patent implements dynamic resource allocation mechanisms where the radio spectrum resources are not statically assigned but continuously adjusted based on real-time channel conditions, traffic demands, and interference levels. This dynamic approach allows the system to adapt to changing conditions and optimize resource utilization efficiency while managing complexity through automated control algorithms.
2Productivity
If machine learning algorithms are used to optimize radio topology selection, then resource sharing optimization is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent employs machine learning algorithms to pre-compute and store optimal radio topology selections and resource allocation strategies in databases before actual operation. When runtime decisions are needed, the system queries these pre-computed results rather than performing complex calculations in real-time, significantly reducing processing time while maintaining optimization quality.
Solution Approach 2:
The patent replaces traditional real-time computational optimization mechanisms with machine learning-based predictive models. Instead of performing complex mathematical optimization calculations during operation, the system uses trained ML models that have learned optimal solutions from historical data, substituting mechanical computation with intelligent prediction to reduce processing time.
3Adaptability or versatility
If multiple radio topologies are supported for different technologies, then system versatility and compatibility are improved, but network configuration and management complexity increase
Solution Approach 1:
The patent implements a universal radio access network architecture that can support multiple radio technologies and topologies through a common framework. The system uses abstracted interface definitions and standardized protocols that allow different radio technologies to be managed through unified mechanisms, enabling multi-functionality without proportionally increasing management complexity.
Solution Approach 2:
The patent segments the network management functions into modular components, where each radio topology and technology can be independently configured and managed through separate but standardized interfaces. This segmentation allows operators to enable or disable specific topologies as needed without affecting the entire system, reducing overall management complexity while maintaining versatility.
Data Source
AI summary
A method disclosed includes receiving data from a plurality of data sources in a broadcast core network for transmission over a radio access network (RAN). The method includes assigning radio spectrum resources for transmitting the data over the RAN according to a policy guidance set by a plurality of network operators for sharing the radio spectrum resources and generating a baseband packet corresponding to the data at a distributed unit (DU) in the RAN. The method includes collecting transmission data from a plurality of user equipments (UEs) in the RAN for training a machine learning algorithm and scheduling transmission of the generated baseband packet to a remote unit (RU) over a fronthaul in a radio topology of a plurality of radio topologies under control of the machine learning algorithm according to the policy guidance. The generated baseband packet is compatible for transmission in the plurality of radio technologies.