ML-Based Node Selection for 5G NR-U Channel Sensing
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Solution Overview
Problem
Current collaborative sensing methods in 5G NR-U networks face inefficiencies due to the exchange of large amounts of data from all nodes, lack of consideration for historical accuracy, and failure to optimize node selection, leading to reduced detection accuracy and network resource exhaustion.
Innovation Solution
A computer-implemented method using a machine learning process to select a subset of nodes for channel sensing based on predicted accuracy, reducing the number of nodes involved and optimizing data collection, thereby enhancing detection accuracy and conserving network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all nodes are involved in collaborative sensing to improve detection accuracy, then the reliability of channel determination is improved, but the network overhead and resource consumption increase significantly
Solution Approach 1:
The patent segments the set of all capable nodes into a selected subset of nodes that will actually perform sensing and report measurements. This segmentation allows the system to maintain detection accuracy by including sufficient nodes while reducing network overhead by excluding unnecessary nodes from the sensing process.
Solution Approach 2:
The patent extracts only the necessary sensing data from the selected subset of nodes, rather than collecting data from all capable nodes. This extraction principle reduces the quantity of sensed information that needs to be exchanged over the network while maintaining the reliability needed for accurate channel determination.
2Measurement precision
If sensing data from all nodes is collected to improve measurement precision, then the accuracy of channel determination is improved, but the complexity of data processing increases
Solution Approach 1:
The patent extracts only the essential measurements from a selected subset of nodes rather than processing data from all nodes. This reduces the complexity of data aggregation and processing while maintaining measurement precision through selective collection of relevant sensing information.
Solution Approach 2:
The patent applies local quality by selecting nodes based on their individual characteristics and the specific sensing requirements. Each selected node contributes measurements appropriate to its local conditions, optimizing the overall measurement precision while minimizing processing complexity through targeted data collection.
3Reliability
If all nodes contribute sensing information to improve reliability, then the robustness of the sensing system is improved, but the energy consumption of the network increases
Solution Approach 1:
The patent segments the network nodes into an active sensing subset and inactive nodes. This segmentation maintains sensing system robustness by including enough diverse nodes to reliably determine channel status while reducing energy consumption by keeping other nodes inactive during the sensing process.
Solution Approach 2:
The patent applies partial action by having only a selected subset of nodes actively participate in sensing rather than all capable nodes. This partial participation maintains sufficient reliability for robust channel determination while significantly reducing the total energy consumption of the network.
Data Source
AI summary
A computer implemented method performed by a first node in a communications network for use in determining whether a channel between the first node and a target node is in use. The method includes selecting, from a plurality of other nodes that are suitable for making measurements on the channel, a subset of the other nodes from which to obtain channel information in order to determine whether the channel is in use. The selection is performed using a first model trained using a first machine learning process to select the subset of other nodes based on accuracy of the resulting determination of whether the channel is in use. The method then includes sending a message to cause the subset of other nodes to obtain the channel information.