I/Q Data Stream Clustering for C-RAN Fronthaul Bandwidth Reduction
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Solution Overview
Problem
The large amount of data transmitted between Baseband Units (BBUs) and Radio Remote Units (RRUs) in Cloud Radio Access Networks (C-RAN) consumes significant transmission resources and increases hardware costs, particularly with the evolution of multiple-antenna technologies.
Innovation Solution
The method involves grouping I/Q data streams into clusters based on correlation, applying different compression manners such as space-time compression and efficient wireless fronthaul (EWF) compression, to reduce the transmission bandwidth required, thereby improving compression efficiency and reducing data transmission amounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data streams are transmitted between BBU and RRU in C-RAN, then signal coverage and transmission quality are improved, but transmission bandwidth requirement increases and hardware costs increase
Solution Approach 1:
The patent segments the m I/Q data streams into multiple clusters based on correlation characteristics. Different clusters are compressed using different compression manners (first compression manner for high-correlation clusters, second compression manner for other clusters). This segmentation allows optimized compression for each cluster, reducing overall transmission bandwidth while maintaining signal quality.
Solution Approach 2:
The patent applies different compression manners to different clusters of data streams based on their specific correlation characteristics. High-correlation clusters receive space-time compression while other clusters receive EWF compression. This local quality approach ensures that each cluster is compressed optimally according to its properties, maximizing compression efficiency and minimizing transmission bandwidth.
2Productivity
If multiple-antenna technology is deployed, then spectrum efficiency and transmission capacity are improved, but data transmission amount increases and hardware costs increase
Solution Approach 1:
The patent segments the data streams from multiple antennas into clusters based on correlation characteristics. By grouping correlated data streams together and applying appropriate compression to each cluster, the system reduces the total amount of data that needs to be transmitted while maintaining the benefits of multiple-antenna technology.
Solution Approach 2:
The patent changes the compression parameters and methods based on the correlation characteristics of different data stream clusters. By adjusting compression parameters dynamically according to cluster properties, the system optimizes the balance between transmission capacity and data reduction, effectively managing the data volume from multiple antennas.
3Area of stationary object
If data compression is applied to reduce transmission bandwidth, then hardware costs and transmission resources are reduced, but compression complexity and processing time increase
Solution Approach 1:
The patent segments the compression process into different stages: first grouping data streams into clusters based on correlation, then applying different compression manners to different clusters. This segmentation simplifies the overall compression process by breaking it down into manageable steps with clear decision criteria, reducing the complexity of implementing comprehensive compression.
Solution Approach 2:
The patent applies different compression manners to different clusters based on their correlation characteristics. This localized approach simplifies the compression process by using the most appropriate method for each cluster rather than applying a single complex compression algorithm to all data streams, thereby reducing overall processing complexity.
Data Source
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AI summary
A data transmission method, an apparatus, and a system are disclosed, and the method includes: grouping, by a transmit end device, m I/Q data streams into a first-type cluster and a second-type cluster, combining a cluster obtained after the first-type cluster is compressed in a first compression manner and a cluster obtained after the second-type cluster is compressed in a second compression manner, and sending a combined data stream to a receive end device. In this application, because transmission bandwidth required after a data stream in the first-type cluster is compressed in the first compression manner is less than transmission bandwidth required after the data stream in the first-type cluster is compressed in the second compression manner, the data stream in the first-type cluster is compressed in the first compression manner, and a data stream in the second-type cluster is compressed in the second compression manner, thereby effectively improving compression efficiency and reducing an amount of data for signal transmission between the transmit end device and the receive end device.