Adaptive DM-RS Patterns for Wireless Overhead Reduction
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Solution Overview
Problem
Wireless communication networks face challenges in reducing reference signal overhead, particularly in low power class nodes where favorable channel conditions are not optimized by existing DM-RS patterns, leading to potential interference and impact on data decoding and HARQ operations.
Innovation Solution
The method involves determining the use of frequency-domain or time-domain bundling patterns for reference signals, along with code-domain or timing-domain reductions, to minimize overhead while ensuring reliable channel estimation and data demodulation, specifically by restricting the position or size of reference signal presence in resource blocks or subframes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predetermined DM-RS patterns are used in all Resource Blocks assigned to PDSCH, then reliable data decoding is achieved, but reference signal overhead increases
Solution Approach 1:
The patent segments the Resource Blocks into different groups and applies different DM-RS patterns to different segments. Specifically, it uses first DM-RS patterns in first Resource Blocks and second DM-RS patterns in second Resource Blocks, allowing selective placement of reference signals only where needed for reliable decoding while reducing overall overhead.
Solution Approach 2:
The patent applies local quality by making different parts of the resource allocation have different DM-RS characteristics. Certain Resource Blocks receive denser DM-RS patterns while others use sparser patterns, optimizing the balance between reliability and overhead based on local channel conditions and interference characteristics.
2Productivity
If reference signal overhead is reduced, then more resources are available for data transmissions, but channel estimation accuracy deteriorates
Solution Approach 1:
The patent introduces dynamic selection of DM-RS patterns based on transmission conditions. The system can adaptively choose between different DM-RS patterns (first pattern with higher overhead, second pattern with lower overhead) depending on channel conditions, interference levels, and traffic requirements, thereby dynamically optimizing the trade-off between channel estimation accuracy and data transmission capacity.
Solution Approach 2:
The patent changes the parameters of DM-RS patterns by defining at least two different patterns with different densities and configurations. By selecting appropriate patterns based on conditions, the system adjusts the reference signal density parameter to maintain sufficient channel estimation accuracy while maximizing data transmission capacity.
3Reliability
If DM-RS patterns are optimized for specific scenarios, then performance in those scenarios improves, but adaptability to other scenarios decreases
Solution Approach 1:
The patent creates a universal DM-RS pattern framework that can serve multiple scenarios. By defining a set of different DM-RS patterns that can be selectively applied, the system achieves multi-functionality where the same framework adapts to various transmission modes, interference conditions, and channel characteristics, rather than requiring separate optimized patterns for each scenario.
Solution Approach 2:
The system dynamically adapts DM-RS patterns to match current transmission scenarios. Rather than being fixed for specific scenarios, the patterns can be changed based on real-time conditions, allowing the system to optimize performance for the current scenario while maintaining the capability to adapt to other scenarios when conditions change.
Data Source
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AI summary
Systems and methods are disclosed which implement one or more overhead reduction technique, if channel conditions favorable to implementation of overhead reduction are present. The one or more overhead reduction technique may have one or more restriction corresponding to the channel for which the overhead reduction technique is implemented. The one or more overhead reduction technique implemented may include time-domain bundling, frequency-domain bundling, and pattern adaptation. Pattern adaptation may include pattern code-domain reduction, pattern timing-domain reduction, and pattern frequency-domain reduction.