Blind In-Service Noise Estimation for Adaptive Modulation
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Solution Overview
Problem
Existing communication systems face challenges in accurately estimating noise power in multi-signal environments, especially when signals are continuously present, leading to difficulties in setting optimal detection thresholds and degrading link performance.
Innovation Solution
A blind in-service noise estimation system that uses a decomposition of a covariance matrix of N data samples to derive noise power estimates, allowing for adaptive modulation and efficient resource allocation in scalar communication systems, even in the presence of multiple interfering signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional outlier rejection schemes are used to estimate noise power, then the method works well in high signal-to-noise ratio scenarios, but it fails when data records are contaminated with signal components
Solution Approach 1:
The patent segments the noise estimation process into multiple stages: first identifying signal components through correlation analysis, then separating noise estimation into distinct phases where noise-only intervals are isolated and processed independently. This segmentation allows accurate noise power estimation even when data is contaminated with signal components, resolving the contradiction between measurement precision and adaptability.
Solution Approach 2:
The patent introduces correlation analysis as an intermediary mechanism between the received signal and the noise estimation process. By using correlation to identify and flag signal-containing intervals, the system creates a mediator that separates useful signal information from noise estimation requirements, enabling accurate noise power measurement in contaminated data environments.
2Measurement precision
If specialized resources are allocated solely for noise estimation, then noise power can be accurately estimated, but communication system efficiency and throughput are reduced
Solution Approach 1:
The patent makes the existing signal processing resources universal by enabling them to serve dual purposes: both communication and noise estimation. The correlation analysis and data processing units perform noise estimation as part of their normal signal processing function, eliminating the need for separate dedicated noise estimation resources while maintaining estimation accuracy and preserving communication throughput.
Solution Approach 2:
The system performs self-service noise estimation where the signal processing operations themselves generate the data needed for noise estimation. The correlation analysis and data processing units utilize their own processing results to estimate noise power, eliminating the need for external specialized resources and maintaining full system productivity.
3Productivity
If noise estimation is performed in-service without reserving specialized resources, then communication efficiency is improved, but the complexity of processing increases
Solution Approach 1:
The patent merges noise estimation operations with existing signal processing functions. The correlation analysis, data buffering, and processing units that handle communication tasks also perform noise estimation by utilizing the same data streams and computational resources. This merging approach increases communication efficiency while managing complexity through functional integration rather than adding separate complex estimation systems.
Data Source
AI summary
A communications system includes a transmitter that transmits a modulated signal having encoded communications data over a communications channel. The transmitter adjusts one of at least modulation and coding at the transmitter based on the received channel state information of the transmitted signal. A receiver determines received signal metrics from the modulated signal. A noise power estimator estimates the noise power of the received communications signal by collecting N data samples from the communications signals, forming a covariance matrix of the N data samples based on a model order estimate, computing the eigenvalue decomposition of the covariance matrix and ranking resultant eigenvalues from the minimum to the maximum for determining the noise power of the received signal. At least one of modulation and coding are adjusted based on the monitored link quality of the communications channel to enhance use of the available channel capacity.