Noise Estimation for Power Line Communication Impulsive Noise
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Solution Overview
Problem
Existing noise estimation methods in communication systems, such as power line communication, are inadequate for handling non-stationary impulsive noise, leading to inaccurate signal-to-noise ratio (SNR) estimation and performance issues due to factors like packet length and impulse offset.
Innovation Solution
A method that accounts for the power, duration, and occurrence of impulsive noise, remapping it into an equivalent SNR for stationary noise conditions, allowing for accurate estimation and detection of noise even when its amplitude is comparable to the signal, and considering random pulses outside received packets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SNR estimation methods are used in PLC systems, then the estimation is simple to implement, but the accuracy deteriorates due to non-stationary impulsive noise
Solution Approach 1:
The patent segments the received signal into multiple symbols and processes each symbol individually to detect impulsive noise. By dividing the signal into discrete symbol segments and analyzing their statistical properties, the method achieves accurate SNR estimation in the presence of non-stationary impulsive noise while maintaining computational feasibility through systematic processing of each segment.
Solution Approach 2:
The patent introduces an intermediary statistical analysis step that examines the distribution of received signal magnitudes across multiple symbols. This intermediary process characterizes the impulsive noise properties and uses them to correct the SNR estimation, acting as a mediator between the raw received signal and the final SNR calculation.
2Reliability
If MCS selection is based on conventional SNR estimation, then the selection process is straightforward, but performance deteriorates under impulsive noise conditions
Solution Approach 1:
The patent implements a feedback mechanism where the estimated impulsive noise characteristics are fed back into the SNR calculation process. This corrected SNR information then feeds back into the MCS selection algorithm, creating a closed-loop system that adapts to impulsive noise conditions and improves selection reliability while maintaining operational simplicity through automated feedback processing.
Solution Approach 2:
The patent changes the parameters used in SNR estimation by incorporating impulsive noise statistics (such as the ratio of impulsive to Gaussian noise power) into the calculation. This parameter modification allows the MCS selection to account for non-stationary noise conditions while preserving the straightforward nature of the selection process through standardized parameter adjustments.
3Productivity
If packet-based transmission is used, then data communication is efficient, but SNR estimation accuracy deteriorates due to packet length and impulse offset effects
Solution Approach 1:
The patent performs preliminary analysis of the received signal statistics before final SNR calculation. By pre-characterizing the impulsive noise properties from the received packets and computing correction factors in advance, the method eliminates the adverse effects of packet length and impulse offset on SNR estimation accuracy while maintaining efficient packet-based transmission.
Solution Approach 2:
The patent introduces dynamic adaptation to packet-based transmission by adjusting the SNR estimation process according to observed impulsive noise patterns in each packet. The method dynamically calculates correction factors based on the actual packet content and noise characteristics, making the estimation accurate regardless of packet length or impulse timing while preserving transmission efficiency.
Data Source
AI summary
Noise in a communication channel is estimated by, in the absence of transmitted information packets, obtaining a plurality of sets of signal samples, and estimating noise power levels associated with the sets of signal samples and allotted to respective noise power classes. In the presence of at least one transmitted information packet, an information packet power level is estimated. A set of signal-to-noise ratios computed between the information packet power level and the noise power levels in the respective noise power classes are compared against a signal-to-noise threshold and partitioned into a first subset and a second subset of signal-to-noise ratios failing to exceed/exceeding, respectively, the threshold. One or more resulting impulsive noise parameters are computed as a function of impulsive noise parameters indicative of noise power levels in the signal-to-noise ratios in the first subset while disregarding impulsive noise parameters indicative of noise power levels in the second subset.