OFDM Receiver SNR Estimation Using Cyclic Prefix Correlation
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Solution Overview
Problem
Existing SNR estimation methods for OFDM receivers are either data-aided, requiring pilot subcarriers, or non-data-aided (blind) methods that are computationally complex or dependent on channel estimation accuracy, and often require a large number of OFDM symbols for accurate estimation due to correlation issues during the Cyclic Prefix interval.
Innovation Solution
A method for estimating SNR in OFDM receivers that involves converting RF signals to digital samples, transforming them into the frequency domain, calculating power ratios between signal and noise samples, and using a Noise to Noise Ratio (NNR) to scale the SNR estimation, which is independent of signal and channel characteristics, allowing for low-complexity, blind SNR estimation applicable to both OFDM and single carrier signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data-aided schemes are used for SNR estimation, then pilot subcarriers or training symbols are required, but this increases device complexity and loss of information
Solution Approach 1:
The patent extracts and utilizes the correlation property inherent in the Cyclic Prefix interval of OFDM signals. By focusing specifically on this correlated portion of the signal, the method eliminates the need for external pilot subcarriers or training symbols, thereby reducing device complexity and information loss while maintaining SNR estimation accuracy
Solution Approach 2:
The OFDM signal's own Cyclic Prefix structure provides the necessary correlation information for SNR estimation. The method uses the signal's inherent temporal correlation during the CP interval, making the system self-sufficient without requiring additional pilot signals or training overhead
2Device complexity
If blind SNR estimation methods are used without pilot subcarriers, then device complexity is reduced, but measurement precision deteriorates due to correlation issues during Cyclic Prefix interval
Solution Approach 1:
The patent segments the OFDM signal processing into distinct phases: utilizing the Cyclic Prefix interval for correlation-based noise variance estimation, and separately processing the payload portion for SNR calculation. This segmentation allows the method to exploit the correlation property in the CP interval without being degraded by interference from previous symbols, thereby maintaining measurement precision while keeping device complexity low
3Measurement precision
If Maximum-Likelihood method is used for SNR estimation, then measurement precision is improved, but device complexity increases significantly
Solution Approach 1:
The patent employs a computationally efficient correlation-based estimation method that uses simple statistical properties of the Cyclic Prefix interval. This approach replaces the computationally intensive Maximum-Likelihood method with a much simpler calculation that achieves adequate SNR estimation accuracy without requiring high computational resources
4Measurement precision
If EM algorithm is used for SNR estimation, then measurement precision is improved, but device complexity increases and dependency on channel estimation accuracy is created
Solution Approach 1:
The patent extracts SNR estimation capability directly from the Cyclic Prefix correlation property without requiring channel estimation. By taking out the estimation process from the channel-dependent EM algorithm and replacing it with a channel-independent correlation method, the patent reduces computational complexity and eliminates dependency on channel estimation accuracy
Data Source
AI summary
This invention concerns the estimation of signal to noise ratio (SNR) at a communications receiver; it may be applicable to a wide range of receivers but is particularly suited for Orthogonal Frequency Division Multiplexing (OFDM) receivers. In particular the invention is a method, a receiver and software for performing the method. The signal to noise ratio (SNR) in the received signals is estimated by directly estimating the power ratio, in the received signal, between the part of the frequency spectrum of the received signal that contains only noise, and the part of the spectrum that contains both signal and noise; and averaging this value over a time interval.