Receiving AFE Non-Linearity Estimation Through Gain-State Separation
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately estimating analog front-end non-linearity (AFE NL) due to the inability to distinguish between transmitter and receiving AFE non-linearities, leading to suboptimal signal-to-noise ratio (SNR) and increased retransmissions.
Innovation Solution
A two-step procedure is employed to estimate both transmitter and receiving AFE NLs by processing a reference signal with different gain states, allowing separation and accurate modeling of each component's non-linearity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single gain state is used for NL estimation, then the estimation process is simple, but the accuracy of separating transmitter NL and AFE NL is insufficient
Solution Approach 1:
The NL estimation process is segmented into two distinct steps: first estimating transmitter NL using a high gain state, then estimating AFE NL using a low gain state and the previously obtained transmitter NL model. This segmentation allows accurate separation of the two non-linearity sources while maintaining a systematic and manageable estimation procedure.
Solution Approach 2:
The transmitter NL is estimated in advance using a high gain state before estimating the AFE NL. This preliminary action allows the AFE NL estimation to focus solely on the AFE component by using the already-obtained transmitter NL model for compensation, thereby improving overall estimation accuracy.
2Measurement precision
If high gain state is used for AFE NL estimation, then AFE NL is reduced, but noise increases leading to lower SNR
Solution Approach 1:
The system dynamically adjusts the gain state based on the estimation phase: using high gain state for transmitter NL estimation and low gain state for AFE NL estimation. This dynamic adaptation allows optimal SNR conditions for each specific estimation task, improving overall measurement reliability.
Solution Approach 2:
The gain parameter is changed between two distinct states (high and low) depending on which NL component is being estimated. This parameter change enables the system to optimize the trade-off between NL magnitude and noise level for each estimation objective.
3Measurement precision
If transmitter NL and AFE NL are not separated, then the estimation process is simple, but the overall NL modeling accuracy is suboptimal
Solution Approach 1:
The total NL is segmented into two separate components (transmitter NL and AFE NL) that are estimated independently using different gain states. This segmentation enables accurate characterization of each component's non-linearity, which can then be combined for comprehensive NL compensation.
Solution Approach 2:
The high gain state acts as an intermediary that enables isolated measurement of transmitter NL by suppressing AFE NL effects. This intermediary measurement approach allows the system to separately characterize each non-linearity source without direct interference between them.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. The described techniques may enable a wireless device to determine a non-linearity (NL) model for a receiving analog front-end (RX AFE) of the wireless device in the presence of NL resulting from a transmitter used to transmit reference signals. The wireless device may determine the RX AFE NL by receiving a reference signal and processing the reference signal with a higher gain state (e.g., with low gain to result in a relatively lower RX AFE NL as compared to a lower gain state with high gain). The wireless device may accordingly separate the RX AFE NL and estimate the NL of the transmitter. The wireless device may receive and process a same reference signal via the RX AFE with a smaller gain state, and may use the processed reference signal and the estimated transmitter NL to estimate the RX AFE NL.