Analog AGC Using Signal Distributions for ADC Range Control
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Solution Overview
Problem
Receivers in wireless communication systems face challenges in maintaining optimal signal power levels due to significant variations in received signal power, leading to nonlinear distortion and reduced dynamic range, which existing gain control methods inadequately address.
Innovation Solution
The implementation of analog automatic gain control (AGC) based on estimated distributions of signal characteristics, using statistical models to update probability distributions with each digital measurement, allowing for near-optimal sequential gain decisions and adjusting signal levels to maintain optimal power within the ADC dynamic range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional gain control methods are used to adjust signal power levels, then the receiver can handle some variations in signal power, but significant variations still cause nonlinear distortion and reduced dynamic range
Solution Approach 1:
The patent implements dynamic gain adjustment through sequential AGC decisions that adapt to varying signal conditions. The system continuously monitors signal characteristics and adjusts gain in real-time based on estimated distributions, transforming the static gain control into a dynamic response mechanism that maintains reliability across varying signal powers while preventing nonlinear distortion.
Solution Approach 2:
The system changes the gain parameter dynamically based on estimated signal characteristics. By treating signal characteristics as random variables and updating probability distributions, the system optimizes the gain parameter to maintain the signal within the ADC dynamic range, thereby preventing nonlinear distortion while ensuring reliable receiver operation.
2Adaptability or versatility
If the signal level is out of the dynamic range of the ADC, then the receiver can still process the signal, but existing gain control methods cannot provide optimal performance
Solution Approach 1:
The system performs preliminary estimation of signal characteristics by treating them as random variables and establishing initial probability distributions before actual signal processing. This preliminary action allows the system to prepare optimal gain decisions in advance, improving adaptability to out-of-range signals while maintaining measurement precision through statistically informed gain control.
Solution Approach 2:
The system implements feedback through sequential updating of probability distributions based on observed signal characteristics. Each new measurement refines the estimated distribution, allowing the system to adapt to out-of-range signals while maintaining accurate gain control through continuous feedback from the actual signal behavior.
3Reliability
If statistical models and probability distribution updates are implemented, then near-optimal sequential AGC gain decisions can be achieved, but the computational complexity increases
Solution Approach 1:
The patent segments the gain control process into discrete sequential decisions, each based on updated probability distributions. By dividing the continuous control problem into discrete steps with defined probability updates, the system achieves reliable gain control while managing computational complexity through structured, modular processing.
Solution Approach 2:
The system uses the observed signal characteristics themselves to update the probability distributions and make gain decisions, rather than requiring external control inputs. This self-service approach allows the system to adapt autonomously, improving reliability while minimizing the need for complex external control mechanisms.
Data Source
AI summary
Devices, systems and methods for analog automatic gain control (AGC) based on estimated distributions of signal characteristics are described. One example method includes generating a digital measurement of a first signal characteristic based on a second signal characteristic of an input signal received by the device, where the second signal characteristic is based on a level of the input signal. The digital measurement of the signal characteristic is used to update a statistical model for the input signal and the posterior probability distribution of the second signal characteristic conditioned on previous digital measurements of the first signal characteristic. The updated posterior probability distribution is used to generate a gain estimate, which is then used to adjust the power level of the input signal. Another example method may use multiple statistical models for each waveform supported by the system.