Adaptive FIR Equalizer Using Sample Subsets for ISI Mitigation
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Solution Overview
Problem
Current communication systems face challenges in achieving high-throughput data transmission due to inter-symbol interference and cross-talk, which existing filtering techniques struggle to effectively address, especially in wireless and optical communications.
Innovation Solution
An adaptive equalizer system implemented in a digital signal processor (DSP) circuit, utilizing a finite impulse response (FIR) filter with adaptive tap weights, which processes input digital samples to generate equalized samples and adjusts tap weights based on residual error estimates from selected subsets of equalized samples, including support and data samples with different modulation formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional filtering techniques are used to process received signals, then device complexity is reduced, but inter-symbol interference and cross-talk cannot be effectively mitigated, resulting in poor signal quality
Solution Approach 1:
The patent implements adaptive equalization where filter coefficients (tap weights) are dynamically adjusted based on incoming signal characteristics. The system transitions from static traditional filtering to dynamic adaptive filtering, allowing the equalizer to continuously optimize its response to changing channel conditions, thereby effectively mitigating inter-symbol interference and cross-talk while maintaining manageable complexity through structured adaptation algorithms
Solution Approach 2:
The system changes the parameters of the filtering system by introducing adaptive tap weights that are continuously updated based on error estimates. Instead of using fixed filter coefficients, the system modifies these parameters in real-time to adapt to varying signal conditions, enabling effective interference mitigation without requiring overly complex system architecture
2Reliability
If adaptive equalization with full sample processing is implemented, then inter-symbol interference and cross-talk are effectively mitigated, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts and processes only the most relevant samples for adaptation purposes. Instead of using all equalized samples for tap weight updates, the system selectively identifies and processes a subset of samples that provide the most valuable information for adaptation, thereby reducing computational overhead and processing time while maintaining effective interference mitigation
Solution Approach 2:
The system applies partial action by using a limited subset of samples for adaptation rather than processing the complete set of equalized samples. This selective approach provides sufficient adaptation information to effectively mitigate interference without the excessive computational burden of processing every available sample, achieving an optimal balance between performance and processing speed
Data Source
AI summary
One example includes an equalizer system. The system includes a filter system configured to receive digital sample blocks associated with an input signal and to provide equalized digital sample blocks associated with the respective digital sample blocks based on adaptive tap weights. Each of the digital sample blocks includes samples and each of the equalized digital sample blocks includes equalized samples. The system also includes a sample set selector to select a subset of equalized samples from each of the equalized digital sample blocks at the output of the filter and an error estimator configured to implement an error estimation algorithm on the subset of the equalized samples to determine a residual error associated with the equalized samples. The system further includes a tap weight generator configured to generate the adaptive tap weights in response to the residual error and to provide the adaptive tap weights to the filter.