Block Equalization Using Prior and Future Autocorrelation Estimates
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Solution Overview
Problem
Current block equalization techniques in wireless communications face challenges in efficiently handling multi-path and fading channel conditions, particularly in wideband waveforms, requiring significant computational resources and channel knowledge, and may not provide desired performance under significant fading and interference.
Innovation Solution
A wireless communications device and method that includes a demodulator with a channel estimation module, autocorrelation module, and transformation module to generate and transform channel matching coefficients using weighted averages of prior and current matrices, allowing for accurate block equalization with reduced computational complexity by simplifying factorization and transformation calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If block equalization is used to decrease computing overhead, then computational complexity is reduced, but channel knowledge requirements increase
Solution Approach 1:
The patent performs channel estimation in advance using known pilot symbols before the actual data transmission. The channel impulse response is estimated using the received signal and known pilots, then stored for use during equalization. This preliminary channel knowledge acquisition allows the block equalizer to operate with reduced real-time computational complexity while maintaining performance.
Solution Approach 2:
The patent introduces an intermediary channel estimation module that bridges the transmitter and receiver. This module processes the known pilot symbols to generate channel impulse response estimates, which then serve as input to the block equalizer. This intermediary approach separates the channel knowledge acquisition from the equalization process, reducing the direct computational burden on the equalizer.
2Measurement precision
If symbol-based equalization is used to compensate for multipath, then equalization accuracy is improved, but computing overhead increases
Solution Approach 1:
The patent segments the equalization process into two distinct phases: channel estimation phase (using known pilots) and equalization phase (using estimated channel response). By dividing the task this way, the system achieves accurate equalization without the excessive computational overhead of symbol-by-symbol updates, as the channel response is estimated once and then applied to entire blocks of symbols.
Solution Approach 2:
The patent performs all necessary channel characterization computations in advance using known pilot symbols. The channel impulse response is fully estimated before the actual data equalization begins. This preliminary action eliminates the need for continuous or per-symbol computations during data transmission, significantly reducing ongoing computing overhead while maintaining equalization accuracy.
3Reliability
If channel estimation is performed using known symbols, then equalization performance is improved, but data transmission time is reduced
Solution Approach 1:
The patent uses a relatively small number of known pilot symbols (e.g., 16-64 symbols) embedded within the data transmission. This partial sampling of the channel characteristics provides sufficient accuracy for the block equalizer to function effectively, while minimizing the overhead time. The system accepts that not all channel variations can be captured with limited pilots, but this trade-off maintains adequate performance for most practical applications.
Solution Approach 2:
The patent adjusts the number and distribution of pilot symbols based on channel conditions and requirements. By varying the pilot density and duration, the system can optimize between estimation accuracy and transmission speed. In channels with slower fading, fewer pilots are needed; in rapidly varying channels, more pilots are required. This parameter adaptation allows the system to maintain good equalization performance while maximizing data transmission rate.
Data Source
AI summary
A wireless communications device may include a wireless receiver for receiving signals comprising alternating known and unknown symbol portions, and a demodulator connected thereto. The demodulator may include a channel estimation module for generating respective channel estimates for a prior unknown symbol portion(s), current unknown symbol portion and for future unknown symbol portion(s). An autocorrelation module may generate autocorrelation matrices for the prior, current and future unknown symbol portions. A channel match filter module may generate respective channel matching coefficients for the prior and current/future unknown symbol portions, and a factorization module may divide the autocorrelation matrices into respective upper and lower autocorrelation matrices. A transformation module may transform the channel matching coefficients into upper and lower channel matching coefficients. A a back-substitution module may determine the current unknown symbol portion based upon the upper and lower autocorrelation matrices and channel matching coefficients for the current and prior/future unknown symbol portions.