MU-MIMO Training Field Sub-carrier Segmentation for Phase Correction
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Solution Overview
Problem
Current uplink MU-MIMO channel estimation methods in wireless local area networks suffer from low precision and poor estimation performance due to carrier frequency offset and phase noise, particularly in the initial transmission phase where frequency fluctuations are significant.
Innovation Solution
The proposed solution involves a signal processing method and apparatus that modify the structure of the training field by dividing sub-carriers in the second part of the training field into N training sub-carrier sets in a manner similar to the first part, allowing each spatial flow to correspond to at least one sub-carrier in each TSS, enabling effective phase correction of channel estimation obtained in the first part, thereby improving precision and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the additional LTF in the second part of the training field is used as a repetition of LTF-1 for CFO estimation, then the structure is simple and easy to implement, but the channel estimation precision is low and estimation performance is poor
Solution Approach 1:
The patent divides the sub-carriers in the second part of the training field into N training sub-carrier sets (TSSs), with each spatial flow corresponding to one TSS in each OFDM symbol. This segmentation allows for more precise channel estimation by dedicating specific sub-carrier sets to each spatial flow, resolving the contradiction between implementation simplicity and estimation precision.
Solution Approach 2:
The patent assigns different sub-carrier sets to different spatial flows in the second part of the training field, creating local quality differentiation. Each spatial flow has dedicated sub-carriers for channel estimation, which improves the local estimation accuracy for each flow while maintaining overall system simplicity through structured allocation.
2Measurement precision
If sub-carriers in the second part of the training field are divided into N training sub-carrier sets with each spatial flow corresponding to one TSS, then channel estimation precision is improved, but the device complexity increases
Solution Approach 1:
By segmenting sub-carriers into N training sub-carrier sets and assigning each spatial flow to one TSS per OFDM symbol, the patent achieves precise channel estimation without requiring complex processing. The segmentation creates a regular, predictable structure that simplifies implementation despite the increased precision requirements.
Solution Approach 2:
The patent performs channel estimation using the divided TSSs in the second part of the training field before final signal processing. This preliminary action with structured sub-carrier allocation establishes accurate channel estimates early in the process, reducing the complexity of subsequent processing steps.
3Duration of action of moving object
If the training field structure maintains repetition of LTF-1 in the second part, then the training field duration is short, but the phase correction performance is insufficient due to carrier frequency offset
Solution Approach 1:
The patent segments the training field into two distinct parts with different functions: the first part for initial channel estimation and the second part with divided TSSs for precise phase correction. This segmentation allows the system to maintain short duration while achieving reliable phase correction through the structured sub-carrier allocation in the second part.
Solution Approach 2:
The second part of the training field with its divided TSSs acts as an intermediary between the initial channel estimation and the final signal processing. It provides the necessary phase correction information through its structured sub-carrier assignment, enabling reliable phase correction without significantly extending the overall training field duration.
Data Source
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AI summary
Embodiments of the present invention provide a signal processing method and apparatus, and a device, applied to an MU-MIMO system. The signal processing apparatus includes: a signal acquiring module and a sending module. A signal in the embodiments of the present invention is a signal including N spatial flows, and the signal includes a training field, where the training field includes a first part and a second part. Subcarriers of an OFDM symbol in the second part of the training field are divided into N TSSs in a division manner that is the same as a division manner of sub-carriers of an OFDM symbol in the first part of the training field, and each spatial flow corresponds to at least one sub-carrier in a TSS, in each frequency domain location, of the OFDM symbol in the second part of the training field. By means of a structure of the second part of the training field in the embodiments of the present invention, phase correction is effectively performed on channel estimation that is obtained in another OFDM symbol in the first part of the training field, so that precision of the channel estimation is improved, and estimation performance is improved.