Long Training Sequence Generation for Wi-Fi Channel Estimation
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Solution Overview
Problem
Existing Wi-Fi systems use the same long training sequence for all transmitting ends, leading to system interference and poor channel estimation accuracy due to inadequate autocorrelation properties of the LTF sequence, which cannot distinguish between transmitting ends at the physical layer.
Innovation Solution
Generating a plurality of long training sequences through cyclic shifts of basic sequences and configuring a mapping rule between terminal devices and long training sequences, allowing each device to select a unique sequence for signal transmission, thereby improving channel estimation accuracy and reducing interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If all transmitting ends use the same long training sequence, then the system is simple to implement, but system interference increases and channel estimation accuracy deteriorates
Solution Approach 1:
The patent segments the single long training sequence into multiple distinct sequences (first long training sequence and second long training sequence) that are different from each other. Each transmitting end device is assigned a specific sequence, which divides the originally unified sequence resource into multiple independent segments. This segmentation reduces system interference by ensuring that simultaneous transmissions from different devices do not use identical sequences, thereby resolving the contradiction between implementation simplicity and interference reduction.
Solution Approach 2:
The patent applies local quality by making each transmitting end device use a different long training sequence specifically tailored to its identity or characteristics. Instead of a uniform sequence applied globally, each device has a locally optimized sequence that improves channel estimation accuracy for that specific device while minimizing interference to others. This local differentiation resolves the contradiction by maintaining simplicity in the overall system architecture while introducing necessary variation at the device level.
2Device complexity
If all transmitting ends use the same long training sequence, then the system is simple to implement, but channel estimation accuracy deteriorates
Solution Approach 1:
The patent segments the channel estimation process by assigning different long training sequences to different transmitting end devices. This segmentation allows the receiving end to distinguish between signals from different devices and perform accurate channel estimation for each individual device. The segmentation of sequences directly enables improved measurement precision in channel estimation while maintaining relatively simple system implementation, resolving the contradiction between complexity and accuracy.
Solution Approach 2:
The patent implements local quality by providing each transmitting end device with a unique long training sequence that is optimized for its specific channel characteristics or device identity. This local customization of sequences improves the accuracy of channel estimation for each device individually. The receiving end can leverage these distinct sequences to accurately estimate channels from different devices, thereby resolving the contradiction between implementation simplicity and channel estimation precision.
3Device complexity
If the LTF sequence lacks good autocorrelation properties, then the system is simpler to implement, but the ability to resist frequency offset and identify transmitting ends deteriorates
Solution Approach 1:
The patent applies local quality by designing long training sequences with specific autocorrelation properties tailored to each transmitting end device. Each device uses a sequence that has been optimized to provide good autocorrelation characteristics, which enables reliable frequency offset resistance and accurate device identification. This local optimization of sequence properties resolves the contradiction by maintaining relatively simple overall system design while achieving high reliability in frequency offset resistance through carefully designed device-specific sequences.
Data Source
AI summary
The present disclosure discloses a method and an apparatus for generating a long training sequence and sending a signal, and belongs to the field of wireless communications. The method includes: obtaining a plurality of long training sequences according to a system parameter and a preset sequence construction formula, wherein the plurality of long training sequences include a plurality of basic training sequences and a plurality of shift training sequences obtained according to cyclic shift of the basic training sequences; and configuring a mapping rule between a terminal device and a long training sequence, for enabling the terminal device to select a long training sequence according to the mapping rule as a long training sequence for sending a signal. By adopting the present disclosure, energy consumption of the receiving end may be reduced and accuracy of channel estimation may be improved.