TDD Precoding Iteration Optimization via Feedback
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Solution Overview
Problem
Existing precoding methods for TDD data transmission systems, such as iterative time reversal (ITR) and MMSE precoders, face challenges in determining the optimum number of iterations and accurately estimating signal-to-noise ratio (SNR), leading to suboptimal performance and high computational complexity.
Innovation Solution
A precoding method that dynamically determines an optimum iterative time reversal precoder by iteratively updating the number of iterations based on pilot signal data rates and applies an offset to the precoder using a power difference calculation, eliminating the need for accurate SNR estimation and reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If iterative time reversal (ITR) precoding is used to focus signals on receive antennas, then signal focusing performance is improved, but determining the optimum number of iterations becomes complex and computationally intensive
Solution Approach 1:
The patent implements feedback by having the receiver measure the signal quality (e.g., SINR) for different iteration counts and feed this information back to the transmitter. The transmitter then uses this feedback to automatically select the optimal number of iterations, eliminating the need for complex theoretical optimization while maintaining high focusing precision.
Solution Approach 2:
The patent makes the iteration count dynamic rather than fixed. The system adapts the number of iterations based on channel conditions and feedback from the receiver, allowing optimal performance across varying scenarios without requiring complex predetermined optimization.
2Productivity
If MMSE precoder is used to maximize data rate, then data transmission efficiency is improved, but accurate SNR estimation is required which increases computational complexity
Solution Approach 1:
The receiver measures the actual signal quality metrics including effective SNR based on received pilot signals and channel estimates, then feeds this information back to the transmitter. This eliminates the need for complex theoretical SNR estimation at the transmitter while enabling accurate MMSE precoding adaptation.
Solution Approach 2:
The receiver performs the SNR measurement and evaluation work locally using its own received signals and channel knowledge, then provides this information to the transmitter. This self-service approach shifts the computational burden from the transmitter to the receiver, simplifying the overall system.
3Manufacturing precision
If the number of iterations is increased to improve precoding accuracy, then signal focusing is improved, but computational time and complexity increase
Solution Approach 1:
The system uses feedback from the receiver about signal quality at different iteration levels to determine the point of diminishing returns. This allows the system to stop iterations when additional iterations no longer provide significant performance improvement, optimizing the trade-off between accuracy and computational time.
Solution Approach 2:
The patent changes the iteration parameter dynamically based on channel conditions and feedback. Rather than using a fixed high number of iterations, the system adjusts the iteration count as a variable parameter to achieve sufficient precision with minimal computational effort for each specific scenario.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach automatically converges to a precoder achieving maximum data rate with minimal calculation, overcoming the limitations of existing methods by dynamically adjusting iterations and simplifying SNR estimation, thereby enhancing data transmission efficiency.
Implementation Method 1
Time reversal is a technique that was originally used in the field of soundwaves and that relies on the wave equation being invariant with respect to time reversal. Thus, a time-reversed wave propagates like a direct wave going backwards in time.
Implementation Method 2
When a short pulse transmitted from an origin point propagates through a propagation medium, and a portion of this wave as received by a destination point is time reversed prior to being sent back through the propagation medium, then the time-reversed wave converges on the origin point where it reconstitutes a short pulse.
Data Source
AI summary
An iterative precoding method for a TDD data transmission system includes a transmitter provided with N transmit antenna(s) (N≧1), and a receiver provided with M receive antennas (M≧2). A series of precoders Ln (n≧0) is defined. Each iteration includes: the transmitter takes account of a predetermined value n=n0 if it is the first iteration, or else a value of n obtained during the preceding iteration; the transmitter sends a triplet of pilot signals precoded with the precoders Ln, Ln+1, and Ln+2 to the receiver; the receiver estimates the triplet (Tn, Tn+1, Tn+2) of total data rates that can be achieved corresponding respectively to (Ln, Ln+1, Ln+2), and deduces therefrom the value of a control command p; the receiver sends a signaling message specifying the value of the control command p; and on receiving the signaling message, the transmitter updates the value of n, by replacing it with the value (n+p).