Neural Network Antenna Selection for OFDM Error Rate
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Solution Overview
Problem
Existing antenna diversity receivers in OFDM wireless communication systems face inefficiencies in selecting the optimal reception antenna due to time-consuming binary error rate calculations, especially when frequent changes occur.
Innovation Solution
A process utilizing a neural network to estimate binary error rates based on the frequency response of transmission channels, leveraging a fast Fourier transform module and a multilayer perceptron model, which learns to determine the necessary signal power for a predetermined error rate, allowing for rapid and efficient antenna selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binary error rate calculation is performed by decoding a large number of bits from the preamble, then measurement precision of the transmission channel quality is improved, but loss of time increases due to the expensive calculation time
Solution Approach 1:
The neural network is pre-trained offline with大量的preamble data to learn the relationship between frequency response characteristics and binary error rates. During runtime, the network directly estimates error rates from frequency response without performing expensive decoding operations, thus achieving accurate measurement with minimal time loss
Solution Approach 2:
The patent replaces the traditional mechanical decoding-based error rate calculation method with a neural network-based estimation approach. The neural network processes frequency response data through learned patterns rather than through sequential bit decoding, dramatically reducing calculation time while maintaining measurement accuracy
2Measurement precision
If traditional binary error rate calculation method is used, then measurement precision is improved, but productivity decreases due to inability to perform frequent antenna switching
Solution Approach 1:
The neural network performs preliminary learning offline to capture the relationship between channel characteristics and error rates. This pre-computed knowledge enables rapid online estimation that supports frequent antenna switching while maintaining measurement accuracy
Solution Approach 2:
The patent substitutes the slow, sequential decoding-based measurement system with a fast neural network estimation system. This substitution enables the receiver to evaluate multiple antennas rapidly and switch between them frequently, greatly improving productivity while preserving measurement precision
3Productivity
If neural network estimation method is used, then productivity is improved by enabling rapid antenna selection, but device complexity increases due to adding neural network components
Solution Approach 1:
The neural network shares the frequency response calculation module with the existing OFDM receiver, utilizing the same FFT processing infrastructure. This multi-functionality approach adds estimation capability without duplicating existing components, thereby limiting the increase in device complexity
Solution Approach 2:
The patent introduces a neural network as an intermediary component that bridges the frequency response data and the antenna selection decision. This intermediary processes the data through learned patterns to produce error rate estimates, enabling rapid antenna selection while keeping the overall system architecture relatively simple
4Reliability
If frequent antenna switching is performed, then reliability is improved by selecting the best transmission channel, but loss of time increases due to repeated calculations
Solution Approach 1:
The neural network is pre-trained with extensive data to capture the statistical relationships between channel characteristics and error rates. This preliminary action enables the system to perform rapid error rate estimation during antenna switching without repeated expensive decoding operations, thus maintaining reliability while reducing time loss
Solution Approach 2:
The patent replaces the time-consuming decoding-based error rate calculation with neural network estimation. This substitution allows frequent antenna switching to be performed rapidly, enabling the system to select the most reliable transmission channel without incurring significant time penalties from repeated calculations
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 enables swift and accurate selection of the antenna with the lowest binary error rate, improving the reliability of the transmission link by automating the choice based on estimated error rates and safety margins, thus enhancing the robustness of the communication system.
Implementation Method 1
A process for selecting a transmission channel in a wireless digital communication system with antenna diversity uses a neural network fed with data representative of the frequency response of the transmission channel calculated by a module for calculating the fast Fourier transform
Implementation Method 2
the process consists in estimating the binary error rate for each transmission channel by feeding a neural network with data representative of the frequency response of the transmission channel
Data Source
AI summary
A process for selecting a transmission channel from several transmission channels of a receiver of Orthogonal Frequency Division Multiplexing (OFDM) radio signals with antenna diversity, with a view to favoring the transmission channel delivering a signal with the lowest binary error rate, consists in estimating the binary error rate for each transmission channel by feeding a neural network with data Radio Frequency Channel (RFC) representative of the frequency response of the transmission channel.