Iterative Likelihood Computation for Wireless Signal Reconstruction
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Solution Overview
Problem
Current communication systems face challenges in reconstructing decoded information words from signals received on receiver devices due to error propagation and latency issues, particularly when using Differential Space Time Block Codes (DSTBC) or Differential Space Frequency Block Codes (DSFBC), which require knowledge of modulated symbols or information words not available on the receiver side.
Innovation Solution
An iterative reconstruction process is implemented on the receiver device, involving scaling and de-interleaving modules to compute likelihoods without requiring knowledge of modulated symbols or information words, using approximated scaling factors and equivalent channel models to achieve equal noise variance and reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact scaling factors and equivalent channel models are used for likelihood computation, then reconstruction accuracy is improved, but computational complexity and requirement for unavailable information increase
Solution Approach 1:
The patent uses approximated scaling factors and approximated equivalent channel models as simplified substitutes for the exact versions. These approximations are computationally cheaper and can be computed independently without requiring unavailable transmitted symbol information, thereby reducing computational complexity while maintaining adequate reconstruction accuracy
Solution Approach 2:
The patent changes the parameters used in likelihood computation from exact values (which require transmitted symbol information) to approximated values (independent of transmitted symbols). This parameter substitution allows the receiver to compute likelihoods with reasonable accuracy without accessing information that is not available at the receiver side
2Loss of time
If approximated scaling factors are used to compute likelihoods independently, then latency and error propagation are reduced, but reconstruction precision deteriorates
Solution Approach 1:
The patent computes approximated scaling factors and approximated equivalent channel models in advance, independently of the likelihood computation step. This preliminary computation allows the receiver to have ready-to-use parameters without waiting for decoded information from previous iterations, thereby reducing latency and avoiding error propagation while maintaining adequate precision through careful approximation design
3Measurement precision
If transmitted symbol information is used for scaling factor computation, then likelihood accuracy is improved, but error propagation and latency increase
Solution Approach 1:
The patent extracts and removes the dependency on transmitted symbol information from the scaling factor computation process. By designing approximated scaling factors that can be computed independently of the transmitted symbols, the patent eliminates the error propagation pathway that would otherwise exist when using exact scaling factors based on decoded information
Solution Approach 2:
The patent introduces approximated scaling factors as an intermediary between the received signal and the likelihood computation. These intermediaries provide the necessary scaling for accurate likelihood computation without requiring direct access to transmitted symbol information, thereby breaking the error propagation chain while maintaining computational accuracy
Data Source
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AI summary
Method for computing likelihoods in a process for reconstructing decoded information words from vectors of observations received from a wireless channel and generated from vectors of transmission symbols using a plurality Alamouti matrix based Differential Space Time Block Codes coder. The method comprises applying an iterative reconstruction process, comprising, in a current iteration: receiving (71) a vector of observations; determining (73), at least one vector of approximated scaling factors to be applied to the vector of observations; determining (75, 76, 77) for each vector of approximated scaling factors a vector of approximated scaled equivalent channel models; and, computing (78) likelihoods in the form of values representative of joint probabilities of obtaining the vector of observations knowing a vector of modulated symbols and a model of the wireless channel using each determined vector of approximated scaling factors and each determined vector of approximated scaled equivalent channel models.