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A Bayesian Tensor Decomposition Method for Joint Estimation of Channel and Interference Parameters.

Yuzhe Sun1, Wei Wang1, Yufan Wang1

  • 1School of Information Engineering, Chang'an University, Xi'an 710064, China.

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Summary
This summary is machine-generated.

This study introduces a robust tensor variational method for estimating channel and interference parameters in MIMO-OFDM systems. The novel approach accurately identifies co-channel and front-end interference, outperforming existing methods.

Keywords:
automatic rank determinationchannel estimationinterference estimationrobust Bayesiantensor decomposition

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Area of Science:

  • Signal Processing
  • Wireless Communications
  • Tensor Analysis

Background:

  • Bayesian tensor decomposition is used for channel estimation with interference.
  • Existing methods struggle to accurately estimate interference parameters due to unconsidered interference types.
  • Accurate interference characterization is crucial for robust wireless system performance.

Purpose of the Study:

  • To develop a robust tensor variational method for joint channel and interference parameter estimation in MIMO-OFDM systems.
  • To incorporate a more realistic interference model, including co-channel interference (CCI) and front-end interference (FEI).
  • To improve upon traditional Bayesian tensor decomposition and other robust methods in terms of estimation accuracy.

Main Methods:

  • Utilized a CANDECOMP/PARAFAC (CP)-based additive interference model within a tensor variational framework.
  • Developed a method for joint estimation of channel and interference parameters in the time-frequency domain.
  • Employed the evidence lower bound (ELBO) for method validation.

Main Results:

  • The proposed method accurately estimates channel and interference parameters in MIMO-OFDM systems.
  • Simulation results demonstrate superior performance compared to traditional information-theoretic methods, tensor decomposition models, and robust CP (RCP) models.
  • The method effectively handles realistic interference scenarios, including CCI and FEI.

Conclusions:

  • The robust tensor variational method offers a significant advancement in interference parameter estimation for wireless communications.
  • This technique enhances anti-interference capabilities and has implications for dynamic spectrum allocation.
  • Accurate joint estimation of channel and interference parameters is achievable with the proposed approach.