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Sparse Sliding-Window Kernel Recursive Least-Squares Channel Prediction for Fast Time-Varying MIMO Systems.

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  • 1ZTE Corporation, Algorithm Department, Wireless Product R&D Institute, Wireless Product Operation Division, Shenzhen 518057, China.

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This study introduces a novel Sparse Sliding-Window Kernel Recursive Least-Squares (SSW-KRLS) algorithm for predicting fast-changing channel state information (CSI) in MIMO systems. The SSW-KRLS algorithm improves prediction accuracy and reduces computational load compared to existing methods.

Keywords:
MIMO systemchannel predictionkernel methodsrecursive least squarestime-varying channels

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Machine Learning

Background:

  • Accurate channel state information (CSI) is crucial for MIMO systems, especially at high speeds.
  • Fast time-varying CSI becomes outdated quickly and exhibits complex nonlinearities.
  • Traditional Kernel Recursive Least-Squares (KRLS) algorithms face storage and computation challenges for online prediction due to growing network structures.

Purpose of the Study:

  • To propose a novel Sparse Sliding-Window Kernel Recursive Least-Squares (SSW-KRLS) algorithm for efficient and accurate online prediction of nonlinear time-varying CSI.
  • To address the limitations of traditional KRLS algorithms in terms of storage and computational complexity for high-speed wireless scenarios.
  • To enhance the tracking of dynamic channel characteristics using a forgetting factor.

Main Methods:

  • Developed a SSW-KRLS algorithm that dynamically updates the kernel dictionary by selecting candidate discard sets based on correlation analysis.
  • Implemented regularization and a forgetting factor to maintain a fixed dictionary size, ensuring constant memory and computation per time step.
  • Validated the algorithm using numerical simulations under a realistic 3GPP channel model in a rich scattering environment.

Main Results:

  • The proposed SSW-KRLS algorithm demonstrated superior predictive accuracy and a smaller kernel dictionary size compared to the ALD-KRLS algorithm.
  • Specifically, at 120 km/h, SSW-KRLS achieved 2 dB lower Normalized Mean Squared Error (NMSE) and a 17% smaller kernel dictionary.
  • The algorithm effectively maintains performance while managing computational resources for online prediction.

Conclusions:

  • The SSW-KRLS algorithm offers an effective solution for online prediction of nonlinear time-varying CSI in high-speed mobile environments.
  • It overcomes the scalability issues of traditional KRLS, providing a practical approach for modern wireless communication systems.
  • The algorithm's efficiency and accuracy make it suitable for applications requiring real-time channel estimation.