Efficient Channel Feedback Scheme for Multi-User MIMO Hybrid Beamforming Systems.

Area of Science:

• Wireless Communications
• Signal Processing
• Information Theory

Background:

• Massive Machine Type Communication (mMTC) in 5G necessitates efficient handling of high-dimensional channel information for multi-user MIMO (MU-MIMO) hybrid beamforming.
• The substantial feedback overhead associated with detailed channel state information hinders practical MU-MIMO system performance.
• Existing methods struggle to balance feedback reduction with accurate channel recovery for complex wireless environments.

Purpose of the Study:

• To propose an efficient channel information feedback scheme for MU-MIMO hybrid beamforming systems.
• To significantly reduce feedback overhead while maintaining accurate channel state information.
• To enhance the achievable sum rate and precoding flexibility in 5G mMTC scenarios.

Main Methods:

• Utilizing common channel sparsity across a wide frequency band identified via Minimum Mean Squared Error Orthogonal Matching Pursuit (MMSE-OMP).
• Employing a nonlinear quantization scheme based on conditional Random Vector Quantization (RVQ) with the Linde-Buzo-Gray (LBG) algorithm.
• Recovering channel information at the transmitter using feedback indices of the common sparse basis and quantized sparse vectors.

Main Results:

• The proposed scheme achieves a lower Minimum Mean Squared Error (MMSE) for channel recovery compared to linear quantization methods.
• Enables flexible analog and digital precoding matrix adaptation at the transmitter.
• Demonstrates a higher sum rate compared to conventional codebook-based MU-MIMO precoding schemes.

Conclusions:

• The developed channel feedback scheme effectively reduces overhead in MU-MIMO systems.
• The combination of MMSE-OMP and conditional RVQ offers superior channel recovery and performance gains.
• This approach is highly suitable for future 5G mMTC deployments requiring efficient beamforming.