Low-rank preconditioner from top eigenpairs of the covariance matrix via randomized EVD with QRC, applied in beamspace, reduces CG iterations by 2-3x for long-term beamforming while matching exact inversion SINR.
Extreme Massive MIMO for Macro Cell Capacity Boost in 5G-Advanced and 6G
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Subspace nulling on long-term statistics preconditions the LTBF covariance matrix to reduce CG iterations and improve numerical stability in massive MU-MIMO.
Long-term beamforming projection matrices can be optimally computed via matrix inverse square root to maximize capacity bounds, yielding near-instantaneous MMSE performance with far lower overhead in rural uplink ray-tracing simulations.
citing papers explorer
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Low-rank Preconditioning in Beamspace Domain For Massive MU-MIMO Long-Term Beamforming
Low-rank preconditioner from top eigenpairs of the covariance matrix via randomized EVD with QRC, applied in beamspace, reduces CG iterations by 2-3x for long-term beamforming while matching exact inversion SINR.
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Interference Suppression for Massive MU-MIMO Long-Term Beamforming with Matrix Inversion Approximation
Subspace nulling on long-term statistics preconditions the LTBF covariance matrix to reduce CG iterations and improve numerical stability in massive MU-MIMO.
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Scalable Long-Term Beamforming for Massive Multi-User MIMO
Long-term beamforming projection matrices can be optimally computed via matrix inverse square root to maximize capacity bounds, yielding near-instantaneous MMSE performance with far lower overhead in rural uplink ray-tracing simulations.