CHARM extracts ADPS priors from path-level radio maps to reduce 3D angle-delay-AoD search to 1D AoD search per path, delivering 34.8x speedup over joint OMP at T≤4 pilots with comparable accuracy and only 3.7 dB degradation under 0.2 rad dictionary mismatch via trust-region constraint.
Massive mimo for next generation wireless systems
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An iterative robust optimization framework jointly optimizes precoding, RIS reflection, common-rate allocation, and movable antenna positions to maximize sum-rate in multi-user RSMA systems under bounded CSI uncertainty.
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.
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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Path-Level Radio Map-Aided Fast and Robust Channel Estimation for Pilot-Starved MIMO-OFDM Systems
CHARM extracts ADPS priors from path-level radio maps to reduce 3D angle-delay-AoD search to 1D AoD search per path, delivering 34.8x speedup over joint OMP at T≤4 pilots with comparable accuracy and only 3.7 dB degradation under 0.2 rad dictionary mismatch via trust-region constraint.
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Channel Uncertainty-Aware Robust Beamforming for RIS-Assisted RSMA Communication With Movable Antennas
An iterative robust optimization framework jointly optimizes precoding, RIS reflection, common-rate allocation, and movable antenna positions to maximize sum-rate in multi-user RSMA systems under bounded CSI uncertainty.
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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.