3 papers
eess.SP2026
Low-rank Preconditioning in Beamspace Domain For Massive MU-MIMO Long-Term Beamforming
Amirreza Kiani, Ali Rasteh, Marco Mezzavilla +1
Long-term beamforming substantially reduces the channel estimation and inversion overhead of conventional massive MU-MIMO receivers; yet, its construction still hinges on the inver…
eess.SP2026
Scalable Long-Term Beamforming for Massive Multi-User MIMO
Ali Rasteh, Amirreza Kiani, Marco Mezzavilla +1
Fully digital massive MIMO systems with large numbers (1000+) of antennas offer dramatically increased capacity gains from spatial multiplexing and beamforming. Designing digital r…
eess.SP2026
Interference Suppression for Massive MU-MIMO Long-Term Beamforming with Matrix Inversion Approximation
Amirreza Kiani, Ali Rasteh, Marco Mezzavilla +1
Long-term beamforming (LTBF) is a widely-used scalable alternative to instantaneous multi-user MIMO processing that leverages slowly varying spatial channel statistics. VLSI implem…