collaborators

8 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

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…

eess.SP2026

A Self-Calibrating SDR for High Fidelity Beam- and Null-forming Arrays

Yongjun Kim, Aditya Dhananjay, Sundeep Rangan +5

Null forming is increasingly essential in modern wireless systems for spectrum-sharing, anti-jamming, and covert communications in contested and congested environments. Achieving d…

eess.SP2026

A Spatial Array for Spectrally Agile Wireless Processing

Ali Rasteh, Andrew Hennessee, Ishaan Shivhare +3

Massive MIMO is a cornerstone of next-generation wireless communication, offering significant gains in capacity, reliability, and energy efficiency. However, to meet emerging deman…

eess.SP2025

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.SP2025

Beyond Point Estimates: Likelihood-Based Full-Posterior Wireless Localization

Haozhe Lei, Hao Guo, Tommy Svensson +1

Modern wireless systems require not only position estimates, but also quantified uncertainty to support planning, control, and radio resource management. We formulate localization…