13 citations · 41 across the 36 of their papers we have counts for
11 papers · 1 filter
Oversampled Adaptive Sensing with Random Projections: Analysis and Algorithmic Approaches
Ralf R. Müller, Ali Bereyhi, Christoph F. Mecklenbräuker
Oversampled adaptive sensing (OAS) is a recently proposed Bayesian framework which sequentially adapts the sensing basis. In OAS, estimation quality is, in each step, measured by c…
On Robustness of Massive MIMO Systems Against Passive Eavesdropping under Antenna Selection
Ali Bereyhi, Saba Asaad, Ralf R. Müller +2
In massive MIMO wiretap settings, the base station can significantly suppress eavesdroppers by narrow beamforming toward legitimate terminals. Numerical investigations show that by…
GLSE Precoders for Massive MIMO Systems: Analysis and Applications
Ali Bereyhi, Mohammad Ali Sedaghat, Ralf R. Müller +1
This paper proposes the class of Generalized Least-Square-Error (GLSE) precoders for multiuser massive MIMO systems. For a generic transmit constellation, GLSE precoders minimize t…
Iterative Antenna Selection for Secrecy Enhancement in Massive MIMO Wiretap Channels
Ali Bereyhi, Saba Asaad, Rafael F. Schaefer +1
The growth of interest in massive MIMO systems is accompanied with hardware cost and computational complexity. Antenna selection is an efficient approach to overcome this cost-plus…
RLS Recovery with Asymmetric Penalty: Fundamental Limits and Algorithmic Approaches
Ali Bereyhi, Mohammad Ali Sedaghat, Ralf R. Müller
This paper studies regularized least square recovery of signals whose samples' prior distributions are nonidentical, e.g., signals with time-variant sparsity. For this model, Bayes…
Theoretical Bounds on MAP Estimation in Distributed Sensing Networks
Ali Bereyhi, Saeid Haghighatshoar, Ralf R. Müller
The typical approach for recovery of spatially correlated signals is regularized least squares with a coupled regularization term. In the Bayesian framework, this algorithm is seen…