122 citations · 306 across the 35 of their papers we have counts for
9 papers · 2 filters
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…
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…
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…
Optimal Transmit Antenna Selection for Massive MIMO Wiretap Channels
Saba Asaad, Ali Bereyhi, Amir M. Rabiei +2
In this paper, we study the impacts of transmit antenna selection on the secrecy performance of massive MIMO systems. We consider a wiretap setting in which a fixed number of trans…
Stepwise Transmit Antenna Selection in Downlink Massive Multiuser MIMO
Ali Bereyhi, Saba Asaad, Ralf R. Müller
Due to the large power consumption in RF-circuitry of massive MIMO systems, practically relevant performance measures such as energy efficiency or bandwidth efficiency are neither…
Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing
Ali Bereyhi, Ralf R. Müller
This paper studies the asymptotic performance of maximum-a-posteriori estimation in the presence of prior information. The problem arises in several applications such as recovery o…
Oversampled Adaptive Sensing
Ralf R. Müller, Ali Bereyhi, Christoph F. Mecklenbräuker
We develop a Bayesian framework for sensing which adapts the sensing time and/or basis functions to the instantaneous sensing quality measured in terms of the expected posterior me…
Precoding via Approximate Message Passing with Instantaneous Signal Constraints
Ali Bereyhi, Mohammad Ali Sedaghat, Ralf R. Müller
This paper proposes a low complexity precoding algorithm based on the recently proposed Generalized Least Square Error (GLSE) scheme with generic penalty and support. The algorithm…