Saeid Haghighatshoar

Massive MIMO Channel Subspace Estimation From Low-Dimensional Projections

Massive MIMO is a variant of multiuser MIMO (Multi-Input Multi-Output) system, where the number of basestation antennas M is very large and generally much larger than the number of spatially multiplexed data streams. Unfortunately, the front-end A/D conversion necessary to drive hundreds of antennas, with a signal bandwidth of 10 to 100 MHz, requires very large sampling bit-rate and power consumption. To reduce complexity, Hybrid Digital-Analog architectures have been proposed. Our work in this paper is motivated by one of such schemes named Joint Spatial Division and Multiplexing (JSDM), where the downlink precoder (resp., uplink linear receiver) is split into product of a baseband linear projection (digital) and an RF reconfigurable beamforming network (analog), such that only m ≪ M A/D converters and RF chains is needed. In JSDM, users are grouped according to similarity of their signal subspaces, and these groups are separated by the analog beamforming stage. Further multiplexing gain in each group is achieved using the digital precoder. Therefore, it is apparent that extracting the signal subspace of the M-dim channel vectors from snapshots of m-dim projections, with m ≪ M, plays a fundamental role in JSDM implementation. In this paper, we develop efficient subspace estimation algorithms that require sampling only m = O(2√M) antennas and, for a given p ≪ M, return a p-dim beamformer (subspace) that has a performance comparable with the best p-dim beamformer designed from the full knowledge of the exact channel covariance matrix. We assess the performance of our proposed estimators both analytically and empirically via numerical simulations.

A New Entropy Power Inequality for Integer-Valued Random Variables

The entropy power inequality (EPI) yields lower bounds on the differential entropy of the sum of two independent real-valued random variables in terms of the individual entropies. Versions of the EPI for discrete random variables have been obtained for special families of distributions with the differential entropy replaced by the discrete entropy, but no universal inequality is known (beyond trivial ones). More recently, the sumset theory for the entropy function yields a sharp inequality

Adaptive sensing using deterministic partial Hadamard matrices

H(X+X^{\prime})-H(X)\geq{{1}\over{2}}-o(1)

A Fast Hadamard Transform for Signals With Sublinear Sparsity in the Transform Domain

when

Polarization of the Rényi information dimension for single and multi terminal analog compression

X

A fast Hadamard transform for signals with sub-linear sparsity

,

The beam alignment problem in mmWave wireless networks

X^{\prime}

Robust microphone placement for source localization from noisy distance measurements

are independent identically distributed (i.i.d.) with high entropy. This paper provides the inequality

Signal recovery from unlabeled samples

H(X+X^{\prime})-H(X)\geq g(H(X))

Massive MIMO Pilot Decontamination and Channel Interpolation via Wideband Sparse Channel Estimation

, where