41 research outputs found
FCFGS-CV-Based Channel Estimation for Wideband MmWave Massive MIMO Systems with Low-Resolution ADCs
In this paper, the fully corrective forward greedy selection-cross
validation-based (FCFGS-CV-based) channel estimator is proposed for wideband
millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems
with low-resolution analog-to-digital converters (ADCs). The sparse nature of
the mmWave virtual channel in the angular and delay domains is exploited to
convert the maximum a posteriori (MAP) channel estimation problem to an
optimization problem with a concave objective function and sparsity constraint.
The FCFGS algorithm, which is the generalized orthogonal matching pursuit (OMP)
algorithm, is used to solve the sparsity-constrained optimization problem.
Furthermore, the CV technique is adopted to determine the proper termination
condition by detecting overfitting when the sparsity level is unknown.Comment: to appear in IEEE Wireless Communications Letter
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
Learning sparse combinations is a frequent theme in machine learning. In this
paper, we study its associated optimization problem in the distributed setting
where the elements to be combined are not centrally located but spread over a
network. We address the key challenges of balancing communication costs and
optimization errors. To this end, we propose a distributed Frank-Wolfe (dFW)
algorithm. We obtain theoretical guarantees on the optimization error
and communication cost that do not depend on the total number of
combining elements. We further show that the communication cost of dFW is
optimal by deriving a lower-bound on the communication cost required to
construct an -approximate solution. We validate our theoretical
analysis with empirical studies on synthetic and real-world data, which
demonstrate that dFW outperforms both baselines and competing methods. We also
study the performance of dFW when the conditions of our analysis are relaxed,
and show that dFW is fairly robust.Comment: Extended version of the SIAM Data Mining 2015 pape