650 research outputs found
Multidimensional sparse recovery for MIMO channel parameter estimation
Multipath propagation is a common phenomenon in wireless communication.
Knowledge of propagation path parameters such as complex channel gain,
propagation delay or angle-of-arrival provides valuable information on the user
position and facilitates channel response estimation. A major challenge in
channel parameter estimation lies in its multidimensional nature, which leads
to large-scale estimation problems which are difficult to solve. Current
approaches of sparse recovery for multidimensional parameter estimation aim at
simultaneously estimating all channel parameters by solving one large-scale
estimation problem. In contrast to that we propose a sparse recovery method
which relies on decomposing the multidimensional problem into successive
one-dimensional parameter estimation problems, which are much easier to solve
and less sensitive to off-grid effects, while providing proper parameter
pairing. Our proposed decomposition relies on convex optimization in terms of
nuclear norm minimization and we present an efficient implementation in terms
of the recently developed STELA algorithm
Deep Signal Recovery with One-Bit Quantization
Machine learning, and more specifically deep learning, have shown remarkable
performance in sensing, communications, and inference. In this paper, we
consider the application of the deep unfolding technique in the problem of
signal reconstruction from its one-bit noisy measurements. Namely, we propose a
model-based machine learning method and unfold the iterations of an inference
optimization algorithm into the layers of a deep neural network for one-bit
signal recovery. The resulting network, which we refer to as DeepRec, can
efficiently handle the recovery of high-dimensional signals from acquired
one-bit noisy measurements. The proposed method results in an improvement in
accuracy and computational efficiency with respect to the original framework as
shown through numerical analysis.Comment: This paper has been submitted to the 44th International Conference on
Acoustics, Speech, and Signal Processing (ICASSP 2019
- …