549 research outputs found
Low-complexity optimization for Two-Dimensional Direction-of-arrival Estimation via Decoupled Atomic Norm Minimization
This paper presents an efficient optimization technique for super-resolution
two-dimensional (2D) direction of arrival (DOA) estimation by introducing a new
formulation of atomic norm minimization (ANM). ANM allows gridless angle
estimation for correlated sources even when the number of snapshots is far less
than the antenna size, yet it incurs huge computational cost in 2D processing.
This paper introduces a novel formulation of ANM via semi-definite programming,
which expresses the original high-dimensional problem by two decoupled Toeplitz
matrices in one dimension, followed by 1D angle estimation with automatic angle
pairing. Compared with the state-of-the-art 2D ANM, the proposed technique
reduces the computational complexity by several orders of magnitude with
respect to the antenna size, while retaining the benefits of ANM in terms of
super-resolution performance with use of a small number of measurements, and
robustness to source correlation and noise. The complexity benefits are
particularly attractive for large-scale antenna systems such as massive MIMO
and radio astronomy
Gridless Two-dimensional DOA Estimation With L-shaped Array Based on the Cross-covariance Matrix
The atomic norm minimization (ANM) has been successfully incorporated into
the two-dimensional (2-D) direction-of-arrival (DOA) estimation problem for
super-resolution. However, its computational workload might be unaffordable
when the number of snapshots is large. In this paper, we propose two gridless
methods for 2-D DOA estimation with L-shaped array based on the atomic norm to
improve the computational efficiency. Firstly, by exploiting the
cross-covariance matrix an ANM-based model has been proposed. We then prove
that this model can be efficiently solved as a semi-definite programming (SDP).
Secondly, a modified model has been presented to improve the estimation
accuracy. It is shown that our proposed methods can be applied to both uniform
and sparse L-shaped arrays and do not require any knowledge of the number of
sources. Furthermore, since our methods greatly reduce the model size as
compared to the conventional ANM method, and thus are much more efficient.
Simulations results are provided to demonstrate the advantage of our methods
DNN-DANM: A High-Accuracy Two-Dimensional DOA Estimation Method Using Practical RIS
Reconfigurable intelligent surface (RIS) or intelligent reflecting surface
(IRS) has been an attractive technology for future wireless communication and
sensing systems. However, in the practical RIS, the mutual coupling effect
among RIS elements, the reflection phase shift, and amplitude errors will
degrade the RIS performance significantly. This paper investigates the
two-dimensional direction-of-arrival (DOA) estimation problem in the scenario
using a practical RIS. After formulating the system model with the mutual
coupling effect and the reflection phase/amplitude errors of the RIS, a novel
DNNDANM method is proposed for the DOA estimation by combining the deep neural
network (DNN) and the decoupling atomic norm minimization (DANM). The DNN step
reconstructs the received signal from the one with RIS impairments, and the
DANM step exploits the signal sparsity in the two-dimensional spatial domain.
Additionally, a semi-definite programming (SDP) method with low computational
complexity is proposed to solve the atomic minimization problem. Finally, both
simulation and prototype are carried out to show estimation performance, and
the proposed method outperforms the existing methods in the two-dimensional DOA
estimation with low complexity in the scenario with practical RIS.Comment: 11 pages, 12 figure
Channel Estimation for RIS-Aided MIMO Systems: A Partially Decoupled Atomic Norm Minimization Approach
Channel estimation (CE) plays a key role in reconfigurable intelligent
surface (RIS)-aided multiple-input multiple-output (MIMO) communication
systems, while it poses a challenging task due to the passive nature of RIS and
the cascaded channel structures. In this paper, a partially decoupled atomic
norm minimization (PDANM) framework is proposed for CE of RIS-aided MIMO
systems, which exploits the three-dimensional angular sparsity of the channel.
In particular, PDANM partially decouples the differential angles at the RIS
from other angles at the base station and user equipment, reducing the
computational complexity compared with existing methods. A reweighted PDANM
(RPDANM) algorithm is proposed to further improve CE accuracy, which
iteratively refines CE through a specifically designed reweighing strategy.
Building upon RPDANM, we propose an iterative approach named RPDANM with
adaptive phase control (RPDANM-APC), which adaptively adjusts the RIS phases
based on previously estimated channel parameters to facilitate CE, achieving
superior CE accuracy while reducing training overhead. Numerical simulations
demonstrate the superiority of our proposed approaches in terms of running
time, CE accuracy, and training overhead. In particular, the RPDANM-APC
approach can achieve higher CE accuracy than existing methods within less than
40 percent training overhead while reducing the running time by tens of times.Comment: 35 pages, 9 figures. Part of this paper has been submitted to the
2023 IEEE Global Communications Conference (GLOBECOM
A Coordinate Descent Approach to Atomic Norm Minimization
Atomic norm minimization is of great interest in various applications of
sparse signal processing including super-resolution line-spectral estimation
and signal denoising. In practice, atomic norm minimization (ANM) is formulated
as a semi-definite programming (SDP) which is generally hard to solve. This
work introduces a low-complexity, matrix-free method for solving ANM. The
method uses the framework of coordinate descent and exploits the
sparsity-induced nature of atomic-norm regularization. Specifically, an
equivalent, non-convex formulation of ANM is first proposed. It is then proved
that applying the coordinate descent framework on the non-convex formulation
leads to convergence to the global optimal point. For the case of a single
measurement vector of length N in discrete fourier transform (DFT) basis, the
complexity of each iteration in the coordinate descent procedure is O(N log N
), rendering the proposed method efficient even for large-scale problems. The
proposed coordinate descent framework can be readily modified to solve a
variety of ANM problems, including multi-dimensional ANM with multiple
measurement vectors. It is easy to implement and can essentially be applied to
any atomic sets as long as a corresponding rank-1 problem can be solved.
Through extensive numerical simulations, it is verified that for solving sparse
problems the proposed method is much faster than the alternating direction
method of multipliers (ADMM) or the customized interior point SDP solver
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