830 research outputs found
A Robust Beamformer Based on Weighted Sparse Constraint
Applying a sparse constraint on the beam pattern has been suggested to
suppress the sidelobe level of a minimum variance distortionless response
(MVDR) beamformer. In this letter, we introduce a weighted sparse constraint in
the beamformer design to provide a lower sidelobe level and deeper nulls for
interference avoidance, as compared with a conventional MVDR beamformer. The
proposed beamformer also shows improved robustness against the mismatch between
the steering angle and the direction of arrival (DOA) of the desired signal,
caused by imperfect estimation of DOA.Comment: 4 pages, 2 figure
Sparse Array DFT Beamformers for Wideband Sources
Sparse arrays are popular for performance optimization while keeping the
hardware and computational costs down. In this paper, we consider sparse arrays
design method for wideband source operating in a wideband jamming environment.
Maximizing the signal-to-interference plus noise ratio (MaxSINR) is adopted as
an optimization objective for wideband beamforming. Sparse array design problem
is formulated in the DFT domain to process the source as parallel narrowband
sources. The problem is formulated as quadratically constraint quadratic
program (QCQP) alongside the weighted mixed -norm squared
penalization of the beamformer weight vector. The semidefinite relaxation (SDR)
of QCQP promotes sparse solutions by iteratively re-weighting beamformer based
on previous iteration. It is shown that the DFT approach reduces the
computational cost considerably as compared to the delay line approach, while
efficiently utilizing the degrees of freedom to harness the maximum output SINR
offered by the given array aperture
Group Sparse Precoding for Cloud-RAN with Multiple User Antennas
Cloud radio access network (C-RAN) has become a promising network
architecture to support the massive data traffic in the next generation
cellular networks. In a C-RAN, a massive number of low-cost remote antenna
ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed
low-latency fronthaul links, which enables efficient resource allocation and
interference management. As the RAPs are geographically distributed, the group
sparse beamforming schemes attracts extensive studies, where a subset of RAPs
is assigned to be active and a high spectral efficiency can be achieved.
However, most studies assumes that each user is equipped with a single antenna.
How to design the group sparse precoder for the multiple antenna users remains
little understood, as it requires the joint optimization of the mutual coupling
transmit and receive beamformers. This paper formulates an optimal joint RAP
selection and precoding design problem in a C-RAN with multiple antennas at
each user. Specifically, we assume a fixed transmit power constraint for each
RAP, and investigate the optimal tradeoff between the sum rate and the number
of active RAPs. Motivated by the compressive sensing theory, this paper
formulates the group sparse precoding problem by inducing the -norm as
a penalty and then uses the reweighted heuristic to find a solution.
By adopting the idea of block diagonalization precoding, the problem can be
formulated as a convex optimization, and an efficient algorithm is proposed
based on its Lagrangian dual. Simulation results verify that our proposed
algorithm can achieve almost the same sum rate as that obtained from exhaustive
search
Radio Astronomical Image Formation using Constrained Least Squares and Krylov Subspaces
Image formation for radio astronomy can be defined as estimating the spatial
power distribution of celestial sources over the sky, given an array of
antennas. One of the challenges with image formation is that the problem
becomes ill-posed as the number of pixels becomes large. The introduction of
constraints that incorporate a-priori knowledge is crucial. In this paper we
show that in addition to non-negativity, the magnitude of each pixel in an
image is also bounded from above. Indeed, the classical "dirty image" is an
upper bound, but a much tighter upper bound can be formed from the data using
array processing techniques. This formulates image formation as a least squares
optimization problem with inequality constraints. We propose to solve this
constrained least squares problem using active set techniques, and the steps
needed to implement it are described. It is shown that the least squares part
of the problem can be efficiently implemented with Krylov subspace based
techniques, where the structure of the problem allows massive parallelism and
reduced storage needs. The performance of the algorithm is evaluated using
simulations
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