2,415 research outputs found
Maximum Entropy Vector Kernels for MIMO system identification
Recent contributions have framed linear system identification as a
nonparametric regularized inverse problem. Relying on -type
regularization which accounts for the stability and smoothness of the impulse
response to be estimated, these approaches have been shown to be competitive
w.r.t classical parametric methods. In this paper, adopting Maximum Entropy
arguments, we derive a new penalty deriving from a vector-valued
kernel; to do so we exploit the structure of the Hankel matrix, thus
controlling at the same time complexity, measured by the McMillan degree,
stability and smoothness of the identified models. As a special case we recover
the nuclear norm penalty on the squared block Hankel matrix. In contrast with
previous literature on reweighted nuclear norm penalties, our kernel is
described by a small number of hyper-parameters, which are iteratively updated
through marginal likelihood maximization; constraining the structure of the
kernel acts as a (hyper)regularizer which helps controlling the effective
degrees of freedom of our estimator. To optimize the marginal likelihood we
adapt a Scaled Gradient Projection (SGP) algorithm which is proved to be
significantly computationally cheaper than other first and second order
off-the-shelf optimization methods. The paper also contains an extensive
comparison with many state-of-the-art methods on several Monte-Carlo studies,
which confirms the effectiveness of our procedure
Robust Transceiver Design Based on Interference Alignment for Multi-User Multi-Cell MIMO Networks with Channel Uncertainty
In this paper, we firstly exploit the inter-user interference (IUI) and
inter-cell interference (ICI) as useful references to develop a robust
transceiver design based on interference alignment for a downlink multi-user
multi-cell multiple-input multiple-output (MIMO) interference network under
channel estimation error. At transmitters, we propose a two-tier transmit
beamforming strategy, we first achieve the inner beamforming direction and
allocated power by minimizing the interference leakage as well as maximizing
the system energy efficiency, respectively. Then, for the outer beamformer
design, we develop an efficient conjugate gradient Grassmann manifold subspace
tracking algorithm to minimize the distances between the subspace spanned by
interference and the interference subspace in the time varying channel. At
receivers, we propose a practical interference alignment based on fast and
robust fast data projection method (FDPM) subspace tracking algorithm, to
achieve the receive beamformer under channel uncertainty. Numerical results
show that our proposed robust transceiver design achieves better performance
compared with some existing methods in terms of the sum rate and the energy
efficiency.Comment: 12 pages, 8 figure
Distributed field estimation in wireless sensor networks
This work takes into account the problem of distributed estimation of a physical field of interest through a wireless sesnor networks
Distributed field estimation in wireless sensor networks
This work takes into account the problem of distributed estimation of a physical field of interest through a wireless sesnor networks
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