871 research outputs found
Receding Horizon Control Based Consensus Scheme in General Linear Multi-agent Systems
This paper investigates the consensus problem of general linear multi-agent
systems under the framework of optimization. A novel distributed receding
horizon control (RHC) strategy for consensus is proposed. We show that the
consensus protocol generated by the unconstrained distributed RHC can be
expressed in an explicit form. Based on the resulting consensus protocol the
necessary and sufficient conditions for ensuring consensus are developed.
Furthermore, we specify more detailed consensus conditions for multi-agent
system with general and one-dimensional linear dynamics depending on the
difference Riccati equations (DREs), respectively. Finally, two case studies
verify the proposed scheme and the corresponding theoretical results.Comment: 18 page
A unitary distributed subgradient method for multi-agent optimization with different coupling sources
In this work, we first consider distributed convex constrained optimization
problems where the objective function is encoded by multiple local and possibly
nonsmooth objectives privately held by a group of agents, and propose a
distributed subgradient method with double averaging (abbreviated as ) that only requires peer-to-peer communication and local computation to
solve the global problem. The algorithmic framework builds on dual methods and
dynamic average consensus; the sequence of test points is formed by iteratively
minimizing a local dual model of the overall objective where the coefficients,
i.e., approximated subgradients of the objective, are supplied by the dynamic
average consensus scheme. We theoretically show that enjoys
non-ergodic convergence properties, i.e., the local minimizing sequence itself
is convergent, a distinct feature that cannot be found in existing results.
Specifically, we establish a convergence rate of in
terms of objective function error. Then, extensions are made to tackle
distributed optimization problems with coupled functional constraints by
combining and dual decomposition. This is made possible by
Lagrangian relaxation that transforms the coupling in constraints of the primal
problem into that in cost functions of the dual, thus allowing us to solve the
dual problem via . Both the dual objective error and the quadratic
penalty for the coupled constraint are proved to converge at a rate of
, and the primal objective error asymptotically
vanishes. Numerical experiments and comparisons are conducted to illustrate the
advantage of the proposed algorithms and validate our theoretical findings.Comment: 15 pages, 2 figure
Fast Beam Alignment for Millimeter Wave Communications: A Sparse Encoding and Phaseless Decoding Approach
In this paper, we studied the problem of beam alignment for millimeter wave
(mmWave) communications, in which we assume a hybrid analog and digital
beamforming structure is employed at the transmitter (i.e. base station), and
an omni-directional antenna or an antenna array is used at the receiver (i.e.
user). By exploiting the sparse scattering nature of mmWave channels, the beam
alignment problem is formulated as a sparse encoding and phaseless decoding
problem. More specifically, the problem of interest involves finding a sparse
sensing matrix and an efficient recovery algorithm to recover the support and
magnitude of the sparse signal from compressive phaseless measurements. A
sparse bipartite graph coding (SBG-Coding) algorithm is developed for sparse
encoding and phaseless decoding. Our theoretical analysis shows that, in the
noiseless case, our proposed algorithm can perfectly recover the support and
magnitude of the sparse signal with probability exceeding a pre-specified value
from measurements, where is the number of nonzero
entries of the sparse signal. The proposed algorithm has a simple decoding
procedure which is computationally efficient and noise-robust. Simulation
results show that our proposed method renders a reliable beam alignment in the
low and moderate signal-to-noise ratio (SNR) regimes and presents a clear
performance advantage over existing methods
Computationally Efficient Sparse Bayesian Learning via Generalized Approximate Message Passing
The sparse Beyesian learning (also referred to as Bayesian compressed
sensing) algorithm is one of the most popular approaches for sparse signal
recovery, and has demonstrated superior performance in a series of experiments.
Nevertheless, the sparse Bayesian learning algorithm has computational
complexity that grows exponentially with the dimension of the signal, which
hinders its application to many practical problems even with moderately large
data sets. To address this issue, in this paper, we propose a computationally
efficient sparse Bayesian learning method via the generalized approximate
message passing (GAMP) technique. Specifically, the algorithm is developed
within an expectation-maximization (EM) framework, using GAMP to efficiently
compute an approximation of the posterior distribution of hidden variables. The
hyperparameters associated with the hierarchical Gaussian prior are learned by
iteratively maximizing the Q-function which is calculated based on the
posterior approximation obtained from the GAMP. Numerical results are provided
to illustrate the computational efficacy and the effectiveness of the proposed
algorithm
Receding Horizon Consensus of General Linear Multi-agent Systems with Input Constraints: An Inverse Optimality Approach
It is desirable but challenging to fulfill system constraints and reach
optimal performance in consensus protocol design for practical multi-agent
systems (MASs). This paper investigates the optimal consensus problem for
general linear MASs subject to control input constraints. Two classes of MASs
including subsystems with semi-stable and unstable dynamics are considered. For
both classes of MASs without input constraints, the results on designing
optimal consensus protocols are first developed by inverse optimality approach.
Utilizing the optimal consensus protocols, the receding horizon control
(RHC)-based consensus strategies are designed for these two classes of MASs
with input constraints. The conditions for assigning the cost functions
distributively are derived, based on which the distributed RHC-based consensus
frameworks are formulated. Next, the feasibility and consensus properties of
the closed-loop systems are analyzed. It is shown that 1) the optimal
performance indices under the inverse optimal consensus protocols are coupled
with the network topologies and the system matrices of subsystems, but they are
different for MASs with semi-stable and unstable subsystems; 2) the unstable
modes of subsystems impose more stringent requirements for the parameter
design; 3) the designed RHC-based consensus strategies can make the control
input constraints fulfilled and ensure consensus for the closed-loop systems in
both cases. But for MASs with semi-stable subsystems, the {\em convergent
consensus} can be reached. Finally, two examples are provided to verify the
effectiveness of the proposed results
Compressed Channel Estimation and Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
In this paper, we consider channel estimation for intelligent reflecting
surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is
deployed to assist the data transmission from the base station (BS) to a user.
It is shown that for the purpose of joint active and passive beamforming, the
knowledge of a large-size cascade channel matrix needs to be acquired. To
reduce the training overhead, the inherent sparsity in mmWave channels is
exploited. By utilizing properties of Katri-Rao and Kronecker products, we find
a sparse representation of the cascade channel and convert cascade channel
estimation into a sparse signal recovery problem. Simulation results show that
our proposed method can provide an accurate channel estimate and achieve a
substantial training overhead reduction.Comment: Accepted by IEEE Signal Processing Letter
Phased Array-Based Sub-Nyquist Sampling for Joint Wideband Spectrum Sensing and Direction-of-Arrival Estimation
In this paper, we study the problem of joint wideband spectrum sensing and
direction-of-arrival (DoA) estimation in a sub-Nyquist sampling framework.
Specifically, considering a scenario where a few uncorrelated narrowband
signals spread over a wide (say, several GHz) frequency band, our objective is
to estimate the carrier frequencies and the DoAs associated with the narrowband
sources, as well as reconstruct the power spectra of these narrowband signals.
To overcome the sampling rate bottleneck for wideband spectrum sensing, we
propose a new phased-array based sub-Nyquist sampling architecture with
variable time delays, where a uniform linear array (ULA) is employed and the
received signal at each antenna is delayed by a variable amount of time and
then sampled by a synchronized low-rate analog-digital converter (ADC). Based
on the collected sub-Nyquist samples, we calculate a set of cross-correlation
matrices with different time lags, and develop a CANDECOMP/PARAFAC (CP)
decomposition-based method for joint DoA, carrier frequency and power spectrum
recovery. Perfect recovery conditions for the associated parameters and the
power spectrum are analyzed. Our analysis reveals that our proposed method does
not require to place any sparse constraint on the wideband spectrum, only needs
the sampling rate to be greater than the bandwidth of the narrowband source
signal with the largest bandwidth among all sources. Simulation results show
that our proposed method can achieve an estimation accuracy close to the
associated Cram\'{e}r-Rao bounds (CRBs) using only a small number of data
samples
Super-Resolution Compressed Sensing: A Generalized Iterative Reweighted L2 Approach
Conventional compressed sensing theory assumes signals have sparse
representations in a known, finite dictionary. Nevertheless, in many practical
applications such as direction-of-arrival (DOA) estimation and line spectral
estimation, the sparsifying dictionary is usually characterized by a set of
unknown parameters in a continuous domain. To apply the conventional compressed
sensing technique to such applications, the continuous parameter space has to
be discretized to a finite set of grid points, based on which a "presumed
dictionary" is constructed for sparse signal recovery. Discretization, however,
inevitably incurs errors since the true parameters do not necessarily lie on
the discretized grid. This error, also referred to as grid mismatch, may lead
to deteriorated recovery performance or even recovery failure. To address this
issue, in this paper, we propose a generalized iterative reweighted L2 method
which jointly estimates the sparse signals and the unknown parameters
associated with the true dictionary. The proposed algorithm is developed by
iteratively decreasing a surrogate function majorizing a given objective
function, resulting in a gradual and interweaved iterative process to refine
the unknown parameters and the sparse signal. A simple yet effective scheme is
developed for adaptively updating the regularization parameter that controls
the tradeoff between the sparsity of the solution and the data fitting error.
Extension of the proposed algorithm to the multiple measurement vector scenario
is also considered. Numerical results show that the proposed algorithm achieves
a super-resolution accuracy and presents superiority over other existing
methods.Comment: arXiv admin note: text overlap with arXiv:1401.431
Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach
We consider the problem of spectrum sharing in a cognitive radio system
consisting of a primary user and a secondary user. The primary user and the
secondary user work in a non-cooperative manner. Specifically, the primary user
is assumed to update its transmit power based on a pre-defined power control
policy. The secondary user does not have any knowledge about the primary user's
transmit power, or its power control strategy. The objective of this paper is
to develop a learning-based power control method for the secondary user in
order to share the common spectrum with the primary user. To assist the
secondary user, a set of sensor nodes are spatially deployed to collect the
received signal strength information at different locations in the wireless
environment. We develop a deep reinforcement learning-based method, which the
secondary user can use to intelligently adjust its transmit power such that
after a few rounds of interaction with the primary user, both users can
transmit their own data successfully with required qualities of service. Our
experimental results show that the secondary user can interact with the primary
user efficiently to reach a goal state (defined as a state in which both users
can successfully transmit their data) from any initial states within a few
number of steps
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models
The problem of low rank matrix completion is considered in this paper. To
exploit the underlying low-rank structure of the data matrix, we propose a
hierarchical Gaussian prior model, where columns of the low-rank matrix are
assumed to follow a Gaussian distribution with zero mean and a common precision
matrix, and a Wishart distribution is specified as a hyperprior over the
precision matrix. We show that such a hierarchical Gaussian prior has the
potential to encourage a low-rank solution. Based on the proposed hierarchical
prior model, a variational Bayesian method is developed for matrix completion,
where the generalized approximate massage passing (GAMP) technique is embedded
into the variational Bayesian inference in order to circumvent cumbersome
matrix inverse operations. Simulation results show that our proposed method
demonstrates superiority over existing state-of-the-art matrix completion
methods
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