20 research outputs found
Compressive Sampling for Remote Control Systems
In remote control, efficient compression or representation of control signals
is essential to send them through rate-limited channels. For this purpose, we
propose an approach of sparse control signal representation using the
compressive sampling technique. The problem of obtaining sparse representation
is formulated by cardinality-constrained L2 optimization of the control
performance, which is reducible to L1-L2 optimization. The low rate random
sampling employed in the proposed method based on the compressive sampling, in
addition to the fact that the L1-L2 optimization can be effectively solved by a
fast iteration method, enables us to generate the sparse control signal with
reduced computational complexity, which is preferable in remote control systems
where computation delays seriously degrade the performance. We give a
theoretical result for control performance analysis based on the notion of
restricted isometry property (RIP). An example is shown to illustrate the
effectiveness of the proposed approach via numerical experiments
Multiuser Detection by MAP Estimation with Sum-of-Absolute-Values Relaxation
In this article, we consider multiuser detection that copes with multiple
access interference caused in star-topology machine-to-machine (M2M)
communications. We assume that the transmitted signals are discrete-valued
(e.g. binary signals taking values of ), which is taken into account as
prior information in detection. We formulate the detection problem as the
maximum a posteriori (MAP) estimation, which is relaxed to a convex
optimization called the sum-of-absolute-values (SOAV) optimization. The SOAV
optimization can be efficiently solved by a proximal splitting algorithm, for
which we give the proximity operator in a closed form. Numerical simulations
are shown to illustrate the effectiveness of the proposed approach compared
with the linear minimum mean-square-error (LMMSE) and the least absolute
shrinkage and selection operator (LASSO) methods.Comment: submitted; 6 pages, 7 figure
A User's Guide to Compressed Sensing for Communications Systems
This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on l1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the l1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control