879 research outputs found
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems
We propose a novel decomposition framework for the distributed optimization
of general nonconvex sum-utility functions arising naturally in the system
design of wireless multiuser interfering systems. Our main contributions are:
i) the development of the first class of (inexact) Jacobi best-response
algorithms with provable convergence, where all the users simultaneously and
iteratively solve a suitably convexified version of the original sum-utility
optimization problem; ii) the derivation of a general dynamic pricing mechanism
that provides a unified view of existing pricing schemes that are based,
instead, on heuristics; and iii) a framework that can be easily particularized
to well-known applications, giving rise to very efficient practical (Jacobi or
Gauss-Seidel) algorithms that outperform existing adhoc methods proposed for
very specific problems. Interestingly, our framework contains as special cases
well-known gradient algorithms for nonconvex sum-utility problems, and many
blockcoordinate descent schemes for convex functions.Comment: submitted to IEEE Transactions on Signal Processin
Convergence-Optimal Quantizer Design of Distributed Contraction-based Iterative Algorithms with Quantized Message Passing
In this paper, we study the convergence behavior of distributed iterative
algorithms with quantized message passing. We first introduce general iterative
function evaluation algorithms for solving fixed point problems distributively.
We then analyze the convergence of the distributed algorithms, e.g. Jacobi
scheme and Gauss-Seidel scheme, under the quantized message passing. Based on
the closed-form convergence performance derived, we propose two quantizer
designs, namely the time invariant convergence-optimal quantizer (TICOQ) and
the time varying convergence-optimal quantizer (TVCOQ), to minimize the effect
of the quantization error on the convergence. We also study the tradeoff
between the convergence error and message passing overhead for both TICOQ and
TVCOQ. As an example, we apply the TICOQ and TVCOQ designs to the iterative
waterfilling algorithm of MIMO interference game.Comment: 17 pages, 9 figures, Transaction on Signal Processing, accepte
Capacity Scaling in MIMO Systems with General Unitarily Invariant Random Matrices
We investigate the capacity scaling of MIMO systems with the system
dimensions. To that end, we quantify how the mutual information varies when the
number of antennas (at either the receiver or transmitter side) is altered. For
a system comprising receive and transmit antennas with , we find
the following: By removing as many receive antennas as needed to obtain a
square system (provided the channel matrices before and after the removal have
full rank) the maximum resulting loss of mutual information over all
signal-to-noise ratios (SNRs) depends only on , and the matrix of
left-singular vectors of the initial channel matrix, but not on its singular
values. In particular, if the latter matrix is Haar distributed the ergodic
rate loss is given by nats. Under
the same assumption, if with the ratio
fixed, the rate loss normalized by converges almost surely to
bits with denoting the binary entropy function. We also quantify and
study how the mutual information as a function of the system dimensions
deviates from the traditionally assumed linear growth in the minimum of the
system dimensions at high SNR.Comment: Accepted for publication in the IEEE Transactions on Information
Theor
Gaussian Message Passing for Overloaded Massive MIMO-NOMA
This paper considers a low-complexity Gaussian Message Passing (GMP) scheme
for a coded massive Multiple-Input Multiple-Output (MIMO) systems with
Non-Orthogonal Multiple Access (massive MIMO-NOMA), in which a base station
with antennas serves sources simultaneously in the same frequency.
Both and are large numbers, and we consider the overloaded cases
with . The GMP for MIMO-NOMA is a message passing algorithm operating
on a fully-connected loopy factor graph, which is well understood to fail to
converge due to the correlation problem. In this paper, we utilize the
large-scale property of the system to simplify the convergence analysis of the
GMP under the overloaded condition. First, we prove that the \emph{variances}
of the GMP definitely converge to the mean square error (MSE) of Linear Minimum
Mean Square Error (LMMSE) multi-user detection. Secondly, the \emph{means} of
the traditional GMP will fail to converge when . Therefore, we propose and derive a new
convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the
LMMSE multi-user detection performance for any , and show that it
has a faster convergence speed than the traditional GMP with the same
complexity. Finally, numerical results are provided to verify the validity and
accuracy of the theoretical results presented.Comment: Accepted by IEEE TWC, 16 pages, 11 figure
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