501 research outputs found
A Variational Inference Framework for Soft-In-Soft-Out Detection in Multiple Access Channels
We propose a unified framework for deriving and studying soft-in-soft-out
(SISO) detection in interference channels using the concept of variational
inference. The proposed framework may be used in multiple-access interference
(MAI), inter-symbol interference (ISI), and multiple-input multiple-outpu
(MIMO) channels. Without loss of generality, we will focus our attention on
turbo multiuser detection, to facilitate a more concrete discussion. It is
shown that, with some loss of optimality, variational inference avoids the
exponential complexity of a posteriori probability (APP) detection by
optimizing a closely-related, but much more manageable, objective function
called variational free energy. In addition to its systematic appeal, there are
several other advantages to this viewpoint. First of all, it provides unified
and rigorous justifications for numerous detectors that were proposed on
radically different grounds, and facilitates convenient joint detection and
decoding (utilizing the turbo principle) when error-control codes are
incorporated. Secondly, efficient joint parameter estimation and data detection
is possible via the variational expectation maximization (EM) algorithm, such
that the detrimental effect of inaccurate channel knowledge at the receiver may
be dealt with systematically. We are also able to extend BPSK-based SISO
detection schemes to arbitrary square QAM constellations in a rigorous manner
using a variational argument.Comment: Submitted to Transactions on Information Theor
Decomposition by Successive Convex Approximation: A Unifying Approach for Linear Transceiver Design in Heterogeneous Networks
We study the downlink linear precoder design problem in a multi-cell dense
heterogeneous network (HetNet). The problem is formulated as a general
sum-utility maximization (SUM) problem, which includes as special cases many
practical precoder design problems such as multi-cell coordinated linear
precoding, full and partial per-cell coordinated multi-point transmission,
zero-forcing precoding and joint BS clustering and beamforming/precoding. The
SUM problem is difficult due to its non-convexity and the tight coupling of the
users' precoders. In this paper we propose a novel convex approximation
technique to approximate the original problem by a series of convex
subproblems, each of which decomposes across all the cells. The convexity of
the subproblems allows for efficient computation, while their decomposability
leads to distributed implementation. {Our approach hinges upon the
identification of certain key convexity properties of the sum-utility
objective, which allows us to transform the problem into a form that can be
solved using a popular algorithmic framework called BSUM (Block Successive
Upper-Bound Minimization).} Simulation experiments show that the proposed
framework is effective for solving interference management problems in large
HetNet.Comment: Accepted by IEEE Transactions on Wireless Communicatio
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