3,343 research outputs found
MIMO Detection for High-Order QAM Based on a Gaussian Tree Approximation
This paper proposes a new detection algorithm for MIMO communication systems
employing high order QAM constellations. The factor graph that corresponds to
this problem is very loopy; in fact, it is a complete graph. Hence, a
straightforward application of the Belief Propagation (BP) algorithm yields
very poor results. Our algorithm is based on an optimal tree approximation of
the Gaussian density of the unconstrained linear system. The finite-set
constraint is then applied to obtain a loop-free discrete distribution. It is
shown that even though the approximation is not directly applied to the exact
discrete distribution, applying the BP algorithm to the loop-free factor graph
outperforms current methods in terms of both performance and complexity. The
improved performance of the proposed algorithm is demonstrated on the problem
of MIMO detection
Turbo EP-based Equalization: a Filter-Type Implementation
This manuscript has been submitted to Transactions on Communications on
September 7, 2017; revised on January 10, 2018 and March 27, 2018; and accepted
on April 25, 2018
We propose a novel filter-type equalizer to improve the solution of the
linear minimum-mean squared-error (LMMSE) turbo equalizer, with computational
complexity constrained to be quadratic in the filter length. When high-order
modulations and/or large memory channels are used the optimal BCJR equalizer is
unavailable, due to its computational complexity. In this scenario, the
filter-type LMMSE turbo equalization exhibits a good performance compared to
other approximations. In this paper, we show that this solution can be
significantly improved by using expectation propagation (EP) in the estimation
of the a posteriori probabilities. First, it yields a more accurate estimation
of the extrinsic distribution to be sent to the channel decoder. Second,
compared to other solutions based on EP the computational complexity of the
proposed solution is constrained to be quadratic in the length of the finite
impulse response (FIR). In addition, we review previous EP-based turbo
equalization implementations. Instead of considering default uniform priors we
exploit the outputs of the decoder. Some simulation results are included to
show that this new EP-based filter remarkably outperforms the turbo approach of
previous versions of the EP algorithm and also improves the LMMSE solution,
with and without turbo equalization
Hybrid approximate message passing
Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable recent attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper presents a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The key concept is a partition of dependencies of a general graphical model into strong and weak edges, with the weak edges representing interactions through aggregates of small, linearizable couplings of variables. AMP approximations based on the Central Limit Theorem can be readily applied to aggregates of many weak edges and integrated with standard message passing updates on the strong edges. The resulting algorithm, which we call hybrid generalized approximate message passing (HyGAMP), can yield significantly simpler implementations of sum-product and max-sum loopy belief propagation. By varying the partition of strong and weak edges, a performance--complexity trade-off can be achieved. Group sparsity and multinomial logistic regression problems are studied as examples of the proposed methodology.The work of S. Rangan was supported in part by the National Science Foundation under Grants 1116589, 1302336, and 1547332, and in part by the industrial affiliates of NYU WIRELESS. The work of A. K. Fletcher was supported in part by the National Science Foundation under Grants 1254204 and 1738286 and in part by the Office of Naval Research under Grant N00014-15-1-2677. The work of V. K. Goyal was supported in part by the National Science Foundation under Grant 1422034. The work of E. Byrne and P. Schniter was supported in part by the National Science Foundation under Grant CCF-1527162. (1116589 - National Science Foundation; 1302336 - National Science Foundation; 1547332 - National Science Foundation; 1254204 - National Science Foundation; 1738286 - National Science Foundation; 1422034 - National Science Foundation; CCF-1527162 - National Science Foundation; NYU WIRELESS; N00014-15-1-2677 - Office of Naval Research
Expectation Propagation Detection for High-Order High-Dimensional MIMO Systems
Modern communications systems use multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the number of antennas and the order of the constellation grow, the design of efficient and low-complexity MIMO receivers possesses big technical challenges. For example, symbol detection can no longer rely on maximum likelihood detection or sphere-decoding methods, as their complexity increases exponentially with the number of transmitters/receivers. In this paper, we propose a low-complexity high-accuracy MIMO symbol detector based on the Expectation Propagation (EP) algorithm. EP allows approximating iteratively at polynomial-time the posterior distribution of the transmitted symbols. We also show that our EP MIMO detector outperforms classic and state-of-The-Art solutions reducing the symbol error rate at a reduced computational complexity.This work has been partly funded by the Spanish Ministry of Science and Innovation with the projects GRE3NSYST (TEC2011- 29006-C03-03) and ALCIT (TEC2012-38800-C03-01) and by the program CONSOLIDER-INGENIO 2010 under the project COMONSENS (CSD 2008- 00010).Publicad
Composite CDMA - A statistical mechanics analysis
Code Division Multiple Access (CDMA) in which the spreading code assignment
to users contains a random element has recently become a cornerstone of CDMA
research. The random element in the construction is particular attractive as it
provides robustness and flexibility in utilising multi-access channels, whilst
not making significant sacrifices in terms of transmission power. Random codes
are generated from some ensemble, here we consider the possibility of combining
two standard paradigms, sparsely and densely spread codes, in a single
composite code ensemble. The composite code analysis includes a replica
symmetric calculation of performance in the large system limit, and
investigation of finite systems through a composite belief propagation
algorithm. A variety of codes are examined with a focus on the high
multi-access interference regime. In both the large size limit and finite
systems we demonstrate scenarios in which the composite code has typical
performance exceeding sparse and dense codes at equivalent signal to noise
ratio.Comment: 23 pages, 11 figures, Sigma Phi 2008 conference submission -
submitted to J.Stat.Mec
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