9,182 research outputs found
Mutual Information-Maximizing Quantized Belief Propagation Decoding of Regular LDPC Codes
In mutual information-maximizing lookup table (MIM-LUT) decoding of
low-density parity-check (LDPC) codes, table lookup operations are used to
replace arithmetic operations. In practice, large tables need to be decomposed
into small tables to save the memory consumption, at the cost of degraded error
performance. In this paper, we propose a method, called mutual
information-maximizing quantized belief propagation (MIM-QBP) decoding, to
remove the lookup tables used for MIM-LUT decoding. Our method leads to a very
efficient decoder, namely the MIM-QBP decoder, which can be implemented based
only on simple mappings and fixed-point additions. Simulation results show that
the MIM-QBP decoder can always considerably outperform the state-of-the-art
MIM-LUT decoder, mainly because it can avoid the performance loss due to table
decomposition. Furthermore, the MIM-QBP decoder with only 3 bits per message
can outperform the floating-point belief propagation (BP) decoder at high
signal-to-noise ratio (SNR) regions when testing on high-rate codes with a
maximum of 10-30 iterations
Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System
Due to the inherent aleatory uncertainties in renewable generators, the
reliability/adequacy assessments of distributed generation (DG) systems have
been particularly focused on the probabilistic modeling of random behaviors,
given sufficient informative data. However, another type of uncertainty
(epistemic uncertainty) must be accounted for in the modeling, due to
incomplete knowledge of the phenomena and imprecise evaluation of the related
characteristic parameters. In circumstances of few informative data, this type
of uncertainty calls for alternative methods of representation, propagation,
analysis and interpretation. In this study, we make a first attempt to
identify, model, and jointly propagate aleatory and epistemic uncertainties in
the context of DG systems modeling for adequacy assessment. Probability and
possibility distributions are used to model the aleatory and epistemic
uncertainties, respectively. Evidence theory is used to incorporate the two
uncertainties under a single framework. Based on the plausibility and belief
functions of evidence theory, the hybrid propagation approach is introduced. A
demonstration is given on a DG system adapted from the IEEE 34 nodes
distribution test feeder. Compared to the pure probabilistic approach, it is
shown that the hybrid propagation is capable of explicitly expressing the
imprecision in the knowledge on the DG parameters into the final adequacy
values assessed. It also effectively captures the growth of uncertainties with
higher DG penetration levels
Designing Multi-User MIMO for Energy Efficiency: When is Massive MIMO the Answer?
Assume that a multi-user multiple-input multiple-output (MIMO) communication
system must be designed to cover a given area with maximal energy efficiency
(bit/Joule). What are the optimal values for the number of antennas, active
users, and transmit power? By using a new model that describes how these three
parameters affect the total energy efficiency of the system, this work provides
closed-form expressions for their optimal values and interactions. In sharp
contrast to common belief, the transmit power is found to increase (not
decrease) with the number of antennas. This implies that energy efficient
systems can operate at high signal-to-noise ratio (SNR) regimes in which the
use of interference-suppressing precoding schemes is essential. Numerical
results show that the maximal energy efficiency is achieved by a massive MIMO
setup wherein hundreds of antennas are deployed to serve relatively many users
using interference-suppressing regularized zero-forcing precoding.Comment: Published at IEEE Wireless Communications and Networking Conference
(WCNC 2014), 6 pages, 5 figures, 1 table. This version improves the visual
presentation of Fig. 2 and corrects a typo in Lemma
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