4,483 research outputs found
Iterative Multiuser Detection and Decoding with Spatially Coupled Interleaving
Spatially coupled (SC) interleaving is proposed to improve the performance of
iterative multiuser detection and decoding (MUDD) for quasi-static fading
multiple-input multiple-output systems. The linear minimum mean-squared error
(LMMSE) demodulator is used to reduce the complexity and to avoid error
propagation. Furthermore, sliding window MUDD is proposed to circumvent an
increase of the decoding latency due to SC interleaving. Theoretical and
numerical analyses show that SC interleaving can improve the performance of the
iterative LMMSE MUDD for regular low-density parity-check codes.Comment: Long version of a paper submitted to IEEE Wireless Commun. Let
Information-Coupled Turbo Codes for LTE Systems
We propose a new class of information-coupled (IC) Turbo codes to improve the
transport block (TB) error rate performance for long-term evolution (LTE)
systems, while keeping the hybrid automatic repeat request protocol and the
Turbo decoder for each code block (CB) unchanged. In the proposed codes, every
two consecutive CBs in a TB are coupled together by sharing a few common
information bits. We propose a feed-forward and feed-back decoding scheme and a
windowed (WD) decoding scheme for decoding the whole TB by exploiting the
coupled information between CBs. Both decoding schemes achieve a considerable
signal-to-noise-ratio (SNR) gain compared to the LTE Turbo codes. We construct
the extrinsic information transfer (EXIT) functions for the LTE Turbo codes and
our proposed IC Turbo codes from the EXIT functions of underlying convolutional
codes. An SNR gain upper bound of our proposed codes over the LTE Turbo codes
is derived and calculated by the constructed EXIT charts. Numerical results
show that the proposed codes achieve an SNR gain of 0.25 dB to 0.72 dB for
various code parameters at a TB error rate level of , which complies
with the derived SNR gain upper bound.Comment: 13 pages, 12 figure
Replica Analysis and Approximate Message Passing Decoder for Superposition Codes
Superposition codes are efficient for the Additive White Gaussian Noise
channel. We provide here a replica analysis of the performances of these codes
for large signals. We also consider a Bayesian Approximate Message Passing
decoder based on a belief-propagation approach, and discuss its performance
using the density evolution technic. Our main findings are 1) for the sizes we
can access, the message-passing decoder outperforms other decoders studied in
the literature 2) its performance is limited by a sharp phase transition and 3)
while these codes reach capacity as (a crucial parameter in the code)
increases, the performance of the message passing decoder worsen as the phase
transition goes to lower rates.Comment: 5 pages, 5 figures, To be presented at the 2014 IEEE International
Symposium on Information Theor
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