Puttarak, Nattakan
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Puttarak, Nattakan
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Puttarak, N.
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nattakan.pu@kmitl.ac.th
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Item type:Publication, EXIT chart analysis of nonbinary protograph LDPC codes for partial response channels(2014-11-01); ; Low-density parity-check (LDPC) codes over finite fields GF(q) provide an error correction in the noisy partial response (PR) channels. The extrinsic information transfer (EXIT) chart can be used to predict the threshold decoding of protograph LDPC codes, however, previous works only consider the binary protograph LDPC codes in the PR channels. In this paper, we propose to perform the EXIT chart analysis on the nonbinary protograph LDPC codes for the PR channels. Unlike prior works, the actual extrinsic information of the channel detector is measured, then the extrinsic information to the variable nodes is generated with the measured statistics. Moreover, since the mutual information of the variable nodes depends on GF(q), we use the Monte Carlo method to approximate the mutual information. The analysis on the regular (2,4) code, regular (3,6) code, RA code, and AR3A code on the PR channels reveal that, for the PR1 channel, the RA code outperforms the others for q = 2, 4, and 8, but the regular (2,4) code is the best for q = 16 and 32. For the PR2 channel, the RA code is the best code for q = 2 and 4, but the regular (2,4) code is the best code for q > 4. The simulation of the codes for q = 4 and 16 are then used to confirm the theoretical results. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reference Signal Received Power Prediction Using Convolutional Neural Network with Residual Loss(2023-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multi-Agent Deep Q-Learning for Antenna Tilt Optimization in Wireless Networks(2023-01-01); The configuration of an antenna installed at a base station involves the quality of communication in wireless networks. For example, at each transmitter, the antenna tilt must be optimized such that the desired and undesired receivers obtain the highest and lowest signal strength, respectively. In this work, we propose to use multi-agent deep Q-learning to optimize the antenna tilt. Our channel model includes the three-dimensional antenna gain, the Ericsson path loss model, and the digital elevation model (DEM). Our simulation indicates that multiagant deep Q-learning provides good signal quality. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, LDPC decoder using pattern-dependent modified llr for the bit patterned media storage with written-in errors(2012-10-29); ; The written-in errors in bit patterned media recording (BPMR) system cause the erroneous bits during the writing process leading to the performance degradation. In this work, we propose the pattern-dependent modified log-likelihood ratio (LLR) usage in low-density parity check (LDPC) decoder to reduce the written-in errors and also improve the write margin. Unlike the existing works, LLR computed based on the input data patterns is used at the LDPC decoder for the cascaded written-in error channel (WEC) with the additive white Gaussian noise (AWGN) channel. The proposed LDPC decoder outperforms the one with conventional LLR in terms of both the write margin, when the SNR is fixed at 5.5 dB, and the performance of LDPC decoder. The SNR gain is about 0.2 dB at the BER of 10 <sup>-6</sup>. © 2012 IEEE.