Puttarak, Nattakan
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Puttarak, Nattakan
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Puttarak, N.
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nattakan.pu@kmitl.ac.th
18 results
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Item type:Publication, Latency-reduced retransmission in multicast network using systematic network code(2016-09-07); Kaewprapha, PhisanThe reliability of multicast network where source delivers many frames of data to multiple receivers over a large scale network is degraded by delay of retransmissions after some data frame has been lost. This paper proposes a coding technique applying on network layer to combat this problem by using systematic network code with overheard retransmission technique. Latency of retransmission and throughput will be measured to represent the performance of this algorithm. The results show that this technique can improve reliability and performance of multicast network by decreasing a delay of retransmission for 40-70 % and yet increasing throughput for more than 8-19% compared with a conventional method. - 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Transfer Learning for Classifying Motor Imagery EEG: A Comparative Study(2021-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An implementation of maximum likelihood decoder for error-file recovery on cloud storage system(2018-07-02)1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Designed MMSE equalizers for nonlinear magnetic recording channels(2014-01-01); Writing process in hard disk drives (HDD) is affected from nonlinearity. Normally, nonlinearity is not easy to be avoided or removed since it is unexpected and caused by various sources. In this paper, we review a description of a nonlinearity behavior by using a Volterra model, and using a random binary number to generate an input data. The Volterra model can describe both linear and nonlinear parts of the read-back signals in terms of the volterra equations. In addition, we propose to use an MMSE method to equalize the read-back signals with nonlinearity using various constraints before applying the Viterbi detector. For a 2nd-order Volterra model, the results show that the equalizer with g<inf>1</inf> =1 constraint gives the lowest MMSE values. Furthermore, as the nonlinearity level increases, the bit error rate (BER) performance degrades. © 2014 IEEE.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A study on Ka-band site switching using advanced prediction for rain-induced attenuation(2015-01-28); High frequency satellite communications suffers from rain-induced attenuations, which may cause service discontinuity during high-intensity rain events. To combat this, we propose a novel site switching technique that anticipates signal attenuations using a linear dynamical system response model and performs site switching in advance. Using only the rainfall data measured at a ground station for attenuation prediction, we demonstrate that our method is able to lessen signal attenuation while maintaining link availability.1 - 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.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reduced-nonlinear effect on magnetic recording channels using MMSE equalizer(2015-01-01); This paper considers the nonlinear effect on magnetic recording channels in order to reduce the system distortion. We propose the designed minimum-mean square error (MMSE) equalizer and targets for a high density perpendicular magnetic recording to combat the nonlinear transition shift (NLTS) in a channel characterized by jitter noise and additive white Gaussian noise (AWGN). Due to the complex nature of nonlinear effects, the nonlinearity of a read-back signal is modeled by the second order Volterra model consisting of linear and nonlinear parts. The bit-error rate (BER) of the nonlinear-affected system using the designed MMSE equalizer is significantly decreased. The results show that the proposed MMSE equalizer with g<inf>t</inf>=1 constraint can decrease the inter-symbol interference (ISI) that causes nonlinearity and also improves a system performance.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A smart dialogue box for the hearing impaired(2019-07-01); 2 - Some of the metrics are blocked by yourconsent settings
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.1