• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1144-1158
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• 2016

In this paper, delay-constrained data transmission is considered over error-prone networks. Network coding is deployed for efficient information exchange, and an approximate decoding approach is deployed to overcome potential all-or-nothing problems. Our focus is on determining the cluster size and its impact on approximate decoding performance. Decoding performance is quantified, and we show that performance is determined only by the number of packets. Moreover, the fundamental tradeoff between approximate decoding performance and data transfer rate improvement is analyzed; as the cluster size increases, the data transfer rate improves and decoding performance is degraded. This tradeoff can lead to an optimal cluster size of network coding-based networks that achieves the target decoding performance of applications. A set of experiment results confirms the analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.117-121
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• 2005

In this work, we first present our study on the decoding schemes for space-time block code as well as our comments on their complexity. Then, we propose a new modified complex sphere decoding scheme, which has lower computational load than that of conventional complex sphere decoders. In the proposed scheme, the boundary for searching is defined by the intersection of an approximate polygon of searching circle and disk of lattice constellation. Therefore, the proposed scheme can reduce the searching boundary and it can avoid missing searching points as well. The performance of the proposed scheme, which is verified by computer simulations, consolidates our scheme.

• Journal of information and communication convergence engineering
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• v.3 no.2
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• pp.72-75
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• 2005

In this work, we first present our study on the decoding schemes for space-time block code as well as our comments on their complexity. Then, we propose a new modified complex sphere decoding scheme, which has lower computational load than that of conventional complex sphere decoders. In the proposed scheme, the boundary for searching is aided with the intersection of approximate polygon of searching circle and disk of constellation. Therefore, the proposed scheme can reduce the searching boundary while can avoid missing searching points. The performance of the proposed scheme is verified by computer simulation that consolidates our scheme.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.46-52
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• 2016

Quasi-Orthogonal STBC (QO-STBC) scheme is proposed conventionally achieving approximate full rate and full diversity in more than 3 transmit antenna and open-loop environmen.. But, conventional QO-STBC has disadvantage that performance degradation by interference terms of detection matrix and high decoding complexity. Recently, this interference cancellation scheme of low decoding complexity by multiplying specific rotation matrix is proposed. We propose more general interference cancellation scheme by using EVD(Eigenvalue Decompostion).

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.39-47
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• 2021

Lempel-Ziv coding is one of the most famous source coding schemes. The output of this source coding is usually a non-uniform code, which requires additional source coding, such as arithmetic coding, to reduce a redundancy. However, this additional source code increases complexity and decoding latency. Thus, this paper proposes the optimal maximum a-posteriori (MAP) receiver for non-uniform source non-orthogonal multiple access (NOMA) with symmetric superposition coding (SSC). First, we derive an analytical expression of the bit-error rate (BER) for non-uniform source NOMA with SSC. Then, Monte Carlo simulations demonstrate that the BER of the optimal MAP receiver for the non-uniform source improves slightly, compared to that of the conventional receiver for the uniform source. Moreover, we also show that the BER of an approximate analytical expression is in a good agreement with the BER of Monte Carlo simulation. As a result, the proposed optimal MAP receiver for non-uniform source could be a promising scheme for NOMA with SSC, to reduce complexity and decoding latency due to additional source coding.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.303-309
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• 2006

By combining the space-time diversity technique and iterative turbo codes, space-time turbo codes(STTCS) are able to provide powerful error correction capability. However, the multi-path transmission and iterative decoding structure of STTCS make the decoder very complex. In this paper, we propose a low complexity decoder, which can be used to decode STTCS as well as general iterative codes such as turbo codes. The efficient implementation of the backward recursion and the log-likelihood ratio(LLR) update in the proposed algorithm improves the computational efficiency. In addition, if we approximate the calculation of the joint LLR by using the approximate ratio(AR) algorithm, the computational complexity can be reduced even further. A complexity analysis and computer simulations over the Rayleigh fading channel show that the proposed algorithm necessitates less than 40% of the additions required by the conventional Max-Log-MAP algorithm, while providing the same overall performance.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.5-9
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• 2008

Researchers have investigated many upper bound techniques applicable to error probabilities on the maximum likelihood (ML) decoding performance of turbo-like codes and low density parity check (LDPC) codes in recent years for a long codeword block size. This is because it is trivial for a short codeword block size. Previous research efforts, such as the simple bound technique [20] recently proposed, developed upper bounds for LDPC codes and turbo-like codes using ensemble codes or the uniformly interleaved assumption. This assumption bounds the performance averaged over all ensemble codes or all interleavers. Another previous research effort [21] obtained the upper bound of turbo-like code with a particular interleaver using a truncated union bound which requires information of the minimum Hamming distance and the number of codewords with the minimum Hamming distance. However, it gives the reliable bound only in the region of the error floor where the minimum Hamming distance is dominant, i.e., in the region of high signal-to-noise ratios. Therefore, currently an upper bound on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix cannot be calculated because of heavy complexity so that only average bounds for ensemble codes can be obtained using a uniform interleaver assumption. In this paper, we propose a new bound technique on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix using ML estimated weight distributions and we also show that the practical iterative decoding performance is approximately suboptimal in ML sense because the simulation performance of iterative decoding is worse than the proposed upper bound and no wonder, even worse than ML decoding performance. In order to show this point, we compare the simulation results with the proposed upper bound and previous bounds. The proposed bound technique is based on the simple bound with an approximate weight distribution including several exact smallest distance terms, not with the ensemble distribution or the uniform interleaver assumption. This technique also shows a tighter upper bound than any other previous bound techniques for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix.

• Electronics and Telecommunications Trends
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• v.33 no.5
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• pp.13-23
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• 2018

Deep learning is a promising solution to a number of complex problems based on its inherent capability to approximate almost all types of functions without the demand for handcrafted feature extraction. New wireless transmission and access schemes based on deep learning are being increasingly proposed as substitutes for existing approaches, providing a lower complexity and better performance gain. Among such schemes, a communications system is viewed as an end-to-end autoencoder. The learning process applied in autoencoders can automatically deal with some nonlinear or unknown properties in communications systems. Deep learning can also be used to optimize each processing block for required tasks such as channel decoding, signal detection, and multiple access. On top of recent related research trends, we suggest appropriate research approaches for communications systems to adopt deep learning.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6A
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• pp.577-586
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• 2011

Modem digital communication systems are required to use forward error correction (FEC) codes to combat inevitable channel impairment. Turbo codes or low density parity check (LDPC) codes, using iterative decoding with soft decision detection (SDD) information, are the most common examples. The excellent performance of these codes should be conditioned on accurate estimation of soft decision detection information. In order to use FEC codes with iterative decoding for Multi-Input Multi-Output (MIMO) system, reliable soft decision channel gain should be provided. In this paper, we investigate efficient SDD methods for turbo-coded MIMO system, and derive the corresponding formulas of SDD for various MIMO detection schemes. We present simulation results of the derived SDD schemes for turbo-coded MIMO systems, and show that the presented results almost approximate to maximum likelihood detection performance with much less computational load.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3566-3575
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• 2000

In this paper, we propose an simple and efficient method to estunate the camera operation by using compressed information, which is extracted diracily from MPEG2 stream without complete decoding. In the method, the motion vector is converted into approximate optical flow by using the feature of predicted frame, because the motion vector in MPEG2 video stream is not regular sequene. And they are used to estimate the camera operation, which consist of pan, and zoom by Hough transform technique. The method provided better results than the least square method for video stream of basketball and socer games. The proposed method can have a reduced computational complexity because the information is directiv abtained in compressed domain. Additionally it can be a useful technology in content-based searching and analysis of video information. Also, the estimatd cameral operationis applicable in searching or tracking objects in MPEG2 video stream without decoding.