• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.7
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• pp.48-55
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• 2012

Because of increasing complexity in maximum-likelihood (ML) decoding of four of higher antenna scenario, Partial Interference Cancellation (PIC) group decoding could be the perfect solution to reduce the decoding complexity occurs in ML decoding. In this paper, we separate the symbols the users in the layered basis and find the equivalent channel matrix. Based on the equivalent channel matrix we provide the grouping scheme. In our paper, we construct a block wise transmission technique which will achieve the desired code rate and reduce the complexity and provide less transmission time. Finally we show the different grouping performance.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.285-288
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• 1998

Viterbi docoder is a maximum likelihood decoding method for convolution coding used in satellite and mobile communications. In this paper, a Viterbi decoder with constraint length of K=7, 3-soft decision and traceback depth of $\Gamma$=96 for convolution code is implemented using VHDL. The hardware size of designed decoder is reduced by 4 bit pre-traceback in the survivor memory.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.41-44
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• 2002

Alamouti proposes a two branch transmit diver-sity scheme that provides the same diversity order as maximal ratio combining at the receiver. It has many advantages of no bandwidth expansion, not requiring channel information at the transmitter and simple maximum likelihood decoding at the receiver. Papadias and Foschini extend this sch-eme to four transmit antennas and suggest several schemes to decrease the interference component and allow the attainment of the open-loop capacity. This paper shows the performance of ZF and MM-SE schemes comparing with ideal case on 4xl sy-stem over BER and 10% outage capacity.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.6 s.360
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• pp.40-47
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• 2007

In this paper, we propose and analyze the Adaptive Modulation System with optimal Turbo Coded V-BLAST(Vertical-Bell-lab Layered Space-Time) technique that adopts the extrinsic information from MAP (Maximum A Posteriori) Decoder with Iterative Decoding as a priori probability in two decoding procedures of V-BLAST; the ordering and the slicing. Also, we consider and compare the Adaptive Modulation System using conventional Turbo Coded V-BLAST technique that is simply combined V-BLAST with Turbo Coding scheme and the Adaptive Modulation System using conventional Turbo Coded V-BLAST technique that is decoded by the ML (Maximum Likelihood) decoding algorithm. We observe a throughput performance and a complexity. As a result of a performance comparison of each system, it has been proved that the complexity of the proposed decoding algorithm is lower than that of the ML decoding algorithm but is higher than that of the conventional V-BLAST decoding algorithm. however, we can see that the proposed system achieves a better throughput performance than the conventional system in the whole SNR (Signal to Noise Ratio) range. And the result shows that the proposed system achieves a throughput performance close to the ML decoded system. Specifically, a simulation shows that the maximum throughput improvement in each MIMO scheme is respectively about 350 kbps, 460 kbps, and 740 kbps compared to the conventional system. It is suggested that the effect of the proposed decoding algorithm accordingly gets higher as the number of system antenna increases.

• Journal of electromagnetic engineering and science
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• v.3 no.1
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• pp.50-56
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• 2003

This paper proposes a coding scheme of control channel for mobile packet communication to maximize the minimum Hamming distance, which is based on shifting of basis vectors of Reed Muller code with optimized dynamic puncturing and/or(partial) sequence repetition. Since the maximum likelihood decoding can be performed using the extremely simple FHT(Fast Hadamard Transformation), it is suitable for real time optimum decoding of control channel information with very little complexity. We show applications of the proposed coding method to TFCI(Transport Format Combination Indicator) code in split and normal modes of 3GPP W-CDMA system. We also discuss how this method can reduce rate indication error over AWGN(Additive White Gaussian Noise) as well as fading channels when the proposed coding scheme is applied to 1xEV-DV system of $3^{rd}$TEX> generation partnership project 2(3GPP2) to indicate the data rate transmitted on the reverse traffic channel by a Mobile Station(MS).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1131-1143
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• 2016

Differential detection schemes do not require any channel estimation, which can be employed under user mobility with low computational complexity. In this work, a soft-input soft-output (SISO) differential detection algorithm is proposed for amplify-and-forward (AF) over time-varying relaying channels based cooperative communications system. Furthermore, maximum-likelihood (ML) detector for M-ary differential Phase-shift keying (DPSK) is derived to calculate a posteriori probabilities (APP) of information bits. In addition, when the SISO is exploited in conjunction with channel decoding, iterative detection and decoding approach by exchanging extrinsic information with outer code is obtained. Finally, simulation results show that the proposed non-coherent approach improves detection performance significantly. In particular, the system can obtain greater performance gain under fast-fading channels.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.4
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• pp.27-36
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• 1996

It is well known that the viterbi algorithm proposed as a mthod of decoding convolutional codes is in fact maximum likelihood (ML) and therefore optimal. But, because hardware complexity grows exponentially with the constraint length, there will be severe constraints on the implementation of the viterbi decoders. In this paper, the three-layered backpropagation neural networks are proposed as an alternative in order to get sufficiently useful performance and deal successfully with the problems of the viterbi decoder. This paper shows that the neural convolutional decoder (NCD) can make a decision in the point of ML in decoding and describes simulation results. The cause of the difference between stochastic results and simulation results is discussed, and then thefuture prospect of the NCD is described on the basis of the characteristic of the transfer function.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12C
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• pp.1062-1067
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• 2008

ABBA codes, a class of quasi-orthognal space-time block codes (QoSTBC) proposed by Tirkkonen and others, allow full rate and a fast maximum likelihood (ML) decoding, but do not have full diversity. In this paper, a linear complex precoder is proposed for ABBA codes to achieve full rate and full diversity. Moreover, the same diversity produce as that of orthogonal space-time block code with linear complex precoder (OSTBC-LCP) is achieved. Meanwhile, the size of the linear complex precoder can be reduced by half without affecting performance, which means the same complexity of decoding as that of the conventional ABBA code is guaranteed.

• ETRI Journal
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• v.41 no.2
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• pp.145-159
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• 2019

To reduce the number of radio frequency (RF) chains in multiple input multiple output (MIMO) systems, generalized spatial modulation (GSM) techniques have been proposed in the literature. In this paper, we propose a zero-forcing (ZF)-based detector, which performs an initial pruning of the search tree that will be considered as the initial condition in a sphere decoding (SD) algorithm. The proposed method significantly reduces the computational complexity of GSM systems while achieving a near maximum likelihood (ML) performance. We analyze the performance of the proposed method and provide an analytic performance difference between the proposed method and the ML detector. Simulation results show that the performance of the proposed method is very close to that of the ML detector, while achieving a significant computational complexity reduction in comparison with the conventional SD method, in terms of the number of visited nodes. We also present some simulations to assess the accuracy of our theoretical results.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.45-50
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• 2020

A Language model with neural networks commonly trained with likelihood loss. Such that the model can learn the sequence of human text. State-of-the-art results achieved in various language generation tasks, e.g., text summarization, dialogue response generation, and text generation, by utilizing the language model's next token output probabilities. Monotonous and boring outputs are a well-known problem of this model, yet only a few solutions proposed to address this problem. Several decoding techniques proposed to suppress repetitive tokens. Unlikelihood training approached this problem by penalizing candidate tokens probabilities if the tokens already seen in previous steps. While the method successfully showed a less repetitive generated token, the method has a large memory consumption because of the training need a big vocabulary size. We effectively reduced memory footprint by encoding words as sequences of subword units. Finally, we report competitive results with token level unlikelihood training in several automatic evaluations compared to the previous work.