• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11C
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• pp.1025-1028
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• 2009

This paper presents upper bounds of Maximum Likelihood (ML) decoding performance of a few irregular LDPC codes using the simple bound and ML input output weight distributions and it is shown that contrary to general opinion that as block length becomes longer, BP decoding performance becomes simply closer to ML decoding performance, before peak degradation, as block length becomes longer, BP decoding performance falls behind ML decoding performance more and after peak degradation, general opinion holds.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.1-10
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• 2007

This paper proposes a multimodal generalized Gaussian distribution (MGGD) to effectively model the varying statistical properties of the extrinsic information. A subsidiary maximum likelihood decoding (MLD) algorithm is subsequently developed to dynamically select the most suitable MGGD parameters to be used in the component maximum a posteriori (MAP) decoders at each decoding iteration to derive the more reliable metrics performance enhancement. Simulation results show that, for a wide range of block lengths, the proposed approach can enhance the overall turbo decoding performance for both parallel and serially concatenated codes in additive white Gaussian noise (AWGN), Rician, and Rayleigh fading channels.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.34-42
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• 2007

The sphere decoder (SD) has recently been proposed to perform maximum likelihood (ML) decoding for multi-input multi-output systems. Employing a 'breadth-first' searching algorithm for closet points in a lattice, we propose a novel ML decoding scheme for multi-input multi-output systems. Simulation results show that the proposed scheme has the same bit error rate performance as the conventional ML decoders while allowing significantly lower computational burden than the SD.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.10 s.352
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• pp.1-7
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• 2006

Lattice decoding concept has been proposed for the implementation of the Maximum-Likelihood detection which is the optimal receiver from the viewpoint of the BER (Bit Error Rate) performance for MIMO (Multiple Input Multiple Output) systems. Sphere decoding algorithm and K-best decoding algorithm are based on the lattice decoding concept. A K-best decoding algorithm shows a good BER performance with relatively low complexity. However, with small K value, the error propagation effect severely degrades the performance. In this paper, we propose an adaptive K-best decoding algorithm which has lower average complexity and better BER performance than conventional K-best decoding algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.7 s.337
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• pp.71-78
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• 2005

A K-Best algerian is known as optimal for implementing the maximum-likelihood detector (MLD), since it has a fixed maximum complexity compared with the sphere decoding or the maximum-likelihood decoding algorithm. However the computational complexity of the K-Best algrithm is still prohibitively high for practical applications when K is large enough. If small value of K is used, the maximum complexity decreases but error flooring at high SNR is caused by error propagation. In this paper, a K-reduction scheme, which reduces K according to each search level, is proposed to solve error propagation problems. Simulations showed that the proposed scheme provides the improved performance in the bit error rate and also reduces the average complexity compared with the conventional scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9C
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• pp.734-742
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• 2008

In spatially multiplexed MIMO systems that enable high data rate transmission over wireless communication channels, the spatial demultiplexing at the receiver is a challenging task and various demultiplexing methods have been developed. Among the previous methods, maximum likelihood detection with QR decomposition and M-algorithm (QRM-MLD), sphere decoding (SD), QOC, and MOC schemes have been reported to achieve a (near) maximum likelihood (ML) hard decision performance. In general, however, the reliability of soft output of these schemes is not satisfactory. In this paper, we propose a method which enhances the reliability of soft output. By computer simulations, we demonstrate the improved performance by the proposed method.

• Journal of Communications and Networks
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• v.13 no.5
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• pp.511-517
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• 2011

The capacity of wireless ad-hoc networks is analyzed for all kinds of information dissemination based on single and multiple packet reception schemes under the physical model. To represent the general information dissemination scheme, we use (n, m, k)-cast model [1] where n, m, and k (k ${\leq}$ m) are the number of nodes, destinations and closest destinations that actually receive packets from the source in each (n, m, k)-cast group, respectively. We first consider point-to-point communication, which implies single packet reception between transmitter-receiver pairs and compute the (n, m, k)-cast communications. Next, the achievable throughput capacity is computed when receiver nodes are endowed with multipacket reception (MPR) capability. We adopt maximum likelihood decoding (MLD) and successive interference cancellation as optimal and suboptimal decoding schemes for MPR. We also demonstrate that physical and protocol models for MPR render the same capacity when we utilize MLD for decoding.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7C
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• pp.616-626
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• 2007

In spatially multiplexed MIMO systems that enable high data rate transmission over wireless communication channels, the spatial demultiplexing at the receiver is a challenging task, and various demultiplexing methods have been developed recently by many researchers. Among the previous methods, maximum likelihood detection with QR decomposition and M-algorithm (QRM-MM)), and sphere decoding (SD) schemes have been reported to achieve a (near) maximum likelihood (ML) performance. In this paper, we propose a novel signal detection method that achieves a near ML performance in a computationally efficient manner. The proposed method is demonstrated via a set of computer simulations that the proposed method achieves a near ML performance while requiring a complexity that is comparable to that of the conventional MMSE-OSIC. We also show that the log likelihood ratio (LLR) values for all bits are obtained without additional calculation but as byproduct in the proposed detection method, while in the previous QRM-MLD, SD, additional computation is necessary after the hard decision for LLR calculation.

• Journal of Communications and Networks
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• v.13 no.5
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• pp.421-427
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• 2011

Problems encountered in IEEE 802.11 medium access control (MAC) design are interferences from neighboring or hidden nodes and collision from simultaneous transmissions within the same contention floors. This paper presents the selective decoding schemes in MAC protocol for multiple input multiple output ad-hoc networks. It is able to mitigate interferences by using a developed minimum mean-squared error technique. This interference mitigation combined with the maximum likelihood decoding schemes for the Alamouti coding enables the receiver to decode and differentiate the desired data streams from co-channel data streams. As a result, it allows a pair of simultaneous transmissions to the same or different nodes which yields the network utilization increase. Moreover, the presented three decoding schemes and time line operations are optimally selected corresponding to the transmission demand of neighboring nodes to avoid collision. The selection is determined by the number of request to send (RTS) packets and the type of clear to send packets. Both theoretical channel capacity and simulation results show that the proposed selective decoding scheme MAC protocol outperforms the mitigation interference using multiple antennas and the parallel RTS processing protocols for the cases of (1) single data stream and (2) two independent data streams which are simultaneously transmitted by two independent transmitters.

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.2
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• pp.219-228
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• 1997

The Fano metric is the maximum likelihood decoding choice for convlutional code for binary symmetric channel. The Fano metric assumes that it has previous knowledge of channel error probability. However, the bit errors in real channel occur in bursts and the channel error probability can not be known exactly. Thus, the Fano metric is not the maximum likelihood choice for bursty-noise channel. In this paper universal metri which dose not require the previous knowlege of the channel transition probability is used for sequential decoding. It is shown that the complexity of the universal is much less than that of the Fano metric bursty-noise channel, since it is estimated on a branch by branch basis.