• 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.

• 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.

• Journal of Communications and Networks
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• v.11 no.1
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• pp.20-25
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• 2009

The maximum likelihood detection with QR decomposition and M-algorithm (QRM-MLD) has been presented as a suboptimum multiple-input multiple-output (MIMO) detection scheme which can provide almost the same performance as the optimum maximum likelihood (ML) MIMO detection scheme but with the reduced complexity. However, due to the lack of parallelism and the regularity in the decoding structure, the conventional QRM-MLD which uses the tree-structure still has very high complexity for the very large scale integration (VLSI) implementation. In this paper, we modify the tree-structure of conventional QRM-MLD into trellis-structure in order to obtain high operational parallelism and regularity and then apply the Viterbi algorithm to the QRM-MLD to ease the burden of the VLSI implementation.We show from our selected numerical examples that, by using the QRM-MLD with our proposed trellis-structure, we can reduce the complexity significantly compared to the tree-structure based QRM-MLD while the performance degradation of our proposed scheme is negligible.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.981-989
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• 2015

In a log likelihood ratio(LLR) calculation of the detected symbol, multiple-input multiple-output(MIMO) system applying an optimal or suboptimal algorithm such as a maximum likelihood(ML) detection, sphere decoding(SD), and QR decomposition with M-algorithm Maximum Likelihood Detection(QRM-MLD) suffers from exponential complexity growth with number of spatial streams and modulation order. In this paper, we propose a LLR calculation method with very low complexity in the QRM-MLD based symbol detector for a high order modulation based $N_T{\times}N_R$ MIMO system. It is able to approach bit error rate(BER) performance of full maximum likelihood detector to within 1 dB. We also analyze the BER performance through computer simulation to verify the validity of the proposed method.

• 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.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11A
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• pp.862-869
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• 2009

We propose a new adaptive K-best Sphere Decoding (SD) algorithm for Multiple Input Multiple Output (MIMO) systems where the number of survivor paths, K is changed based on the characteristics of path metrics which contain the instantaneous channel condition. In order to overcome a major drawback of Maximum Likelihood Detection (MLD) which exponentially increases the computational complexity with the number of transmit antennas, the conventional adaptive K-best SD algorithms which achieve near to MLD performance have been proposed. However, they still have redundant computation complexity since they only employ the channel fading gain as a channel condition indicator without instantaneous Signal to Noise Ratio (SNR) information. hi order to complement this drawback, the proposed algorithm use the characteristics of path metrics as a simple channel indicator. It is found that the ratio of the minimum path metric to the other path metrics reflects SNR information as well as channel fading gain. By adaptively changing K based on this ratio, the proposed algorithm more effectively reduce the computation complexity compared to the conventional K-best algorithms which achieve same performance.

• ETRI Journal
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• v.36 no.4
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• pp.564-571
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• 2014

Sphere decoding (SD) for multiple-input and multiple-output systems is a well-recognized approach for achieving near-maximum likelihood performance with reduced complexity. SD is a tree search process, whereby a large number of nodes can be searched in an effort to find an estimation of a transmitted symbol vector. In this paper, a simple and generalized approach called layer pruning is proposed to achieve complexity reduction in SD. Pruning a layer from a search process reduces the total number of nodes in a sphere search. The symbols corresponding to the pruned layer are obtained by adopting a QRM-MLD receiver. Simulation results show that the proposed method reduces the number of nodes to be searched for decoding the transmitted symbols by maintaining negligible performance loss. The proposed technique reduces the complexity by 35% to 42% in the low and medium signal-to-noise ratio regime. To demonstrate the potential of our method, we compare the results with another well-known method - namely, probabilistic tree pruning SD.

• 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.

• Journal of Communications and Networks
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• v.9 no.2
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• pp.167-176
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• 2007

In multimedia communication systems, efficient transmission system design should incorporate the use of matching unequal error protection (UEP), since source coders exhibit unequal bit error sensitivity. In this paper, we present UEP schemes which exploit differences in bit error protection levels in orthogonal frequency division multiplexing (OFDM) systems over frequency selective fading channels. We introduce an UEP scheme which improves the link performance with multiple transmit and receive antennas. Especially, we propose a new receiver structure based on two stage Maximum Likelihood detection (MLD) schemes which can approach the performance of a full search MLD receiver with much reduced computational complexity. In the performance analysis, we derive a generalized pairwise error probability expression for the proposed UEP schemes. Simulation results show that the proposed schemes achieve a significant performance gain over the conventional equal error protection (EEP) scheme.