• Journal of Communications and Networks
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• v.16 no.6
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• pp.620-626
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• 2014

In this paper, we propose a parallel collaborative sphere decoder with a scalable architecture promising quasi-maximum likelyhood performance with a relatively small amount of computational resources. This design offers a hardware-friendly algorithm using a modified node operation through fixing the variable complexity of the critical path caused by the sequential nature of the conventional sphere decoder (SD). It also reduces the computational complexity compared to the fixed-complexity sphere decoder (FSD) algorithm by tree pruning using collaboratively operated node operators. A Monte Carlo simulation shows that our proposed design can be implemented using only half the parallel operators compared to the approach using an ideal fully parallel scheme such as FSD, with only about a 7% increase of the normalized decoding time for MIMO dimensions of $16{\times}16$ with 16-QAM modulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.890-893
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• 2008

In this paper, we present a low complexity modified multi-level sphere decoder (SD) for multiple-input multiple-output (MIMO) systems employing quadrature amplitude modulation (QAM) signals. The proposed decoder, exploiting the multi-level structure of the QAM signal scheme, first decomposes the high-level constellation into low-level 4-QAM constellations, so-called sub-constellations. Then, it deploys SD in the sub-constellations in parallel. In addition, in the searching stage, it uses the optimal low-complexity sort method. Computer simulation results show that the proposed decoder can provide near optimal maximum-likelihood (ML) performance while it significantly reduces the computational load.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.567-571
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• 2008

In this paper, an overview on the ordering sphere decoder (SD) class for space-time codes (STC) will be presented. In SDs, the ordering techniques are considered as promising methods for reducing complexity by exploiting a sorted list of candidates, thus decreasing the number of tested points. First, we will present the current state of art of SD with their advantages and disadvantages. Then, the overview of simply geometrical approaches for ordering is presented to address the question to overcome the disadvantages. The computer simulation results shown that, thanks to the aid of ordering, the ordering SDs can achieve optimal bit-error-rate (BER) performance while requiring the very low complexity, which is comparable to that of linear sub-optimal decoders.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10A
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• pp.976-981
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• 2006

In this paper, we describe a hybrid ZF-SD method. The method is based on dimensionality reduction via predecoding and cancellation of those symbols that can be quickly and reliably detected by a linear decoder. The proposed method shows BER performance similar to SD but with much lower computational complexity than SD.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.6A
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• pp.577-582
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• 2008

In this paper, we present a scheme to overcome the worst case complexity of the sphere decoder. If the number of visited nodes reaches the threshold, the detected symbol vector is determined between two candidate symbol vectors. One candidate symbol vector is obtained from the demodulated output of ZF receiver which is initial stage of the sphere decoder. The other candidate symbol vector consists of two sub-symbol vectors. The first sub-symbol vector consists of lately visited nodes running from the most upper layer. The second one contains corresponding demodulated outputs of ZF receiver. Between these two candidate symbol vectors, the one with smaller euclidean distance to the received symbol vector is chosen as detected symbol vector. In addition, we show the upper bound of symbol error rate performance for the sphere decoder using the proposed scheme. In the simulation, the proposed scheme shows the significant reduction of the worst case complexity while having negligible SER performance degradation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.11-19
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• 2014

In this paper, a low complexity parallel sphere decoder algorithm is proposed for high-order MIMO system. It reduces the computational complexity compared to the fixed-complexity sphere decoder (FSD) algorithm by static tree-pruning and dynamic tree-pruning using scalable node operators, and offers near-maximum likelihood decoding performance. Moreover, it also offers hardware-friendly node operation algorithm through fixing the variable computational complexity caused by the sequential nature of the conventional SD algorithm. A Monte Carlo simulation shows our proposed algorithm decreases the average number of expanded nodes by 55% with only 6.3% increase of the normalized decoding time compared to a full parallelized FSD algorithm for high-order MIMO communication system with 16 QAM modulation.

• 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.48 no.3
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• pp.13-18
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• 2011

Sphere Decoding (SD) is a decoding technique able to achieve the Maximum Likelihood (ML) performance in fading environments; nevertheless, the main disadvantage of this technique is its high complexity, especially in poor channel conditions. In this paper, we present an adaptive hybrid algorithm which reduces the conventional Sphere Decoder's complexity and keeps the ML performance. The system called Adaptive OSIC-SD modifies its operation based on Signal to Noise Ratio (SNR) information and achieves an optimal performance in terms of Bit Error Rate (BER) and complexity. Through simulations, we probe that the proposed system maintains almost the same bit error rate performance of the conventional SD, and exhibits a lower, quasi-constant complexity.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.888-894
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• 2007

The computational complexity of a sphere decoder (SD) is conventionally reduced by decoding order scheme which sorts candidate symbols in the ascending order of the Euclidean distance from the output of a zero-forcing (ZF) receiver. However, since the ZF output may not be a reliable sorting reference, we propose two types of sorting schemes to allow faster decoding. The first is to use the newly found lattice points in the previous search round instead of the ZF output (Type I). Since these lattice points are closer to the received signal than the ZF output, they can serve as a more reliable sorting reference for finding the maximum likelihood (ML) solution. The second sorting scheme is to sort candidate symbols in descending order according to the number of candidate symbols in the following layer, which are called child symbols (Type II). These two proposed sorting schemes can be combined with layer sorting for more complexity reduction. Through simulation, the Type I and Type II sorting schemes were found to provide 12% and 20% complexity reduction respectively over conventional sorting schemes. When they are combined with layer sorting, Type I and Type II provide an additional 10-15% complexity reduction while maintaining detection performance.