• Journal of Communications and Networks
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• v.18 no.5
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• pp.735-743
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• 2016

This paper proposes a direction-of-arrival (DOA)-based beamforming approach for multi-cell massive multiple-input multiple-output systems with uniform rectangular arrays (URAs). The proposed approach utilizes the steering vectors of the URA to form a basis of the spatial space and selects the partial space for beamforming according to the DOA information. As a result, the proposed approach is of lower computational complexity than the existing methods which utilize the channel covariance matrices. Moreover, the analysis demonstrates that the proposed approach can eliminate the interference in the limit of infinite number of the URA antennas. Since the proposed approach utilizes the multipaths to enhance the signal rather than discarding them, the proposed approach is of better performance than the existing low-complexity method, which is verified by the simulation results.

• Journal of information and communication convergence engineering
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• v.12 no.1
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• pp.8-13
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• 2014

We consider the sensor selection problem in large sensor networks where the goal is to find the best set of sensors that maximizes application objectives. Since sensor selection typically involves a large number of sensors, a low complexity should be maintained for practical applications. We propose a geometry-based sensor selection algorithm that utilizes only the information of sensor locations. In particular, by observing that sensors clustered together tend to have redundant information, we theorize that the redundancy is inversely proportional to the distance between sensors and seek to minimize this redundancy by searching for a set of sensors with the maximum average distance. To further reduce the computational complexity, we perform an iterative sequential search without losing optimality. We apply the proposed algorithm to an acoustic sensor network for source localization, and demonstrate using simulations that the proposed algorithm yields significant improvements in the localization performance with respect to the randomly generated sets of sensors.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06a
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• pp.3-8
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• 1998

The conversion of monoscopic video to 3D stereoscopic video has been studied by some pioneering researchers. In spite of the commercial of potential of the technology, two problems have bothered the progress of this research area: vertical motion parallax and high computational complexity. The former causes the low 3D perception, while the hardware complexity is required by the latter. The previous research has dealt with NTSC video, thur requiring complex processing steps, one of which is motion estimation. This paper proposes 3D stereoscopic conversion method of MPGE encoded data. Our proposed method has the advantage that motion estimation can be avoided by processing MPEG compressed data for the extraction of motion data as well as that camera and object motion in random in random directions can be handled.

• Journal of electromagnetic engineering and science
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• v.11 no.2
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• pp.71-75
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• 2011

The sum-rate maximization rule can find an optimal user set that maximizes the sum capacity in multiple input multiple output (MIMO) broadcast channels (BCs), but the search space for finding the optimal user set becomes prohibitively large as the number of users increases. The proposed algorithm selects a user set of the largest effective channel norms based on statistical channel state information (CSI) for reducing the computational complexity, and uses Tomlinson-Harashima precoding (THP) for minimizing the interference between selected users in time-varying MIMO BCs.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.11
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• pp.2033-2035
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• 2007

This paper proposes a novel iterative LMS-based decision feedback equalizer for short burst transmission with relatively short training sequence. In the proposed equalizer, the longer concatenated training sequence can provide the more sufficient channel information and the reused original training sequence can provide the correct decision feedback information. In addition, the overall adaptive processing is performed using the low complexity LMS algorithm. The study shows the performance of the proposed method is enhanced with the number of iterations and, furthermore, better than that of the conventional LMS-based DFEs with the training sequence of longer or equal length. Computational complexity is increased linearly with the number of iterations.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.320-326
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• 2016

In existing literature on multiple-input multiple-output (MIMO) relaying communication systems, antenna selection is often implemented by maximizing the channel capacity or the output single-to-noise ratio (SNR). In this paper, we propose an energy-efficient low-complexity antenna selection scheme for MIMO relaying communication systems. The proposed algorithm is based on beamforming and maximizing the Frobenius norm to jointly optimize the transmit power, number of active antennas, and antenna subsets at the source, relaying and destination. We maximize the energy efficiency between the link of source to relay and the link of relay to destination to obtain the maximum energy efficiency of the system, subject to the SNR constraint. Compared to existing antenna selection methods forMIMO relaying communication systems, simulation results demonstrate that the proposed method can save more power in term of energy efficiency, while having lower computational complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1244-1246
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• 2016

Influence maximization problem is to select seed node set, which maximizes information spread in social networks. Greedy algorithm shows an optimum solution, but has a high computational cost. A few heuristic algorithms were proposed to reduce the complexity, but their performance in influence maximization is limited. In this paper, we propose general degree discount algorithm, and show that it has better performance while keeping complexity low.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.143-146
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• 2009

In this paper, we investigated the list sphere decoders (LSD) for multiple-input multiple-output (MIMO) systems. We showed that the ordering procedures play an important role in LSD in order to achieve the low complexity without degrading the bit-error-rate (BER) performance. Then, we proposed a novel ordering algorithm for the LSD which uses a look-up table and simply comparative operations. Comparative results in terms of BER performance and computational complexity are provided through computer simulations.

• Journal of Communications and Networks
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• v.13 no.3
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• pp.232-239
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• 2011

This paper investigates the user selection problem of successive zero-forcing precoded multiuser multiple-input multiple-output (MU-MIMO) downlink systems, in which the base station and mobile receivers are equipped with multiple antennas. Assuming full knowledge of the channel state information at the transmitter, dirty paper coding (DPC) is an optimal precoding strategy, but practical implementation is difficult because of its excessive complexity. As a suboptimal DPC solution, successive zero-forcing DPC (SZF-DPC) was recently proposed; it employs partial interference cancellation at the transmitter with dirty paper encoding. Because of a dimensionality constraint, the base station may select a subset of users to serve in order to maximize the total throughput. The exhaustive search algorithm is optimal; however, its computational complexity is prohibitive. In this paper, we develop two low-complexity user scheduling algorithms to maximize the sum rate capacity of MU-MIMO systems with SZF-DPC. Both algorithms add one user at a time. The first algorithm selects the user with the maximum product of the maximum column norm and maximum eigenvalue. The second algorithm selects the user with the maximum product of the minimum column norm and minimum eigenvalue. Simulation results demonstrate that the second algorithm achieves a performance similar to that of a previously proposed capacity-based selection algorithm at a high signal-to-noise (SNR), and the first algorithm achieves performance very similar to that of a capacity-based algorithm at a low SNR, but both do so with much lower complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1C
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• pp.82-91
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• 2005

This paper introduces an efficient affine projection algorithm(APA) using iterative hyperplane projection. Among various fast converging adaptation algorithms, APA has been preferred to be employed for various applications due to its inherent effectiveness against the rank deficient problem. However, the amount of complexity of the conventional APA could not be negligible because of the accomplishment of sample matrix inversion(SMI). Moreover, the 'shifting invariance property' usually exploited in single channel case does not hold for the application of space-time decision-directed equalizer(STDE) deployed in single-input-multi-output(SIMO) systems. Thus, it is impossible to utilize the fast adaptation schemes such as fast transversal filter(FlF) having low-complexity. To accomplish such tasks, this paper introduces the low-complexity APA by employing hyperplane projection algorithm, which shows the excellent tracking capability as well as the fast convergence. In order to confirm th validity of the proposed method, its performance is evaluated under wireless SIMO channel in respect to bit error rate(BER) behavior and computational complexity.