• ETRI Journal
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• v.44 no.6
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• pp.925-935
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• 2022

In this study, we propose a limited feedback-based frequency divided group beamforming with sparse space-frequency transmit diversity coded orthogonal frequency division multiplexing (OFDM) system for ultrareliable low latency communication (URLLC) scenario. The proposed scheme has several advantages over the traditional hybrid beamforming approach, including not requiring downlink channel state information for baseband precoding, supporting distributed multipoint transmission structures for diversity, and reducing beam sweeping latency with little uplink overhead. These are all positive aspects of physical layer characteristics intended for URLLC. It is suggested in the system to manage the multipoint transmission structure realized by distributed panels using a power allocation method based on cooperative game theory. Link-level simulations demonstrate that the proposed scheme offers reliability by achieving both higher diversity order and array gain in a nonline-of-sight channel of selectivity and limited spatial scattering.

• ETRI Journal
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• v.45 no.3
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• pp.379-393
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• 2023

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized leastmean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted l₁-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.

• The Transactions of the Korea Information Processing Society
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• v.2 no.6
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• pp.960-968
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• 1995

In this paper we present a reordering scheme that could lead to efficient vectorization of the preconditioners for the large sparse linear systems arising from partial differential equations on the CRAY-2, This reordering scheme is a line version of the conventional red/black ordering. This reordering scheme, coupled with a variant of ILU(Incomplete LU) preconditioning, can overcome the poor rate of convergence of the conventional red/black reordering, if relatively large number of fill-ins were used. We substantiate our claim by conducting various experiments on the CRAY-2 machine. Also, the computation of the Frobenius norm of the error matrices agree with our claim.

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.80-86
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• 2010

Compressive Sensing (CS) has been widely studied as a promising technique in many applications. The CS theory tells that a signal that is known to be sparse in a specific basis can be reconstructed using convex optimization from far fewer samples than traditional methods use. In this paper, we apply CS technique to Multiple-input multiple-output (MIMO) radar systems which employ uniform linear arrays (ULA). Especially, we investigate the problem of finding the direction-of-arrival (DOA) using CS technique and compare the performance with the conventional adaptive MIMO techniques. The results suggest the CS method can provide the similar performance with far fewer snapshots than the conventional adaptive techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2134-2153
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• 2013

A network-coding-based scheme is proposed to improve the energy efficiency of distributed storage systems in WSNs (Wireless Sensor Networks). We mainly focus on two problems: firstly, consideration is given to effective distributed storage technology; secondly, we address how to effectively repair the data in failed storage nodes. For the first problem, we propose a method to obtain a sparse generator matrix to construct network codes, and this sparse generator matrix is proven to be the sparsest. Benefiting from this matrix, the energy consumption required to implement distributed storage is reduced. For the second problem, we designed a network-coding-based iterative repair method, which adequately utilizes the idea of re-encoding at intermediate nodes from network coding theory. Benefiting from the re-encoding, the energy consumption required by data repair is significantly reduced. Moreover, we provide an explicit lower bound of field size required by this scheme, which implies that it can work over a small field and the required computation overhead is very low. The simulation result verifies that the proposed scheme not only reduces the total energy consumption required to implement distributed storage system in WSNs, but also balances energy consumption of the networks.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.817-821
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• 2001

In this paper, a new learning methodology for kernel methods that results in a sparse representation of kernel space from the training patterns for classification problems is suggested. Among the traditional algorithms of linear discriminant function, this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epoches. For sequential learning of kernel methods, extended SVM and kernel discriminant function are defined. Systematic derivation of learning algorithm is introduced. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.4
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• pp.26-30
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• 2003

The Lanczos algorithm is the most commonly used in approximating a small number of extreme eigenvalues for symmetric large sparse matrices. Global communications in MP(Message Passing) parallel computer decrease the computation speed. In this paper, we introduce the s-step Lanczos method, and s-step method generates reduction matrices which are similar to reduction matrices generated by the standard Lanczos method. One iteration of the s-step Lanczos algorithm corresponds to s iterations of the standard Lanczos algorithm. The s-step method has the minimized global communication and has the superior parallel properties to the standard method. These algorithms are implemented on Cray T3E and performance results are presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.1
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• pp.1-11
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• 2016

We investigate compressive sensing techniques to estimate sparse channel in Orthogonal Frequency Division Multiplexing(OFDM) systems. In the case of large channel delay spread, compressive sensing may not be applicable because it is affected by length of measurement vectors. In this paper, we increase length of measurement vector adding pilot information to OFDM data block. The increased measurement vector improves probability of finding path delay set and Mean Squared Error(MSE) performance. Simulation results show that signal recovery performance of a proposed scheme is better than conventional schemes.

• Communications of the Korean Mathematical Society
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• v.9 no.2
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• pp.449-465
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• 1994

The two common numerical methods to approximate the solution of partial differential equations are the finite element method and the finite difference method. They both lead to solving large sparse linear systems. For many applications, it is not unusal that the order of matrix is greater than 10, 000. For this kind of problem, a direct method such as Gaussian elimination can not be used because of the prohibitive cost. To this end, many iterative methods with modest cost have been studied and proposed by numerical analysts.(omitted)

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.531-531
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• 2000

The challenge is to control unstable nonlinear dynamic systems using only sparse feedback from the environment concerning its performance. The design of such controllers can be achieved by evolving neural networks. An evolutionary approach to train neural networks in realtime is proposed. Evolutionary strategies adapt the weights of neural networks and the threshold values of neuron's synapses. The proposed method has been successfully implemented for pole balancing problem.