• Communications for Statistical Applications and Methods
• /
• v.9 no.2
• /
• pp.315-324
• /
• 2002

For estimating the error variance under the relative squared error loss in two-way analysis of variance models, we provide a class of hierarchical Bayes estimators and then derive a subclass of the hierarchical Bayes estimators, each member of which dominates the best multiple of the error sum of squares which is known to be minimax. We also identify a subclass of non-minimax hierarchical Bayes estimators.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.8
• /
• pp.2722-2742
• /
• 2014

We propose methods of linear transceiver design for two different power constraints, sum relay power constraint and per relay power constraint, which determine signal processing matrices such as base station (BS) transmitter, relay precoders and user receivers to minimize sum mean square error (SMSE) for multi-relay multi-user (MRMU) networks. However, since the formulated problem is non-convex one which is hard to be solved, we suboptimally solve the problems by defining convex subproblems with some fixed variables. We adopt iterative sequential designs of which each iteration stage corresponds to each subproblem. Karush-Kuhn-Tucker (KKT) theorem and SMSE duality are employed as specific methods to solve subproblems. The numerical results verify that the proposed methods provide comparable performance to that of a full relay cooperation bound (FRCB) method while outperforming the simple amplify-and-forward (SAF) and minimum mean square error (MMSE) relaying in terms of not only SMSE, but also the sum rate.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.45 no.2
• /
• pp.37-43
• /
• 2008

The performance and hardware complexity of LDPC decoders depend on the design parameters of quantization, the clipping threshold $c_{th}$ and the number of quantization bits q, and also on the maximum number of decoding iterations. In this paper, the BER performances of LDPC codes are evaluated according to the clipping threshold $c_{th}$ and the number of quantization bits q through the simulation studies. By comparing the quantized Min-Sum algorithm with the ideal Min-Sum algorithm, it is shown that the quantized case with $c_{th}=2.5$ and q=6 has the best performance, which approaches the idea case. The decoding complexities are calculated and the word error rates(WER) are estimated by using the pdf which is obtained through the statistical analyses on the iteration numbers. These results can be utilized to tradeoff between the decoding performance and the complexity in LDPC decoder design.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.771-774
• /
• 2010

In this paper, BER performance of LDPC(Low-Density Parity-Check) decoder for WiMAX is analyzed, and optimal design conditions of LDPC decoder are derived. The min-sum LDPC decoding algorithm which is based on an approximation of LLR sum-product algorithm is modeled and simulated by Matlab, and it is analyzed that the effects of LLR approximation bit-width and maximum iteration cycles on the bit error rate(BER) performance of LDCP decoder. The parity check matrix for IEEE 802.16e standard which has block length of 2304 and code rate of 1/2 is used, and AWGN channel with QPSK modulation is assumed. The simulation results show that optimal BER performance is achieved for 7 iteration cycles and LLR bit-width of (8,6).

• Journal of the Korean Data and Information Science Society
• /
• v.16 no.2
• /
• pp.371-382
• /
• 2005

The paper compares the performance of some widely used Bayesian estimators such as Bayes estimator, empirical Bayes estimator, constrained Bayes estimator and constrained Bayes estimator by means of a new measurement under squared error loss function for the typical normal-normal situation. The proposed measurement is a weighted sum of the precisions of first and second moments. As a result, one can gets the criterion according to the size of prior variance against the population variance.

• Journal of Korea Water Resources Association
• /
• v.36 no.5
• /
• pp.751-760
• /
• 2003

Error analysis of finite difference equation on the Muskingum-Cunge flood routing method with free time and space weighting factor was carried out. The error analysis shows that the numerical solution of the Muskingum-Cunge method becomes diverged with time when the sum of time weighting factor and space weighting factor is greater than 1.0. Numerical diffusion increases when the sum of time weighting factor and space weighting factor decreases. Numerical diffusion and numerical oscillation increase when the grid resolution is coarse. Numerical experiments and field applications show that the Muskingum-Cunge method with free space weighting factor is more effective for simulating the flood routing with great peak diminution than conventional Muskingum-Cunge method with fixed space weighting factor, 0.5.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.1A
• /
• pp.54-61
• /
• 2010

We propose a subspace interference alignment by orthogonalization of reference vectors. The proposed method improves the sum-rate capacity degradation due to the channel decomposition error and channel estimation error in the real environment. Using the proposed method, each cell uses the reference vector that is orthogonal to the adjacent cells. Then the residual interference produced by the channel decomposition error and the channel estimation error is decreased. The simulation results demonstrate that the proposed method achieves the enhanced sum-rate capacity.

• ETRI Journal
• /
• v.36 no.4
• /
• pp.591-598
• /
• 2014

This paper proposes a new min-sum algorithm for low-density parity-check decoding. In this paper, we first define the negative and positive effects of the received signal-to-noise ratio (SNR) in the min-sum decoding algorithm. To improve the performance of error correction by considering the negative and positive effects of the received SNR, the proposed algorithm applies adaptive scaling factors not only to extrinsic information but also to a received log-likelihood ratio. We also propose a combined variable and check node architecture to realize the proposed algorithm with low complexity. The simulation results show that the proposed algorithm achieves up to 0.4 dB coding gain with low complexity compared to existing min-sum-based algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.11
• /
• pp.2616-2635
• /
• 2013

In this paper, we propose an iterative transceiver design in a multi-relay multi-user multiple-input multiple-output (MIMO) system. The design criterion is to minimize sum mean squared error (SMSE) under relay sum power constraint (RSPC) where only local channel state information (CSI)s are available at relays. Local CSI at a relay is defined as the CSI of the channel between BS and the relay in the $1^{st}$ hop link, and the CSI of the channel between the relay and all users in the $2^{nd}$ hop link. Exploiting BS transmitter structure which is concatenated with block diagonalization (BD) precoder, each relay's precoder can be determined using local CSI at the relay. The proposed scheme is based on sequential iteration of two stages; stage 1 determines BS transmitter and relay precoders jointly with SMSE duality, and stage 2 determines user receivers. We verify that the proposed scheme outperforms simple amplify-and-forward (SAF), minimum mean squared error (MMSE) relay, and an existing good scheme of [13] in terms of both SMSE and sum-rate performances.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.1285-1288
• /
• 1998

In this paper, we propose a new learning algorithm for multilayer neural networks. In the error backpropagation that is widely used for training multilayer neural networks, weights are adjusted to reduce the error function that is sum of squared error for all the neurons in the output layer of the network. In the proposed learning algorithm, we consider each output of the output layer as a function of weights and adjust the weights directly so that the output neurons produce the desired outputs. Experiments show that the proposed algorithm outperforms the backpropagation learning algorithm.