• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.42-46
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• 2018

Adaptive signal processing is quite important in various signal and communication environments. In adaptive signal processing methods since the least mean square(LMS) algorithm is simple and robust, it is used everywhere. As the step is varied in the variable step(VS) LMS algorithm, the fast convergence speed and the small excess mean square error can be obtained. Various variable step LMS algorithms are researched for better performances. But in some of variable step LMS algorithms the computational complexity is quite large for better performances. The fixed step LMS algorithm with a low computational complexity merit and the variable step LMS algorithm with a fast convergence merit are combined in the proposed sporadic step algorithm. As the step is sporadically updated, the performances of the variable step LMS algorithm can be maintained in the low update rate using Fibonacci sequence. The performances of the proposed variable step LMS algorithm are proved in the adaptive equalizer.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.418-429
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• 2016

In this paper, we propose a hybrid Wyner-Ziv video coding structure that combines conventional motion predictive video coding and Wyner-Ziv video coding to eliminate the feedback channel, which is a major practical problem in applications using the Wyner-Ziv video coding approach. The proposed method divides a hybrid frame into two regions. One is coded by a motion predictive video coder, and the other by the Wyner-Ziv coding method. The proposed encoder estimates side information with low computational complexity, using the coding information of the motion predictive coded region, and estimates the number of syndrome bits required to decode the region. The decoder generates side information using the same method as the encoder, which also reduces the computational complexity in the decoder. Experimental results show that the proposed method can eliminate the feedback channel without incurring a significant rate-distortion performance loss.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.153-162
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• 1995

When vector quantization is used in low rate image coding (e.g., R<0.5), the primary problem is the tremendous computational complexity which is required to search the whole codebook to find the closest codevector to an input vector. Since the number of code vectors in a vector quantizer is given by an exponential function of the dimension. i.e., L=2$^{nR}$ where Rn. To alleviate this problem, a multiple shell structure of hypercube feature maps (MSSHFM) is proposed. A binary HFM of k-dimension is composed of nodes at hypercube vertices and a multiple shell architecture is constructed by surrounding the k-dimensional hfm with a (k+1)-dimensional HFM. Such a multiple shell construction of nodes inherently has a complete tree structure in it and an efficient partial search scheme can be applied with drastically reduced computational complexity, computer simulations of still image coding were conducted and the validity of the proposed method has been verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3524-3542
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• 2017

In a single-source and multi-relay amplify-forward (AF) cooperative network, the outage probability and the power allocation are two key factors to influence the performance of an entire system. In this paper, an optimized AF relay selection by an exclusive method and near optimal power allocation (NOPA) is proposed for both good outage probability and power efficiency. Given the same power at the source and the relay nodes, a threshold for selecting the relay nodes is deduced and employed to minimize the average outage probability. It mainly excludes the relay nodes with much higher thresholds over the aforementioned threshold and thus the remainders of the relay nodes participate in cooperative forwarding efficiently. So the proposed scheme can improve the utility of the resources in the cooperative multi-relay system, as well as reduce the computational complexity. In addition, based on the proposed scheme, a NOPA is also suggested to approach sub-optimal power efficiency with low complexity. Simulation results show that the proposed scheme obtains about 2.1dB and 5.8dB performance gain at outage probability of $10^{-4}$, when compared with the all-relay-forward (6 participated relays) and the single-relay-forward schemes. Furthermore, it obtains the minimum outage probability among all selective relay schemes with the same number of the relays. Meanwhile, it approaches closely to the optimal exhaustive scheme, thus reduce much complexity. Moreover, the proposed NOPA scheme achieves better outage probability than those of the equal power allocation schemes. Therefore, the proposed scheme can obtain good outage probability, low computational complexity and high power efficiency, which makes it pragmatic efficiently in the single-source and multi-relay AF based cooperative networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.10
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• pp.841-849
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• 2012

This paper proposes an low-complexity soft-output multiple-input multiple-output (soft-MIMO) detection algorithm for achieving soft-output maximum-likelihood (soft-ML) performance under max-log approximation. The proposed algorithm is based on a parallel tree-search (PTS) applying a channel ordering by a sorted-QR decomposition (SQRD) with altered sort order. The empty-set problem that can occur in calculation of log-likelihood ratio (LLR) for each bit is solved by inserting additional nodes at each search level. Since only the closest node is inserted among nodes with opposite bit value to a selected node, the proposed node insertion scheme is very efficient in the perspective of computational complexity. The computational complexity of the proposed algorithm is approximately 37-74% of that of existing algorithms, and from simulation results for a $4{\times}4$ system, the proposed algorithm shows a performance degradation of less than 0.1dB.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1010-1019
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• 2016

The K-user multiple-input single-output broadcast channel is considered under per-antenna power constraints, i. e., each transmit antenna must satisfy its own power constraints. A low complexity zeroforcing(ZF) precoder is proposed when the number of transmit antennas M is greater than K. The proposed precoder design significantly reduces computational complexity for the precoder construction while attaining the sum spectral efficiency close to that achievable by the optimal ZF precoder.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.20-25
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• 2016

This study addresses a low complexity and lossless frame memory compression algorithm based on block-buffer structure for efficient high resolution video processing. Our study utilizes the block-based MHT (modified Hadamard transform) for spatial decorrelation and AGR (adaptive Golomb-Rice) coding as an entropy encoding stage to achieve lossless image compression with low complexity and efficient hardware implementation. The MHT contains only adders and 1-bit shift operators. As a result of AGR not requiring additional memory space and memory access operations, AGR is effective for low complexity development. Comprehensive experiments and computational complexity analysis demonstrate that the proposed algorithm accomplishes superior compression performance relative to existing methods, and can be applied to hardware devices without image quality degradation as well as negligible modification of the existing codec structure. Moreover, the proposed method does not require the memory access operation, and thus it can reduce costs for hardware implementation and can be useful for processing high resolution video over Full HD.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.41-48
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• 2020

In this paper, we derive a common computational part in an algorithm that can simultaneously perform multiplication and square over finite fields, and propose a low-area bit-parallel systolic array that reduces hardware through sequential processing. The proposed systolic array has less space and area-time (AT) complexity than the existing related arrays. In detail, the proposed systolic array saves about 48% and 44% of Choi-Lee and Kim-Kim's systolic arrays in terms of area complexity, and about 74% and 44% in AT complexity. Therefore, the proposed systolic array is suitable for VLSI implementation and can be applied as a basic component in hardware constrained environment such as IoT.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2908-2924
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• 2019

Multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) is widely applied in wireless communication by virtue of its excellent properties in data transmission rate and transmission accuracy. However, as a major drawback of MIMO-OFDM systems, the high peak-to-average power ratio (PAPR) complicates the design of the power amplifier at the receiver end. Some available PAPR reduction methods such as selective mapping (SLM) suffer from high computational complexity. In this paper, a low-complexity SLM method based on active constellation extension (ACE) and joint space-time selective mapping (AST-SLM) for reducing PAPR in Alamouti STBC MIMO-OFDM systems is proposed. In SLM scheme, two IFFT operations are required for obtaining each transmission sequence pair, and the selected phase vector is transmitted as side information(SI). However, in the proposed AST-SLM method, only a few IFFT operations are required for generating all the transmission sequence pairs. The complexity of AST-SLM is at least 86% less than SLM. In addition, the SI needed in AST-SLM is at least 92.1% less than SLM by using the presented blind detection scheme to estimate SI. We show, analytically and with simulations, that AST-SLM can achieve significant performance of PAPR reduction and close performance of bit error rate (BER) compared to SLM scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3458-3478
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• 2022

Automatic modulation recognition is the core algorithm in the field of modulation classification in communication systems. Our investigations show that deep learning (DL) based modulation recognition techniques have achieved effective progress for multiple-input multiple-output (MIMO) systems. However, network complexity is always an additional burden for high-accuracy classifications, which makes it impractical. Therefore, in this paper, we propose a low-complexity dimensional interactive lightweight network (DilNet) for MIMO systems. Specifically, the signals received by different antennas are cooperatively input into the network, and the network calculation amount is reduced through the depth-wise separable convolution. A two-dimensional interactive attention (TDIA) module is designed to extract interactive information of different dimensions, and improve the effectiveness of the cooperation features. In addition, the TDIA module ensures low complexity through compressing the convolution dimension, and the computational burden after inserting TDIA is also acceptable. Finally, the network is trained with a penalized statistical entropy loss function. Simulation results show that compared to existing modulation recognition methods, the proposed DilNet dramatically reduces the model complexity. The dimensional interactive lightweight network trained by penalized statistical entropy also performs better for recognition accuracy in MIMO systems.