• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.10
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• pp.947-955
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• 2012

In this paper, we propose iterative algorithms to obtain the transmit and the receive vectors for interference alignment in cellular network. Although the conventional interference alignment algorithms for interference channels can be applied to cellular network, the number of iterations required to achieve a high sum rate is very large. The key idea in the proposed algorithms is to ignore intra-cell interference in updating the transmit vector for uplink and the receive vector for downlink. Numerical results show that the proposed algorithms achieve higher sum rates than the conventional algorithms for given iteration numbers when multiple antennas and a single carrier are used for interference alignment. It is also shown that the proposed algorithms outperform the conventional algorithms when a single antenna and multiple subcarriers are used for interference alignment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.485-494
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• 2011

In this paper, novel sub-space interference alignment algorithms are proposed to boost the capacity in multi-cell environment. In the case of conventional sub-space alignment, arbitrary reference vectors have been adopted as transmitting vectors at the transmitter side, and the inter-cell interference among users are eliminated by using orthogonal vectors of the chosen reference vectors at the receiver side. However, in this case, sum-rate varies using different reference vectors even though the channel values keep constant, and vice versa. Therefore, the relationship between reference vectors and channel values are analyzed in this paper, and novel interference alignment algorithms are proposed to increase multi-cell capacity. Reference vectors with similar magnitude are adopted in the proposed algorithm. Simulation results show that the proposed algorithms provide about 50 % higher sum-rate than conventional algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.7
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• pp.23-28
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• 2010

We propose a reference vector diversity method in multi-cell multi-user uplink system with the subspace interference alignment to obtain higher sum rate capacity. The proposed method transmits several reference vectors before the data transmission, and selects the best reference vector to maximize the cell sum rate. The proposed method provides higher sum-rate capacity compared with the previous interferenc alignment. Simulation result exhibits the proposed method improves the sum-rate capacity by 60%.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.402-405
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• 2012

문서와 문서간의 유사도들 측정하는 방법 은 크게 지문법 (fingerprint)을 이용한 방법과 서열 정렬(sequence alignment)알고리즘을 이용한 방법이 있다. 두 방법은 각각 속도와 정확도라는 장점을 가지고 있다. 다단계정렬(MLA, Multi-Level alignment))는 이러한 두 방법을 조합하여 탐색 속도와 정확도 사이의 비중을 사용자가 결정할 수 있도록 하기 위한 방법이다.[1] 다단계 정렬은 두 문서를 단위 블록(basis block)로 나누고 블록 간의 벡터를 비교하여 유사도를 측정하게 되는데, 본 연구에서는 초성 추출 및 어간 추출을 통해 단위 블록의 벡터를 빠른 시 간에 생성하고 비교하는 방법과 다단계 탐색을 통해 정확도를 유지하면서 빠르게 유사도를 측정하는 방식에 대해 설명한다. 실험결과 제안 방법을 통해 다단계 정렬 방법을 이용한 대용량 문서 비교의 속도가 2 배 이상 빨라짐을 보인다.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.457-463
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• 2011

This paper proposes theorthogonal reference vectors selection method of the subspace interference alignment. The proposed method selects multiple orthogonal reference vectors instead of using one reference vector for all users at the same time. The proposed scheme selects a reference vector which maximizes a sum-rate for a certain cell, generates orthogonal vectors to the previous selected vector and selects the one of orthogonal vectors whose sum rate is maximized for each cell. Larger channel gain and sum-rate than the previous method can be obtained by selection degree of freedom. The computer simulation demonstrates the proposed method gives higher sum-rate compared with that of the previous reference vector selection method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1077-1083
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• 2015

In this paper, we introduce an energy-efficient opportunistic interference alignment (OIA) scheme that greatly improves the sum-rates in multi-cell uplink networks. Each user employs optimal transmit vector design and power control in the sense of minimizing the amount of generated interference to other-cell base stations while satisfying a required signal quality. As our main result, it is shown that owing to the reduced interference level, the proposed OIA schemes attains larger sum-rates than those of OIA with no power control for almost all signal-to-noise ratio regions. In addition, when both zero-forcing and minimum mean square error (MMSE) detectors are employed at the receiver along with the OIA scheme, it is shown that the OIA scheme with MMSE detection shows superior performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6A
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• pp.488-496
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• 2012

In this paper, the scheme for designing optimal beamforming matrix for interference control is proposed. The optimal beamforming matrix is found though linear combination of interference alignment conditions and renewal of linear combination coefficient. The proposed scheme has advantages that the complexity is reduced and there is no multiplying operation in matrix calculations even if proposed scheme has the form similar to that of existing least square based scheme. The simulation results show that proposed scheme has about 4bps/Hz higher gain than existing least square scheme. Also there is no additional multiplying calculation and increase of matrix size when the number of transmit and receive antennas is increased.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.11-19
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• 2021

In this paper, we propose a non-orthogonal multiple access (NOMA) method based on channel alignment to simultaneously transmit multiple unicast and multicast streams in frequency-efficient manner. In this method, all receivers in a multicast cluster use the receive beamforming vectors that align their channels, and the base station uses the aligned channel information to design the transmit beamforming vectors that eliminate interference between multicast clusters. Using the effective receive channel information combined with the transmit beamforming vectors, unicast receivers design their own receive beamforming vectors that eliminate interference between unicast receivers. Since the proposed method effectively eliminates the interference, it achieves a higher sum rate than the existing orthogonal multiple access (OMA) method in high SNR regions. In addition, we present a hybrid method that exploits the benefits of the proposed NOMA method and the existing OMA method. Depending on the channel state, the hybrid method adaptively employs the existing OMA method, which improves the received signal power, in low SNR regions and the proposed NOMA method, which effectively eliminates the interference, in high SNR regions, thereby achieving a good sum rate over the entire SNR region.

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