• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.57-60
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• 2015

Recently, a multi-user massive MIMO (MU-Massive MIMO) network has been attracting tremendous interest as one of technologies to accommodate explosively increasing mobile data traffic. The MU-Massive MIMO network can significantly enhance the network capacity because a base station (BS) equipped with large-scale transmit antennas can transmit high-rate data to multiple users simultaneously. In the MU-Massive MIMO network, transmit antenna selection schemes are generally used to decrease the computational complexity and cost of the BS. In this paper, we investigate the energy efficiency of the transmit antenna selection scheme in the MU-Massive MIMO network and the optimal number of selected transmit antennas for maximizing the energy efficiency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.77-82
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• 2015

Recently, multi-user massive MIMO (MU-Massive MIMO) network has attracted a lot of attention as a technology to accommodate explosively increasing mobile data traffic. However, the MU-Massive MIMO network causes a tremendous hardware complexity in a base station and computational complexity to select optimal set of users. In this paper, we thus propose a simple algorithm for selecting antennas and users while reducing the hardware and computational complexities simultaneously. The proposed scheme has a computational complexity of $O((N-S_a+1){\times}min(S_a,K))$, which is significantly reduced compared to the complexity of optimal scheme based on Brute-Force searching, $$O\left({_N}C_S_a\sum_{i=1}^{min(S_a,K)}_KC_i\right)$$, where N, $S_a$, and K denote the number of total transmit antennas, the number of selected antennas, and the number of all users, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1597-1601
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• 2013

Multiple antenna technique is attracting attention as a core technology for next-generation mobile communication systems to accommodate explosively increasing mobile data traffic. Especially, recent researches focus on multi-user multiple input multiple output (MU-MIMO) system where base stations are equipped with several tens of transmit antennas and transmit data to multiple terminals (users) simultaneously. To enhance the performance of MU-MIMO systems, we, in this paper, propose an adaptive user selection algorithm which adaptively selects a user set according to varying channel states. According to Monte-Carlo based computer simulations, the performance of proposed scheme is significantly improved compared to the conventional scheme without user selection and approaches that of exhaustive search-based optimal scheme. On the other hand, the proposed scheme can reduce the computational complexity to $K/(2^K-1)$ compared to the optimal scheme where K denotes the number of total users.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1063-1070
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• 2015

In this paper, we propose a distributed user scheduling with transmit power control based on the amount of generating interference to other base stations (BSs) in multi-cell multi-input multi-output (MIMO) networks. Assuming that the time-division duplexing (TDD) system is used, the interference channel from users to other cell BSs is obtained at each user. In the proposed scheduling, each user first generates a transmit beamforming vector by using singular value decompositon (SVD) over MIMO channels and reduces the transmit power if its generating interference to other BSs is larger than a predetermined threshold. Each BS selects the user with the largest effective channel gains among users, which reflects the adjusted power of users. Simulation results show that the proposed technique significantly outperforms the existing user scheduling algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1367-1373
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• 2022

In this paper, we consider a power re-allocation technique in order to enhance the frequency efficiency of the low performance user in a cell-free multiple input multiple output (MIMO) network. The AP first allocates transmit power to the user to be proportional to the large-scale fading coefficients of the connected users. Then, the AP reduces the power of the users who were allocated power greater than the threshold ratio of total allocated power to be equal to the threshold ratio of the allocated power. Finally, the AP re-allocates the reduced power from the strong channel user to the user who has the worst channel condition, and thus, the frequency efficiency of the low performance user can be enhanced. In the simulation results, we verify the performance of the power re-allocation technique in terms of the spectral efficiency of the low performance user.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.114-121
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• 2022

In this paper, we consider a user centric clustering in order to guarantee the performance of the users in cell free multiple-input multiple-output (MIMO) network. In the user centric clustering scheme, by using large scale fading coefficients of the connected access points (APs), each user decides own cluster with the APs having the higher the large scale fading coefficients than threshold value compared to the highest large scale fading coefficient. In the determined user centric clusters, the APs design the beamformers and power allocations in the distributed manner and the APs cooperatively transmit data to users by using beamformers and power allocations. In the simulation results, we verify the performance of user centric clustering in terms of the spectral efficiency and we also find the optimal threshold value in the given configuration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.975-980
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• 2019

In this paper, we propose a deep learning-based scheduling scheme for user selection in multi-user multi-antenna networks which is considered one of key technologies for the next generation mobile communication systems. We obtained 90,000 data samples from the conventional optimal scheme to train the proposed neural network and verified the trained neural network to check if the trained neural network is over-fitted. Although the proposed neural network-based scheduling algorithm requires considerable complexity and time for training in the initial stage, it does not cause any extra complexity once it has been trained successfully. On the other hand, the conventional optimal scheme continuously requires the same complexity of computations for every scheduling. According to extensive computer-simulations, the proposed deep learning-based scheduling algorithm yields about 88~96% average sum-rates of the conventional scheme for SNRs lower than 10dB, while it can achieve optimal average sum-rates for SNRs higher than 10dB.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1559-1565
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• 2021

In this paper, an antenna selection scheme is proposed in massive multi-user multiple input multiple output (MU-MIMO) systems. The proposed antenna selection scheme can achieve almost the same performance as a conventional scheme while significantly reducing the overhead of feedback by using deep reinforcement learning (DRL). Each user compares the channel gains of massive antennas in base station (BS) to the L-largest channel gain, converts them to one-bit binary numbers, and feed them back to BS. Thus, the feedback overhead can be significantly reduced. In the proposed scheme, DRL is adopted to prevent the performance loss that might be caused by the reduced feedback information. We carried out extensive Monte-Carlo simulations to analyze the performance of the proposed scheme and it was shown that the proposed scheme can achieve almost the same average sum-rates as a conventional scheme that is almost optimal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.10
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• pp.921-928
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• 2012

In this paper, we propose a novel interference alignment transceiver for multi-cell MIMO downlink channels with arbitrary number of cells and users per cell. We design the receive beamformer to align the interference from undesired base stations to the effective inter-cell interference (ICI) channels. Subsequently, we design the transmit precoder which can nulllify the interference from the corresponding base station. The proposed transceiver design can attain the degrees of freedom (DOF) equal to the number of streams per user. Accordingly, we investigate conditions for the antenna configuration. From numerical results, we confirm that the proposed transceiver design can achieve higher DOF than the conventional scheme under equal antenna configuration.

• Information and Communications Magazine
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• v.24 no.5
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• pp.98-106
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• 2007

전송다이버시티 는 일반적으로 송신기에서 하나이상의 안테나를 필요로 한다. 그러나 많은 무선기기들은 크기나 하드웨어의 복잡도로 인해 다수의 안테나를 사용하는데 많은 제약을 가지고 있었다. 최근 들어 해외에서 활발히 연구되고 있는 협력통신이라는 전송방식은 단일 안테나를 사용하는 다수의 사용자가 있는 환경에서, 사용자간의 안테나를 공유하여 가상의 다중 안테나를 가진 송, 수신기의 성능을 제공하여 높은 전송 다이버시티를 획득 할 수 있는 기술이다. 이와 관련된 연구로 저전력 무선 센서 네트워크를 위한 순환부호를 이용한 부호화 협력통신을 소개한다.