• 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.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.

• 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.

• 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.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.

• Information and Communications Magazine
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• v.32 no.5
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• pp.56-64
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• 2015

기회적 간섭 정렬 기술은 사용자 간섭 채널에서 탁월한 이론적 성능을 보이는 간섭 정렬 기술을 다수의 사용자와 기지국, 다수의 안테나가 존재하는 다중 셀 다중안테나 셀룰라 네트워크에 적용하는 기술 중 하나로서 각 셀의 사용자가 증가할수록 이론적인 최적 자유도를 달성하는 기술이다. 기회적 간섭 정렬 기법의 장점은 각 셀 상호 정보의 교환 없이 독립적으로 동작하고 각 사용자는 자신과 연관된 채널 정보(local channel state information, local CSI)만을 요구한다는 것이다. 셀의 크기가 감소하고 기지국 및 사용자 단말의 밀도가 크게 증가할 것으로 예상되는 5세대 이동통신 시스템에서는 각 통신링크간 간섭제어가 매우 중요할 것으로 예상된다. 따라서, 본 논문에서는 5세대 이동통신 시스템에서 핵심적인 역할을 할 것으로 예상되는 기회적 간섭 정렬 기법에 관하여 살펴본다.