• ETRI Journal
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• 제40권5호
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• pp.582-591
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• 2018

In this paper, we present a performance analysis of large-scale multi-input multi-output (MIMO) systems for wireless backhaul networks. We focus on fully connected N nodes in a wireless meshed and multi-hop network topology. We also consider a large number of antennas at both the receiver and transmitter. We investigate the transmission schemes to support fully connected N nodes for half-duplex and full-duplex transmission, analyze the achievable ergodic sum rate among N nodes, and propose a closed-form expression of the achievable ergodic sum rate for each scheme. Furthermore, we present numerical evaluation results and compare the resuts with closed-form expressions.

• ETRI Journal
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• 제41권3호
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• pp.298-307
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• 2019

This paper investigates the use of the inverse-free sparse Bayesian learning (SBL) approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM)-based multiuser massive multiple-input multiple-output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought-after low-PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation-maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E-step) is averted by invoking a relaxed evidence lower bound (relaxed-ELBO). The resulting inverse-free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.

• International Journal of Computer Science & Network Security
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• 제23권2호
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• pp.140-146
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• 2023

With the rapid growth of new users and massive need for very high data rate in 5G communications system, different technologies have been developed and applied to enhance communication efficiency. One of those technologies is the MISO, MISO and MIMO which transmits and receives information with more reliability by using multiple antennas on transmitter or/and receiver side. This paper presents the latest trends in 5G telecommunications system based on software defined radio, A novel low-cost SIMO, MISO and MIMO using flexibility between USRP and Simulink is implemented tested and validated.

• 전자공학회지
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• 제42권10호
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• pp.16-34
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• 2015

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권1호
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• pp.108-122
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• 2014

Massive antenna array is an attractive candidate technique for future broadband wireless communications to acquire high spectrum and energy efficiency. However, such benefits can be realized only when proper channel information is available at the transmitter. Since the amount of the channel information required by the transmitter is large for massive antennas, the feedback is burdensome in practice, especially for frequency division duplex (FDD) systems, and needs normally to be reduced. In this paper a novel channel feedback reduction scheme based on the theory of distributed compressive sensing (DCS) is proposed to apply to massive antenna arrays with spatial correlation, which brings substantially reduced feedback load. Simulation results prove that the novel scheme is better than the channel feedback technique based on traditional compressive sensing (CS) in the aspects of mean square error (MSE), cumulative distributed function (CDF) performance and feedback resources saving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1504-1526
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• 2018

For uplink multi-user massive MIMO systems, conventional minimum mean square error (MMSE) linear detection method achieves near-optimal performance when the number of antennas at base station is much larger than that of the single-antenna users. However, MMSE detection involves complicated matrix inversion, thus making it cumbersome to be implemented cost-effectively and rapidly. In this paper, we first summarize in detail the state-of-the-art simplified MMSE detection algorithms that circumvent the complicated matrix inversion and hence reduce the computation complexity from ${\mathcal{O}}(K^3)$ to ${\mathcal{O}}(K^2)$ or ${\mathcal{O}}(NK)$ with some certain performance sacrifice. Meanwhile, we divide the simplified algorithms into two categories, namely the matrix inversion approximation and the classical iterative linear equation solving methods, and make comparisons between them in terms of detection performance and computation complexity. In order to further optimize the detection performance of the existing detection algorithms, we propose more proper solutions to set the initial values and relaxation parameters, and present a new way of reconstructing the exact effective noise variance to accelerate the convergence speed. Analysis and simulation results verify that with the help of proper initial values and parameters, the simplified matrix inversion based detection algorithms can achieve detection performance quite close to that of the ideal matrix inversion based MMSE algorithm with only a small number of series expansions or iterations.

• 한국인터넷방송통신학회논문지
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• 제18권2호
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• pp.89-103
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• 2018

천재수학자 가우스와 통신 공학자 샤논은 창의적인 아이디어 모티베이션(motivation)을 어디에서 가져왔을까. 정답은 동전 한 잎이다. 가우스는 1부터 100까지 합을 구하는 문제에서 창의적인 아이디어를 찾았다. 이것은 동전 한 잎을 던졌을 때 나올 확률 값 분포 곡선과 같다. 샤논은 가우스 확률 분포를 확장하여 엔트로피(Entropy)를 정의했는데, Source 심볼과 그 역수(Reciprocal) 대수를 취하여 가중평균을 구했다. 가우스와 샤논은 똑같이 <동전 한 잎>에서 만났다. 본고에서는 이점에 착안, 가우스 분포와 샤논 엔트로피를 쉽게 증명한다. 그 응용예로 제주 정낭 채널 용량과 천이확률을 구했는데, 동등한 천이확률이 1/2 일때 샤논 채널 용량은 1이됨을 밝혔다.

• 한국통신학회논문지
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• 제40권4호
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• pp.628-636
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• 2015

본 논문은 거대 다중 안테나 시스템을 위한 넌컨백스 압축센싱 기반 채널 상태 정보 피드백 기법을 제안한다. 제안하는 피드백 기법은 랜덤 백터 양자화 방식과 결합하여, 피드백 양을 줄이면서 송신단에서 정확한 채널 정보를 획득할 수 있게 해준다. 또한, 측정값이 부정확하고 불완전하더라도 기존의 컨백스 압축센싱 기반 채널 상태정보 피드백 기법보다 더 적은 수의 측정값만으로 채널 상태 정보를 복구할 수 있다. 실험을 통해 제안하는 넌컨백스 압축센싱 기반 피드백 기법이 기존의 압축센싱 기반 피드백 기법과 랜덤 백터 양자화 피드백 기법에 비해 같은 피드백 양으로 더 높은 전송률을 제공함을 확인하였다.

• 한국인터넷방송통신학회논문지
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• 제20권5호
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• pp.57-62
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• 2020

30GHz~300GHz 대역의 스펙트럼을 사용하는 밀리미터파는 높은 주파수로 인해 파장이 짧아서 기지국에 더 많은 안테나를 장착할 수 있어 Massive MIMO 시스템에 적합하다. 하지만 안테나 당 RF chain이 요구되기 때문에 안테나의 수가 증가되면 하드웨어 비용 및 전력 소비가 증가하는 문제점을 갖는다. 따라서 본 논문에서는 이러한 문제점을 해결하기 위해 안테나 선택 기법을 조사한다. 기존 철저한 조사 기반 안테나 선택 기법에서 높은 계산 복잡도를 가지는 문제를 해결하기 위해 딥러닝 기술을 적용하는 방안을 제안한다. 멀티 클래스를 분류할 수 있는 DNN 모델을 사용하여 최적의 안테나 조합을 예측한다. 시뮬레이션을 통해 기존 안테나 선택 기법들과 제안하는 딥러닝 기반 안테나 선택 기법을 비교하여 평가한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2357-2380
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• 2019

In this paper, we investigate the impact of hardware impairments (HWIs) on the performance of a downlink massive MIMO system. We consider a single-cell system with maximum ratio transmission (MRT) as precoding scheme, and with all the HWIs characteristics such as phase noise, distortion noise, and amplified thermal noise. Based on the system model, we derive closed-form expressions for a typical user data rate under two scenarios: when a common local oscillator (CLO) is used at the base station and when separated oscillators (SLOs) are used. We also derive closed-form expressions for the downlink transmit power required for some desired per-user data rate under each scenario. Compared to the conventional system with ideal transceiver hardware, our results show that impairments of hardware make a finite upper limit on the user's downlink channel capacity; and as the number of base station antennas grows large, it is only the hardware impairments at the users that mainly limit the capacity. Our results also show that SLOs configuration provides higher data rate than CLO at the price of higher power consumption. An approach to minimize the effect of the hardware impairments on the system performance is also proposed in the paper. In our approach, we show that by reducing the cell size, the effect of accumulated phase noise during channel estimation time is minimized and hence the user capacity is increased, and the downlink transmit power is decreased.