• Journal of Communications and Networks
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• v.18 no.4
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• pp.649-657
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• 2016

In this paper, we investigate energy efficiency (EE) of the traditional co-located and the distributed massive multiple-input multiple-output (MIMO) systems. First, we derive an approximate EE expression for both the idealistic and the realistic power consumption models. Then an optimal energy-efficient remote access unit (RAU) selection algorithm based on the distance between the mobile stations (MSs) and the RAUs are developed to maximize the EE for the downlink distributed massive MIMO systems under the realistic power consumption model. Numerical results show that the EE of the distributed massive MIMO systems is larger than the co-located massive MIMO systems under both the idealistic and realistic power consumption models, and the optimal EE can be obtained by the developed energy-efficient RAU selection algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1485-1491
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• 2015

This paper introduces a new pilot assignment algorithm for uplink Massive multiple-input multiple-output (MIMO) systems. Since the conventional pilot assignment algorithm has the performance degradation compared to the optimal algorithm which performs the exhaustive search, we propose a new pilot assignment algorithm using Pre-determined Interference and Pre-determined Desired-term techniques. The proposed algorithm has the low complexity and guarantees negligible performance loss compared to the optimal algorithm. Simulation result verifies that the proposed algorithm achieves a large performance gain over the conventional algorithm.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.352-361
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• 2013

Combining multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) with a massive number of transmit antennas (massive MIMO-OFDM) is an attractive way of increasing the spectrum efficiency or reducing the transmission energy per bit. The effectiveness of Massive MIMO-OFDM is strongly affected by the channel state information (CSI) estimation method used. The overheads of training frame transmission and CSI feedback decrease multiple access channel (MAC) efficiency and increase the CSI estimation cost at a user station (STA). This paper proposes a CSI estimation scheme that reduces the training frame length by using a novel pilot design and a novel unitary matrix feedback method. The proposed pilot design and unitary matrix feedback enable the access point (AP) to estimate the CSI of the signal space of all transmit antennas using a small number of training frames. Simulations in an IEEE 802.11n channel verify the attractive transmission performance of the proposed methods.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.338-351
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• 2013

Multiple-input multiple-output (MIMO) communication may provide high spectral efficiency through the deployment of a very large number of antenna elements at the base stations. The gains from massive MIMO communication come from the use of multi-user MIMO on the uplink and downlink, but with a large excess of antennas at the base station compared to the number of served users. Initial work on massive MIMO did not fully address several practical issues associated with its deployment. This paper considers the impact of channel aging on the performance of massive MIMO systems. The effects of channel variation are characterized as a function of different system parameters assuming a simple model for the channel time variations at the transmitter. Channel prediction is proposed to overcome channel aging effects. The analytical results on aging show how capacity is lost due to time variation in the channel. Numerical results in a multicell network show that massive MIMO works even with some channel variation and that channel prediction could partially overcome channel aging effects.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.19-24
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• 2015

We introduce an optimization of the number of base station antennas in massive multiple-input multiple-output (MIMO) wireless powered communication network (WPCN). We use channel hardening property of massive MIMO system to approximate channel gain in terms of the number of base station antennas. Then, we find an optimal solution by partial differential and obtain a closed form solution by using Lambert-W function. The simulation results show that the approximation and the method of solving the optimization problem are reasonable, and the optimal solution of proposed scheme is almost identical to the optimal number of base station antennas by the exhaustive search method.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.398-406
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• 2013

In this paper, we investigate two types of in-phase and quadrature-phase (IQ) data transfer methods for cloud multiple-input multiple-output (MIMO) network operation. They are termed "after-precoding" and "before-precoding". We formulate a cloud massive MIMO operation problem that aims at selecting the best IQ data transfer method and transmission strategy (beamforming technique, the number of concurrently receiving users, the number of used antennas for transmission) to maximize the ergodic sum-rate under a limited capacity of the digital unit-radio unit link. Based on our proposed solution, the optimal numbers of users and antennas are simultaneously chosen. Numerical results confirm that the sum-rate gain is greater when adaptive "after/before-precoding" method is available than when only conventional "after-precoding" IQ-data transfer is available.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.1
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• pp.32-35
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• 2018

Massive multiple-input multiple-output (MIMO) transmission is an essential technique for achieving the high bandwidth efficiency required in 5G mobile communication systems. Various forms of arrays can be used as the number of antenna elements increases for massive MIMO transmission. In this letter, we propose a beamforming algorithm applicable to multiuser MIMO transmission using uniform circular arrays. By employing quasi-orthogonal beam pairs obtained from the inter-beam correlation information, we minimize inter-user interference and evaluate the resulting performance gain.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.5
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• pp.520-530
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• 2016

Since FDD massive MIMO (multiple-input multiple-output) system deploys hundreds of transmit antennas at base station (BS) compared to conventional MIMO system, the overhead of transmitting channel state information reference signal (CSI-RS) increases proportionally to the number of transmit-antennas. To overcome these disadvantages, we proposed beamforming based CSI-RS transmission technique for FDD massive MIMO system which transmit CSI-RS by limited amount of downlink resources.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.8
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• pp.724-732
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• 2013

In this paper, we analyze a performance of multiuser massive multiple-input and multiple-output (MIMO) system. We derive the ergodic cell capacity based on a downlink frame structure and analyze the ergodic cell capacity with respect to the number of base station (BS) antennas and the number of users. This paper shows that the ergodic cell capacity is a concave function with respect to the number of BS antennas and the number of users, and also derives the optimal numbers of BS antennas and users for the maximum cell capacity. The simulation results verify the derived analyses and show that the derived numbers of BS antennas and users provide the maximum cell capacity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1239-1247
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• 2015

In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system, the computational complexity of downlink channel estimation is proportional to the number of antennas at a base station. Therefore, effective channel estimation techniques may have to be studied. In this paper, novel channel estimation algorithms using adaptive techniques such as Kalman and least mean square (LMS) filters are proposed in a channel model with temporal and spatial correlation.