• ETRI Journal
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• v.40 no.5
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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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• v.41 no.3
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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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• v.23 no.2
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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.

• The Magazine of the IEIE
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• v.42 no.10
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• pp.16-34
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• 2015

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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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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• v.12 no.4
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.89-103
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• 2018

A genius "Prince of Mathematician" Gaussian and "Father of Communication" Shannon comes up with the creative idea of motivation to meet each other? The answer is a coin leaf. Gaussian found some creative ideas in the matter of obtaining a sum of 1 to 100. This is the same as the probability distribution curve when a coin leaf is thrown. Shannon extended the Gaussian probability distribution to define the entropy, taking the source symbol and the reciprocal logarithm to obtain the weighted average. These where the genius Gaussian and Shannon meet in the same coin leaf. This paper focuses on this point, and easily proves Gaussian distribution and Shannon entropy. As an application example, we have obtained the capacity and transition probability of Jeongju seminal vesicle, and the Shannon channel capacity is 1 when the equivalent transition probability is 1/2.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.628-636
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• 2015

In this paper, we propose a non-convex compressed sensing(NCCS)-based channel state information(CSI) feedback scheme for massive multiple-input multiple-output(MIMO) systems. Combining the random vector quantization(RVQ), the proposed scheme permits a transmitter to obtain CSI with acceptable accuracy under substantially reduced feedback load. Furthermore, it recovers CSI from fewer measurements than that of existing convex compressed sensing(CCS)-based schemes even if the measurements are inaccurate and incomplete. Simulation results show that the proposed scheme achieves higher throughput than both existing CCS-based feedback scheme and random vector quantization(RVQ) feedback scheme with the same feedback load.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.57-62
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• 2020

The millimeter wave that uses the spectrum in the 30GHz~300GHz band has a shorter wavelength due to its high carrier frequency, so it is suitable for Massive MIMO systems because more antennas can be equipped in the base station. However, since an RF chain is required per antenna, hardware cost and power consumption increase as the number of antennas increases. Therefore, in this paper, we investigate antenna selection schemes to solve this problem. In order to solve the problem of high computational complexity in the exhaustive search based antenna selection scheme, we propose a approach of applying deep learning technology. An best antenna combination is predicted using a DNN model capable of classifying multi-classes. By simulation tests, we compare and evaluate the existing antenna selection schemes and the proposed deep learning-based antenna selection scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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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.