• 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.

• 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.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.688-698
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• 2016

In this paper, we investigate the achievable sum rate and energy efficiency of downlink massive multiple-input multiple-output antenna systems with zero-forcing precoding, by taking into account the randomness of user locations. Specifically, we propose two types of non-uniform user distributions, namely, center-intensive user distribution and edge-intensive user distribution. Based on these user distributions, we derive novel tight lower and upper bounds on the average sum rate. In addition, the impact of user distributions on the optimal number of users maximizing the sum rate is characterized. Moreover, by adopting a realistic power consumption model which accounts for the transmit power, circuit power and signal processing power, the energy efficiency of the system is studied. In particular, closed-form solutions for the key system parameters, such as the number of antennas and the optimal transmit signal-to-noise ratio maximizing the energy efficiency, are obtained. The findings of the paper suggest that user distribution has a significant impact on the system performance: for instance, the highest average sum rate is achieved with the center-intensive user distribution, while the lowest average sum rate is obtained with the edge-intensive user distribution. Also, more users can be served with the center-intensive user distribution.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.3
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• pp.280-285
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• 2015

We consider a linear transceiver design method for multi-user multiple-input multiple-output (MIMO) downlink channels where a base station (BS) equipped with a finitely large number of antennas. Although a matched-filter precoder is a capacity-achieving method in massive MIMO downlink systems, it cannot guarantee to achieve the multi-user MIMO capacity in a finitely large number of antennas due to inter-user interferences. In this paper, we propose a two-stage precoder design method that maximizes the sum-rate of cell-edge users when the BS equipped with a finitely large number of antennas. At the first stage, a matched-filter precoder is adopted to exploit both beamforming gain and the reduction of the dimension of effective channels. Then, we derive the second stage precoder that maximizes the sum-rate by minimizing the weighted mean square error (WMSE). From simulation and analysis, we verify the effectiveness of the proposed method.

• 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.12 no.3
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• pp.1098-1112
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• 2018

Achievable sum rate and energy efficiency (EE) are investigated for the massive multiple-input multiple-output (Massive MIMO) downlink with channel aging in the time varying Ricean fading channel. Specifically, the expression of the achievable sum rate of the system for the maximum ratio transmission (MRT) precoder with aged channel state information (CSI) in the time varying Ricean fading channel is first presented. Based on the expression, the effect of both channel aging and the Ricean factor on the power scaling law are studied. It is found that the transmit power of base station (BS) is scaled down by $1/{\sqrt{M}}$(where M is the number of the BS antennas) when the Ricean factor K is equal to zero (i.e., time varying Rayleigh fading channel), indicating that aged CSI does not affect the power scaling law. However, the transmit power of the BS is scaled down by 1/M for the time varying Ricean fading channel (where $K{\neq}0$) indicating that the Ricean factor affects the power scaling law and sum rate, and channel aging only leads to a reduction of the sum rate. Second, the EE of the system is analyzed based on the general power consumption model. Both the theoretical analysis and the simulations show that the channel aging could degrade the sum rate and the EE of the system, and it does not affect the power scaling law.

• 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.

• 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.