• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.861-869
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• 2014

This paper shows the research of a platform architecture relates to the LTE-based macro basestation; the proposed platform architecture is designed with the interface between the baseband signal and IF (Intermediate Frequency) per codeword. Using this method, we can smoothly transmit/receive a large amounts of data regardless of the number of antenna in a macro base station which is used technology such as massive MIMO. In this paper, We analyzed the evolution of LTE technology and the trend in the development of the LTE-based system. For validation of the proposed architecture, we compare the general architecture of a conventional with the proposed architecture. From the calculation results of transmission quantity data, we see that the proposed architecture can give better performance than the existing architecture. By presenting this architecture, we hope to provide a new foundation for Design and Implementation of a LTE base station platform which is used technology such as massive MIMO, carrier aggregation (CA), coordinated multi point (CoMP).

• Journal of Communications and Networks
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• v.18 no.1
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• pp.95-104
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• 2016

Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. To compress CSI, prior work has applied compressive sensing (CS) techniques and the fact that CSI can be sparsified. The adopted sparsifying bases fail, however, to reflect the spatial correlation and channel conditions or to be feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel, and needs no change as long as the spatial correlation model does not change. We propose a new reconstruction algorithm for CS, and also suggest dimensionality reduction as a compression method. To feed back compressed CSI in practice, we propose a new codebook for the compressed channel quantization assuming no other-cell interference. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single-user) and point-to-multi-point (multi-user) scenarios.

• 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 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1042-1058
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• 2024

Terahertz (THz) communication is becoming a key technology for future 6G wireless networks because of its ultra-wide band. However, the implementation of THz communication systems confronts formidable challenges, notably beam splitting effects and high computational complexity associated with them. Our primary objective is to design a hybrid precoder that minimizes the Euclidean distance from the fully digital precoder. The analog precoding part adopts the delay-phase alternating minimization (DP-AltMin) algorithm, which divides the analog precoder into phase shifters and time delayers. This effectively addresses the beam splitting effects within THz communication by incorporating time delays. The traditional digital precoding solution, however, needs matrix inversion in THz massive multiple-input multiple-output (MIMO) communication systems, resulting in significant computational complexity and complicating the design of the analog precoder. To address this issue, we exploit the characteristics of THz massive MIMO communication systems and construct the digital precoder as a product of scale factors and semi-unitary matrices. We utilize Schatten norm and Hölder's inequality to create semi-unitary matrices after initializing the scale factors depending on the power allocation. Finally, the analog precoder and digital precoder are alternately optimized to obtain the ultimate hybrid precoding scheme. Extensive numerical simulations have demonstrated that our proposed algorithm outperforms existing methods in mitigating the beam splitting issue, improving system performance, and exhibiting lower complexity. Furthermore, our approach exhibits a more favorable alignment with practical application requirements, underlying its practicality and efficiency.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.49-55
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• 2022

In the next generation mobile communication systems, hybrid massive multiple-input multiple output (MIMO) can be used to highly improve an achievable rate without increasing the number of RF chains. Recently, successive-interference-cancellation (SIC) based hybrid precoder design scheme was proposed to reduce the complexity. However, since this scheme uses simple diagonal matrix for baseband precoding, it cannot solve an interference problem between the transmitted streams. Also, there is a limitation for improving the data rate because of the use of one phase shifter for analog precoding. To solve these problems, in this paper we propose a digital precoding based on the SVD of the effective channel and an analog precoding using two phase shifters. Through simulation, we show that the proposed scheme has better achievable rate and SINR performances than the conventional one.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1083-1096
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• 2021

Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value u_op by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and u_op. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

• 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.

• 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.