• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5410-5426
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• 2019

With the advent of the fifth-generation (5G) era, Massive multiple-input multiple-output (MIMO) relay systems have experienced the rapid development. Recently, the performance analysis models of Massive MIMO relay systems have been proposed, which are mostly based on Rayleigh fading channels. In order to create a more suitable model for 5G Internet of Things scenarios, our study is based on the Rician fading channels, where line-of-sight (LOS) path exists in the channels. In this paper, we assume the channel state information (CSI) is perfect. In this case, we use statistical information to derive the analytical exact closed-form expression for the achievable sum rate of the uplink for the Massive MIMO two-hop relay system over Rician fading channels. Moreover, considering the different communication scenarios, we derive the analytical exact closed-form expression for the achievable sum rates of the uplink for other three scenarios. Finally, based on these expressions, we make simulations and analyze the performance under different transmit powers and Rician-factors, which provides a theoretical basis and reference for further research.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.58-67
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• 2020

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3054-3067
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• 2015

The phenomena of higher channel cross polarization discrimination (XPD) is mainly observed for future wireless technologies such as small cell network and massive multiple-input multiple-output (MIMO) system. Therefore, utilization of high XPD is very important and space-polarization division multiple access (SPDMA) with dual-polarized MIMO system could be a suitable solution to high-speed transmission in high XPD environment as well as reduction of array size at base station (BS). By SPDMA with dual-polarized MIMO system, two parallel data signals can be transmitted by both vertically and horizontally polarized antennas to serve different mobile stations (MSs) simultaneously compare to conventional space division multiple access (SDMA) with single-polarized MIMO system. This paper analyzes the performance of SPDMA for maximum ratio transmission (MRT) in time division duplexing (TDD) system by proposed dual-polarized MIMO spatial channel model (SCM) compare to conventional SDMA. Simulation results indicate that how SPDMA utilizes the high XPD as the number of MS increases and SPDMA performs very close to conventional SDMA for same number of antenna elements but half size of the array at BS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.628-649
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• 2017

Security and resource-saving are both demands of the fifth generation (5G) wireless networks. In this paper, we study the secrecy spectrum efficiency (SSE) and secrecy energy efficiency (SEE) of a K-tier massive multiple-input multiple-output (MIMO) enabled heterogeneous cellular network (HetNet), in which artificial noise (AN) are employed for secrecy enhancement. Assuming (i) independent Poisson point process model for the locations of base stations (BSs) of each tier as well as that of eavesdroppers, (ii) zero-forcing precoding at the macrocell BSs (MBSs), and (iii) maximum average received power-based cell selection, the tractable lower bound expressions for SSE and SEE of massive MIMO enabled HetNets are derived. Then, the influences on secrecy oriented spectrum and energy efficiency performance caused by the power allocation for AN, transmit antenna number, number of users served by each MBS, and eavesdropper density are analyzed respectively. Moreover, the analysis accuracy is verified by Monte Carlo simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1330-1350
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• 2022

As a preprocessing operation of transmitter antennas, the hybrid precoding is restricted by the limited computing resources of the transmitter. Therefore, this paper proposes a novel hybrid precoding that guarantees the communication efficiency with low complexity and a fast computational speed. First, the analog and digital precoding matrix is derived from the maximum eigenvectors of the channel matrix in the sub-connected architecture to maximize the communication rate. Second, the extended power iteration (EPI) is utilized to obtain the maximum eigenvalues and their eigenvectors of the channel matrix, which reduces the computational complexity caused by the singular value decomposition (SVD). Third, the Aitken acceleration method is utilized to further improve the convergence rate of the EPI algorithm. Finally, the hybrid precoding based on the EPI method and the Aitken acceleration algorithm is evaluated in millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) systems. The experimental results show that the proposed method can reduce the computational complexity with the high performance in mmWave massive MIMO systems. The method has the wide application prospect in future wireless communication systems.

• ETRI Journal
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• v.42 no.3
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• pp.333-340
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• 2020

We consider a hybrid combiner design for downlink massive multiple-input multiple-output systems when there is residual inter-user interference and each user is equipped with a limited number of radio frequency (RF) chains (less than the number of receive antennas). We propose a hybrid combiner that minimizes the mean-squared error (MSE) between the information symbols and the ones estimated with a constant amplitude constraint on the RF combiner. In the proposed scheme, an iterative alternating optimization method is utilized. At each iteration, one of the analog RF and digital baseband combining matrices is updated to minimize the MSE by fixing the other matrix without considering the constant amplitude constraint. Then, the other matrix is updated by changing the roles of the two matrices. Each element in the RF combining matrix is obtained from the phase component of the solution matrix of the optimization problem for the RF combining matrix. Simulation results show that the proposed scheme performs better than conventional matrix-decomposition schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.794-815
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• 2023

The rapid development of mobile communication not only has made the industry gradually diversified, but also has enhanced the service quality requirements of users. In this regard, it is imperative to consider jointly network slicing and mobile edge computing. The former mainly ensures the requirements of varied vertical services preferably, and the latter solves the conflict between the user's own energy and harsh latency. At present, the integration of the two faces many challenges and need to carry out at different levels. The main target of the paper is to minimize the energy consumption of the system, and introduce a multi-slice joint task offloading and resource allocation scheme for massive multiple input multiple output enabled heterogeneous networks. The problem is formulated by collaborative optimizing offloading ratios, user association, transmission power and resource slicing, while being limited by the dissimilar latency and rate of multi-slice. To solve it, assign the optimal problem to two sub-problems of offloading decision and resource allocation, then solve them separately by exploiting the alternative optimization technique and Karush-Kuhn-Tucker conditions. Finally, a novel slices task offloading and resource allocation algorithm is proposed to get the offloading and resource allocation strategies. Numerous simulation results manifest that the proposed scheme has certain feasibility and effectiveness, and its performance is better than the other baseline scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.261-279
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• 2023

In this article, the uplink achievable rate is investigated for massive multiple-input multiple-output (MIMO) under correlated Ricean fading channel, where each base station (BS) and user are both deployed multiple antennas. Considering the availability of prior knowledge at BS, two different channel estimation approaches are adopted with and without prior knowledge. Based on these channel estimations, a two-layer decoding scheme is adopted with maximum ratio precoding as the first layer decoder and optimal second layer precoding in the second layer. Based on two aforementioned channel estimations and two-layer decoding scheme, the exact closed form expressions for uplink achievable rates are computed with and without prior knowledge, respectively. These derived expressions enable us to analyze the impacts of line-of-sight (LoS) component, two-layer decoding, data transmit power, pilot contamination, and spatially correlated Ricean fading. Then, numerical results illustrate that the system with spatially correlated Ricean fading channel is superior in terms of uplink achievable rate. Besides, it reveals that compared with the single-layer decoding, the two-layer decoding scheme can significantly improve the uplink achievable rate performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.2047-2066
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• 2024

In order to improve the coverage and achieve massive spectrum access, non-orthogonal multiple access (NOMA) technology is applied in millimeter wave massive multiple-input multiple-output (mMIMO) communication network. However, the power assumption of active sensors greatly limits its wide applications. Recently, Intelligent Reconfigurable Surface (IRS) technology has received wide attention due to its ability to reduce power consumption and achieve passive transmission. In this paper, spectral efficiency maximum problem in the millimeter wave mMIMO-NOMA system with IRS is considered. The sparse RF chain antenna structure is designed at the base station based on continuous phase modulation. Furthermore, a joint optimization problem for power allocation, power splitting, analog precoding and IRS reconfigurable matrices are constructed, which aim to achieve the maximum spectral efficiency of the system under the constraints of user's quality of service, minimum energy harvesting and total transmit power. A three-stage iterative algorithm is proposed to solve the above mentioned non-convex optimization problems. We obtain the local optimal solution by fixing some optimization parameters firstly, then introduce the relaxation variables to realize the global optimal solution. Simulation results show that the spectral efficiency of the proposed scheme is superior compared to the conventional system with phase shifter modulation. It is also demonstrated that IRS can effectively assist mmWave communication and improve the system spectral efficiency.

• ETRI Journal
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• v.45 no.6
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• pp.952-962
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• 2023

In this study, a space-time transmission scheme is proposed to tackle the limitations of channel estimation with orthogonal pilot information in colocated multiple-input multiple-output systems with several transmitting and receiving antennas. Channel information is obtained using orthogonal pilots. Channel estimation introduces pilot heads required to estimate a channel. This leads to bandwidth insufficiency. As a result, trade-offs exist between the number of pilots required to estimate a channel versus spectral efficiency. The detection of data symbols is performed using the maximum likelihood decoding method as it provides a consistent approach to parameter estimation problems. The moment-generating function of the instantaneous signal-to-noise ratio is used to drive an approximate expression of the symbol error rate for the proposed scheme. Furthermore, the order of diversity is less by one than the number of receiver antennas used in the proposed scheme. The effect of the length of a pilot sequence on the proposed scheme's performance is also investigated.