For optical wireless communication systems with restricted bandwidth, combining multiple-input multiple-output (MIMO) transmission with orthogonal frequency division multiplexing (OFDM) modulation was found to be a hi...
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For optical wireless communication systems with restricted bandwidth, combining multiple-input multiple-output (MIMO) transmission with orthogonal frequency division multiplexing (OFDM) modulation was found to be a highly effective method of boosting system capacity. This paper presents and analyzes two optical OFDM-based MIMO schemes for visible light communications, namely OFDM-based time domain general spatial modulation (TD-GSM) and OFDM-based time domain general spatial multiplexing (TD-GSMP), which utilize general-numbered MIMO configurations of light-emitting diodes (LEDs). These schemes exhibit reduced complexity and provide enhanced spectral efficiency (SE) in comparison to conventional frequency-domain (FD) GSM and GSMP schemes, achieved by transmitting twice the spatial information. Previous research in this area typically employed an even number of LEDs for spatial-based MIMO transmission, represented as 2(la), where l(a) is the number of spatial bits. However, to enhance SE, this paper introduces schemes that can accommodate a general number of LEDs without the constraint of an even-numbered LED configuration. In indoor environments, several LEDs are already used for illumination purposes, so incorporating additional LEDs for communication does not introduce complexity to the system. To detect both spatial and radiated information, a maximum-likelihood estimation algorithm is proposed. Simulation results demonstrate that, depending on the user's position, selecting the optimal MIMO configuration is crucial for achieving desired bit error rate (BER) performance and SE requirements. Moreover, as the total number of LEDs increases relative to the active number of LEDs, the modulation order required to transmit signals decreases in order to support a specified SE, resulting in improved BER performance. When all LEDs are active during communication, TD-GSMP outperforms TD-GSM in terms of BER performance. The results also show the superiority of TD schemes ov
Unmanned aerial vehicles (UAVs) and millimeter-wave frequencies play key roles in supporting 5G wireless communication systems. They expand the field of wireless communication by increasing the data capacities of comm...
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Unmanned aerial vehicles (UAVs) and millimeter-wave frequencies play key roles in supporting 5G wireless communication systems. They expand the field of wireless communication by increasing the data capacities of communication systems and supporting high data rates. However, short wavelengths, owing to the high millimeter-wave frequencies can cause problems, such as signal attenuation and path loss. To address these limitations, research on high directional beamforming technologies continue to garner interest. Furthermore, owing to the mobility of the UAVs, it is essential to track the beam angle accurately to obtain full beamforming gain. This study presents a beam tracking method based on the unscented Kalman filter using hybrid beamforming. The simulation results reveal that the proposed beam tracking scheme improves the overall performance in terms of the mean -squared error and spectral efficiency. In addition, by expanding analog beamforming to hybrid beamforming, the proposed algorithm can be used even in multi-user and multi-stream environments to increase data capacity, thereby increasing utilization in new-radio multiple -inputmultiple-output orthogonal frequency-division multiplexing systems.
As research progresses, an increasing number of deep neural networks (DNNs) are applied to the multiple-input multiple-output (MIMO) detectors. This letter presents a simplified realization of the multi-layer perceptr...
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As research progresses, an increasing number of deep neural networks (DNNs) are applied to the multiple-input multiple-output (MIMO) detectors. This letter presents a simplified realization of the multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs) in data-driven MIMO detectors, which are implemented in the ChannelNet detector. ChannelNet is a data-driven MIMO detector exclusively crafted from readily available DNN components, such as MLPs and CNNs. The implementation of the MLP and CNN can be converted to matrix multiplication and replaced by a series of simple operations involving comparisons, querying the lookup tables, and integer additions. By setting a target loss in advance, a sequentially iterative simplification of ChannelNet is achieved. Numerical results demonstrate that ChannelNet has superior performance, and the simplified ChannelNet reduces at least half of the computational complexity with less than 0.2 dB performance loss at the expense of acceptable storage space.
In this letter, we study secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multiple-input multiple-output cognitive radio network, where a secon...
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In this letter, we study secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multiple-input multiple-output cognitive radio network, where a secondary transmitter (ST) sends confidential information to a secondary user while sharing the same frequency band with a primary user in the presence of an eavesdropper. We aim to maximize the secrecy rate of the secondary system by jointly optimizing the transmit precoding matrices at the ST, along with the transmission and reflection coefficients of the STAR-RIS. An efficient alternating optimization algorithm based on the penalty method and the generalized S-procedure is proposed to solve the formulated non-convex problem. Simulation results show that the proposed scheme significantly outperforms the conventional RIS in improving the secrecy rate.
Due to the advantages of beamforming technology in multi-antenna wireless communication systems, it has been widely studied and applied. However, its effectiveness in high-mobility scenarios may significantly deterior...
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Due to the advantages of beamforming technology in multi-antenna wireless communication systems, it has been widely studied and applied. However, its effectiveness in high-mobility scenarios may significantly deteriorate due to severe channel aging. In this paper, we propose an improved Levy induced stochastic differential equation network (LDE-Net) based on the Euler-Maruyama method for channel prediction. The proposed scheme captures the stochastic and rapid changes of wireless channels, by dividing the neural network into fine-grained stages while maintaining a moderate model size by parameter sharing among stages. In order to verify its performance in practice, we conduct channel measurements in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) scenarios on our campus, respectively. In both scenarios, LDE-Net performs better than the conventional and widely-used autoregressive model, long short term memory, and recurrent reural network schemes. In the LoS scenario, its throughput is 19.615% on average higher than all other schemes at various speeds, and 10.64% for NLoS.
In this work, we investigate a practical secure multiple-input single-output (MISO) wireless transmission of information from a source to a destination assisted by cooperative devices. In the first phase, a space-time...
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In this work, we investigate a practical secure multiple-input single-output (MISO) wireless transmission of information from a source to a destination assisted by cooperative devices. In the first phase, a space-time block code (STBC) is employed;thus, the source does not require any channel state information (CSI). In the second phase, the cooperative devices are divided into two groups, depending on whether they serve for relaying or jamming. Based on the imperfect CSI toward the destination, the devices allocated to the relay group assist the source in forwarding information, whereas those within the jamming group send artificial noise (AN) so as to prevent the eavesdropper from receiving information. Furthermore, the system utilizes two grouping strategies, referred to as random and adaptive. In the former case the role (relaying or jamming) of each device is predetermined randomly, whereas in the latter case it is determined autonomously depending on the decoding results. We analyze the outage probability of our proposed system with and without power allocation optimization under the assumption of imperfect CSI at the cooperative devices, and their accuracy is verified by Monte-Carlo simulations.
The communication performance can be restricted by inevitable training overhead for acquiring channel state information (CSI). A space- time code (STC)-based cooperative communication scheme is proposed to reduce the ...
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The communication performance can be restricted by inevitable training overhead for acquiring channel state information (CSI). A space- time code (STC)-based cooperative communication scheme is proposed to reduce the training overhead by employing a two-antenna relay node (RN) that broadcasts merely two orthogonal pilot sequences from the two antennas to a source node (SN) and a destination node (DN). The received signal-to-noise ratio, the achievable rate, and the bit error rate are mathematically analyzed, and numerical results confirm that the proposed STC-based relay system increases the achievable rate irrespective of the number of antennas at SN and DN. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
Massive multiple-input multiple-output (MIMO) technology is a key enabler for future wireless communication systems. On the other hand, crosstalk is inevitable in the highly integrated large-scale antenna array. The n...
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Massive multiple-input multiple-output (MIMO) technology is a key enabler for future wireless communication systems. On the other hand, crosstalk is inevitable in the highly integrated large-scale antenna array. The nonlinearity of power amplifiers (PAs) coupled with crosstalk is much more complicated when the number of antennas is large. Digital predistortion (DPD) can compensate for the PA nonlinearity without sacrificing efficiency and is widely applied in both base stations and mobile devices. Extending existing solutions to the large-scale MIMO system usually suffers from performance degradation or greatly increased complexity. In this paper, we consider the nonlinearity with crosstalk for the large-scale MIMO scenario. A much simplified DPD model is proposed by ignoring the high-order crosstalk terms generated from the complete nonlinear model. Compared to existing solutions, the proposed DPD can compensate for both the nonlinear and linear crosstalk of the system. It achieves satisfactory linearization performance with much reduced computational complexity. The complexity of the proposed DPD is linearly related to the number of transmitter branches. Thus, it is scalable in the large-scale MIMO scenario. Moreover, the proposed DPD can be extended to the partially-connected hybrid beamforming system with crosstalk. Simulation results validate the effectiveness of the proposed DPD scheme.
Differential space-time coding methods have been investigated for ultra-wideband communication systems to avoid channel estimation. However, their performance in the line-of-sight (LOS) environment is worse than the r...
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Differential space-time coding methods have been investigated for ultra-wideband communication systems to avoid channel estimation. However, their performance in the line-of-sight (LOS) environment is worse than the recently proposed orientational beamforming (OBF) system under low signal-to-noise ratios (SNRs). On the other hand, the OBF system cannot work well in the non-LOS (NLOS) environment. To address these issues, a hybrid differential detection (HDD) scheme is proposed in this paper, which combines the OBF system with a proposed differential OBF (DOBF) system. First, the DOBF system with a fully differential detection scheme is proposed, whose performance is almost the same as the differential space-time block coding method. However, its detection complexity only linearly increases with the number of transmitting antennas. Then, the HDD scheme is proposed, with its decision statistic being a combination of a modified OBF decision statistic and the DOBF decision statistic. In the NLOS environment, the HDD scheme is reduced to the DOBF system. In the LOS environment, a combination coefficient is obtained by mapping an SNR-related factor through a sigmoid function. The optimal parameters for the sigmoid function are determined through simulations, with which the proposed HDD scheme can achieve overall better performance than the OBF and DOBF systems.
Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of R...
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Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.
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