Measurement-Based Analysis of Transmit Antenna Selection for In-Cabin Distributed MIMO System

Zhang, Yan; Li, Zhenghui; Luan, Fengyu; Xiao, Limin; Zhou, Shidong; Jing, Wang

doi:10.1155/2012/598049

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The Tsinghua University (THU) channel sounder [21] was adopted for the measurement campaigns. It worked at the central frequencies of 2.4 GHz, 3.52 GHz, and 5.8 GHz, with a bandwidth of 40 MHz [22]. At the transmitter (Tx) side, a commercial signal generator was used to periodically generate a chirp serial signal.…”

Section: Measurement Description and Data Post-processing A Measmentioning

confidence: 99%

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

et al. 2019

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Wireless communications in aircraft cabin environments have drawn widespread attention with the increase of application requirements. To ensure reliable and stable in-cabin communications, the investigation of channel parameters such as path loss is necessary. In this paper, four machine learning methods, including back propagation neural network (BPNN), support vector regression (SVR), random forest, and AdaBoost, are used to build path loss prediction models for an MD-82 aircraft cabin. Firstly, machine-learning-based models are designed to predict the path loss values at different locations at a fixed frequency. It is shown that these models fit the measured data well, e.g., at 2.4 GHz central frequency the root mean square errors (RMSEs) of BPNN, SVR, random forest, and AdaBoost predictors are 1.90 dB, 2.20 dB, 1.76 dB, and 2.12 dB. Subsequent research is engaged to forecast path loss at a new frequency based on available information at known frequencies. Additionally, to solve the data limitation problem at the new frequency, we propose a path loss prediction scheme combining empirical models and machinelearning-based models. This scheme uses estimated values generated by the empirical model according to prior information to expand the training set. To verify the performance of this scheme, measured samples at 2.4 GHz and 3.52 GHz, as well as samples generated by the empirical model are employed as the training set for the path loss prediction at 5.8 GHz. The RMSEs of BPNN, SVR, random forest, and AdaBoost models are 2.49 dB, 2.78 dB, 2.54 dB, and 3.76 dB. In contrast, without samples generated by the empirical model, the RMSEs of those models are 3.84 dB, 4.94 dB, 6.57 dB, and 6.77 dB. Results show that the proposed data expansion scheme improves prediction performance when there are few measurement samples at the new frequency. INDEX TERMS Aircraft cabin, data expansion, machine learning, path loss prediction, propagation characteristics.

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Section: Measurement Description and Data Post-processing A Measmentioning

confidence: 99%

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

et al. 2019

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“…Then, Eve can respectively have the channel characteristics of Alice → Eve and Bob → Eve. Moreover, if she is aware of the distribution of the scatters in the environment, she can estimate the channel characteristics of Alice ↔ Bob with high probability [29][30][31]. Then, she can filch the new secret keys.…”

Section: Applicationmentioning

confidence: 99%

Identity Authentication over Noisy Channels

Zheng

Xiao

Zhou

et al. 2015

Entropy

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Identity authentication is the process of verifying users' validity. Unlike classical key-based authentications, which are built on noiseless channels, this paper introduces a general analysis and design framework for identity authentication over noisy channels. Specifically, the authentication scenarios of single time and multiple times are investigated. For each scenario, the lower bound on the opponent's success probability is derived, and it is smaller than the classical identity authentication's. In addition, it can remain the same, even if the secret key is reused. Remarkably, the Cartesian authentication code proves to be helpful for hiding the secret key to maximize the secrecy performance. Finally, we show a potential application of this authentication technique.

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“…TAS technique is able to use the transmitting power efficiently and reduce the processing complexity. At the same time, TAS can lead capacity gain as fixed total transmit power [17,18]. In D-MIMO system, the affection of spatial correlation can be partially counteracted, too.…”

Section: Transmitting Antenna Selection In D-mimo Systemmentioning

confidence: 99%

Measurement-Based Spatial Correlation and Capacity of Indoor Distributed MIMO System

Zhang

Xiao

Zhou

et al. 2013

International Journal of Antennas and Propagation

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Distributed MIMO (D-MIMO) system is one of the candidates for future wireless access networks. In this study, the spatial correlation and capacity in indoor D-MIMO system are presented. All results are from the actual channel measurements in typical indoor scenarios, including office and corridor. Based on measured data, spatial correlation coefficients between distributed transmitting antennas are analyzed. Although the literature about D-MIMO system assumes the small scale fading between distributed antennas is independent, we find that spatial correlation may still exist in specific propagation scenario. This correlation can also degrade the performance of D-MIMO system. To mitigate the impact of spatial correlation, one efficient method is to use transmitting antenna selection technique.

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Measurement-Based Analysis of Transmit Antenna Selection for In-Cabin Distributed MIMO System

Cited by 3 publications

References 12 publications

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

Identity Authentication over Noisy Channels

Measurement-Based Spatial Correlation and Capacity of Indoor Distributed MIMO System

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