References of "Kong, Long 50037137"
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See detailDecision Triggered Data Transmission and Collection in Industrial Internet of Things
He, Jiguang; Kong, Long UL; Frondelius, Tero et al

in 2020 IEEE Wireless Communications and Networking Conference (WCNC) (2020, June 19)

We propose a decision triggered data transmission and collection (DTDTC) protocol for condition monitoring and anomaly detection in the industrial Internet of things (IIoT). In the IIoT, the collection ... [more ▼]

We propose a decision triggered data transmission and collection (DTDTC) protocol for condition monitoring and anomaly detection in the industrial Internet of things (IIoT). In the IIoT, the collection, processing, encoding, and transmission of the sensor readings are usually not for the reconstruction of the original data but for decision making at the fusion center. By moving the decision making process to the local end devices, the amount of data transmission can be significantly reduced, especially when normal signals with positive decisions dominate in the whole life cycle and the fusion center is only interested in collecting the abnormal data. The proposed concept combines compressive sensing, machine learning, data transmission, and joint decision making. The sensor readings are encoded and transmitted to the fusion center only when abnormal signals with negative decisions are detected. All the abnormal signals from the end devices are gathered at the fusion center for a joint decision with feedback messages forwarded to the local actuators. The advantage of such an approach lies in that it can significantly reduce the volume of data to be transmitted through wireless links. Moreover, the introduction of compressive sensing can further reduce the dimension of data tremendously. An exemplary case, i.e., diesel engine condition monitoring, is provided to validate the effectiveness and efficiency of the proposed scheme compared to the conventional ones. [less ▲]

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See detailUnified Framework for Secrecy Characteristics with Mixture of Gaussian (MoG) Distribution
Kong, Long UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Wireless Communications Letters (2020)

The mixture of Gaussian (MoG) distribution was proposed to model the wireless channels by implementing the completely unsupervised expectation-maximization (EM) learning algorithm. With the high ... [more ▼]

The mixture of Gaussian (MoG) distribution was proposed to model the wireless channels by implementing the completely unsupervised expectation-maximization (EM) learning algorithm. With the high convenience for density estimation applications, the focus of this letter is supposed to investigate the secrecy metrics, including secrecy outage probability (SOP), the lower bound of SOP, the probability of non-zero secrecy capacity (PNZ), and the average secrecy capacity (ASC) from the information-theoretic perspective. The above-mentioned metrics are derived with simple and unified closed-form expressions. The effectiveness of our obtained analytical expressions are successfully examined and compared with Monte-Carlo simulations. One can conclude that this letter provides a simple but effective closed-form secrecy analysis solution exploiting the MoG distribution. [less ▲]

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See detailEffective Throughput Analysis of α-η-κ-μ Fading Channels
Ai, Yun; Mathur, Aashish; Kong, Long UL et al

in IEEE Access (2020)

The α-η-κ-µ fading model is a very useful instrument to accurately describe various radio wave propagation scenarios. In this paper, we study the effective throughput performance of communication systems ... [more ▼]

The α-η-κ-µ fading model is a very useful instrument to accurately describe various radio wave propagation scenarios. In this paper, we study the effective throughput performance of communication systems over the α-η-κ-µ fading channels. Novel and exact expressions for the effective throughput over α-η-κ-µ channels are derived, and the effective throughput of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) systems over some widely used small-scale fading models are presented based on the derived results. To obtain more understandings on the impact of physical channel characteristics and system configuration on the effective throughput, closed-form expressions for the asymptotic effective throughput at high signal-to-noise ratio (SNR) regimes are also obtained. The results reveal the underlying connections between different physical channel parameters (e.g., scattering level, phase correlation, channel nonlinearity, multipath clustering, and channel imbalance) and the effective throughput. It is found that the effective throughput improves with the increase of channel nonlinearity and number of multipath clusters, and the high-SNR slope is only dependent on the channel nonlinearity and the number of multipath clusters present in the physical channel. [less ▲]

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See detailLarge Intelligent Surface for Positioning in Millimeter Wave MIMO Systems
He, Jiguang; Wymeersch, Henk; Kong, Long UL et al

in 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (2020)

Millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system for the fifth generation (5G) cellular communications can also enable single-anchor positioning and object tracking due to its large ... [more ▼]

Millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system for the fifth generation (5G) cellular communications can also enable single-anchor positioning and object tracking due to its large bandwidth and inherently high angular resolution. In this paper, we introduce the newly invented concept, large intelligent surface (LIS), to mmWave positioning systems, study the theoretical performance bounds (i.e., Cramér-Rao lower bounds) for positioning, and evaluate the impact of the number of LIS elements and the value of phase shifters on the position estimation accuracy compared to the conventional scheme with one direct link and one non-line-of-sight path. It is verified that better performance can be achieved with a LIS from the theoretical analyses and numerical study. [less ▲]

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