Shanyun Liu

Smart Channel Sounder for 5G IoT: From Wireless Big Data to Active Communication

Internet-of-Things (IoT) will connect billions of smart devices and generate inundant data through prominent solutions, such as machine type communication. The Third Generation Partnership Project has launched the corresponding standards for multiple heterogeneous wireless smart devices in the long term evolution (LTE)/LTE-advanced. In the forthcoming years, the valuable information hidden in the deluge of data will be extracted and utilized in every field to improve quality and efficiency. However, the bottleneck of realizing this magnificent vista of future intelligent lives lies in how to satisfy the practical demands to transmit huge data volume through efficient wireless communication in diverse scenarios. Herein, multi-scenario wireless communication triggers critical problems in wireless channel modeling and soundings for 5G IoT, which by far, are understudied. In this paper, we introduce a general wireless channel model and its multiple up-to-date corresponding channel sounding methods for future 5G IoT green wireless communication. Through adopting the perspective of wireless big data excavation, the smart channel sounder transforms the traditional passive wireless communication scheme into an active expectation-guaranteed wireless communication scheme, which helps achieve efficient and green communication. To demonstrate the validity and efficiency of this smart sounder scheme, we make a compatible prototype testified in multiple scenarios. The multiple real-scenario experiments demonstrate that the smart sounder can function effectively, especially in those scenarios where traditional channel state information is not available or imperfect.

Location-Aware Low Complexity ICI Reduction in OFDM Downlinks for High-Speed Railway Communication Systems with Distributed Antennas

High mobility may destroy the orthogonality of subcarriers in OFDM systems, resulting in inter-carrier interference (ICI), which may greatly reduce the service quantity of high speed railway (HSR) wireless communications. This paper focuses on ICI mitigation in the HSR downlinks with distributed transmit antennas. In such a system, its key feature is that the ICIs are caused by multiple carrier frequency offsets corresponding to multiple transmit antennas. Meanwhile, the channel of HSR is fast time varying, which is another big challenge in the system design. In order to get a good performance, low complexity real-time ICI reduction is necessary. To this end, we first analyzed the property of the ICI matrix and then propose a low complexity ICI reduction method based on location information. For evaluating the effectiveness of the proposed method, the maximum and minimum remaining interference after ICI reduction is analyzed and the service quantity is also discussed. Numerical results are presented to verify our theoretical analysis and the effectiveness of the proposed ICI reduction method. One important observation is that our proposed ICI mitigation method can achieve almost the same service quantity with that obtained on the case without ICI when the velocity of the train is 300km/h.

Position-based diversity and multiplexing analysis for high speed railway communications

The booming development of high speed railway (HSR) in recent years triggers a rapidly growing demand of high-quality wireless communication, which at the meantime motivates great research interest in providing a reliable and high data rate wireless communication in high speed scenarios. To continuously explore the nature and character of HSR scenario, this paper mainly aims to investigate the reliability and instantaneous capacity of the multiple-input-multiple-output (MIMO) system through taking full advantage of diversity gain and positioned-based channel characters. We take both the large and small scale fading into modeling this scenario. Through Monte-Carlo simulation, we give out the bit error rate and the handover success probability for one carriage when applying a low decoding complexity full-rate full-diversity orthogonal space time block coding scheme. Besides, we give out the channel capacity and the total mobile service of one base station (BS) with full multiplexing gain in HSR scenario. After compared with practical measured values, we can draw the conclusion that a single antenna system can hardly meet the requirements of growth-expected wireless communication demands without buffering or subtle transmission scheme designing, which will obviously aggravate the overheads and increase the system complexity. However, a 2×2 antenna system with full multiplexing gain is more than enough to solve this tradeoff dilemma.

DOA estimator for multiple coherently distributed sources with symmetric angular distribution

In applications such as mobile communications and sonar where the effect of angular spread may not be ignored, the direct application of most high-resolution methods may lead to inaccurate estimation of direction of arrival (DOA). Though the MUSIC and ESPRIT method can be generalized for coherently distributed sources. For uniform linear array, a novel DOA estimator is proposed by using the generalized eigenvalue decomposition rather than resort to 1 or 2-dimensional search. Simulation results are also presented to demonstrate the effectiveness of the proposed method, especially in case of sources with different angular distribution.

Location-Aided Umbrella-Shaped Massive MIMO Beamforming Scheme with Transmit Diversity for High Speed Railway Communications

In this paper, we present a practical simple location-aided umbrella-shaped beamforming scheme with transmit diversity of massive Multiple-input Multiple-output (MIMO) system for high speed railway scenarios. Unlike conventional schemes which combines space-time block coding (STBC) with adaptive beamforming or orthogonal switched beamforming, our scheme needs neither uplink channel covariance matrix (UCCM) nor downlink CCM (DCCM) but precalculates the beamforming weights with the help of train location information, which can be completed through pure off-line calculation and therefore reduce system implementation complexity. A closed-form solution of power allocation optimization is derived and the performance of our scheme is verified with simulations from the perspectives of instantaneous received signal-to- noise ratio (SNR), bit error rate (BER) and handover success probability. It indicates that the performance of our scheme approaches to the combination scheme of STBC and adaptive beamforming (STBC-ABF) without introducing any on-line system complexities.

Message Importance Measure and Its Application to Minority Subset Detection in Big Data

Message importance measure (MIM) is an important index to describe the message importance in the scenario of big data. Similar to the Shannon Entropy and Renyi Entropy, MIM is proposed to characterize the uncertainty of a random process and some related statistical characteristics. Moreover, MIM also needs to highlight the importance of those events with relatively small occurring probabilities, thereby is especially applicable to the big data scenario. In this paper, we define a parametric MIM measure from the viewpoint of information theory and then investigate its properties. We also present a parameter selection principle that provides answers to the minority subsets detection problem in the statistical processing of big data.

Location-Aware ICI Reduction in MIMO-OFDM Downlinks for High-Speed Railway Communication Systems

In the long-term evolution for railway, orthogonal frequency-division multiplexing (OFDM) is adopted to provide seamless connection to existing ground cellular network and support high mobility communication in future 5G. However, due to the high velocity of high-speed railway (HSR), the channel is fast time-varying and serious intercarrier interference (ICI) is introduced, meaning that low-complexity and real-time ICI reduction methods are needed, which has not been well discussed in previous works. In this paper, we focus on the HSR downlinks with distributed transmit antennas and develop two corresponding ICI reduction methods for additive white Gaussian noise (AWGN) and Rician channels, respectively. With the information of relative locations and velocities between corresponding antenna pairs, we show that the ICI matrices in AWGN and Rician channels can be mathematically calculated, and they are approximately unitary. With these results, we propose two corresponding low-complexity ICI reduction methods to avoid matrix inversion and suit fast time-varying nature. Simulation results show our proposed ICI mitigation method can achieve similar service quantity with that obtained on the case without ICI when velocity is about 300 km/h.

Hardware implementation on m parameter ML estimation of Nakagami-m fading channel

A wideband field-programmable gate array (FPGA) based hardware implementation for the m parameter estimation of the Nakagami-m fading channel is introduced. It requires fresh estimation of the noise spectrum power density to actually evaluate the signal-to-noise ratio (SNR), and with it, one can efficiently measure the m parameter of Nakagami-m fading channel. The hardware employs a Xilinx Virtex-6 SX475T-2c FPGA integrated Minibee platform operated in Centos system, in which a wideband quadrature modulator ADL5375 is integrated, with output frequency ranging from 400MHz to 6GHz. Such a hardware framework enables the measure system to function well in wireless wideband systems. In addition, we utilize it to conduct real-scenario estimations and compare the results with the software simulations. It also indicates our developed m parameter ML estimation has a better performance compared with some known estimation algorithms.

Focusing on a probability element: Parameter selection of message importance measure in big data

Message importance measure (MIM) is applicable to characterize the importance of information in the scenario of big data, similar to entropy in information theory. In fact, MIM with a variable parameter can make an effect on the characterization of distribution. Furthermore, by choosing an appropriate parameter of MIM, it is possible to emphasize the message importance of a certain probability element in a distribution. Therefore, parametric MIM can play a vital role in anomaly detection of big data by focusing on probability of an anomalous event. In this paper, we propose a parameter selection method of MIM focusing on a probability element and then present its major properties. In addition, we discuss the parameter selection with prior probability, and investigate the availability in a statistical processing model of big data for anomaly detection problem.

Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big Data

Information transfer that characterizes the information feature variation can have a crucial impact on big data analytics and processing. Actually, the measure for information transfer can reflect the system change from the statistics by using the variable distributions, similar to Kullback-Leibler (KL) divergence and Renyi divergence. Furthermore, to some degree, small probability events may carry the most important part of the total message in an information transfer of big data. Therefore, it is significant to propose an information transfer measure with respect to the message importance from the viewpoint of small probability events. In this paper, we present the message importance transfer measure (MITM) and analyze its performance and applications in three aspects. First, we discuss the robustness of MITM by using it to measuring information distance. Then, we present a message importance transfer capacity by resorting to the MITM and give an upper bound for the information transfer process with disturbance. Finally, we apply the MITM to discuss the queue length selection, which is the fundamental problem of caching operation on mobile edge computing.