Jiaxun Lu

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.

General hardware framework of Nakagami m parameter estimator for wireless fading channel

The Nakagami-m fading is well known in characterizing the envelop distribution in diverse wireless fading channels, and an accurate estimation of the m parameter is very crucial to evaluate the fading channel capacity. In this paper, we develop a hardware framework implanted in FPGA with corresponding interface design for the m parameter estimation of the Nakagami-m fading channel. Owing to the advantages of the high-speed hardware calculation, real-scenario estimation and the flexible system design, this newly developed framework can efficiently estimate the real m value with actual data base over a large frequency range. Moreover, it can be easily upgraded by software-based m parameter estimation methods through substituting the estimation algorithm. Most importantly, We evaluate our design with different m parameter estimation algorithms. The experiment results show that our design can properly adapt to different estimation algorithms and efficiently estimate the m value in both high-SNR and low-SNR regimes, thus allow us to observe the true variation of the channel state and make applications according to the observation, which is not attainable through software simulation.

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.

To Smart City: Public Safety Network Design for Emergency

In smart cities, there are always unpredicted emergencies, which must be handled to maintain the regular order. Then a smart system is needed to detect threats and deal with them. In this paper, we propose a system structure composed of a central agent and three layers: unmanned aerial vehicle (UAV) layer, multi-robot layer and sensor network layer. The UAVs act as moving sensors and conveyors in the air. They provide the overall rough monitoring data from the air and transport robots to the emergency occurring places. The robots on the ground are responsible for obtaining detailed monitoring data and dealing with the emergencies. The sensor networks keep monitoring the environment and assist robots and UAVs in the range to complete their tasks. The central agent can adjust the system according to the specific task requirements. We provide a general system design and review main required technologies, highlighting the challenges and some possible solutions. The future researching directions are presented to guide the system design.

Information Theory in Formation Control: An Error Analysis to Multi-Robot Formation

Multi-robot formation control makes prerequisites for a team of robots to execute complex tasks cooperatively, which has been widely applied in both civilian and military scenarios. However, the limited precision of sensors and controllers may inevitably cause position errors in the finally achieved formation, which will affect the tasks undertaken. In this paper, the formation error is analyzed from the viewpoint of information theory. The desired position and the actually achieved position are viewed as two random variables. By calculating the mutual information between them, a lower bound of the formation error is derived. The results provide insights for the estimation of possible formation errors in the multi-robot system, which can assist designers to choose sensors and controllers with proper precision.

Massive MIMO Beamforming With Transmit Diversity for High Mobility Wireless Communications

Providing stable and fast data transmission service is challenging in a high mobility wireless communication system, where massive multiple-input multiple-output (MIMO) beamforming is deemed as a potential solution. In the literature, the majority of previous works focused on how to optimize the beamforming scheme with traditional side information like perfect or imperfect channel state information (CSI) in non-mobile or low mobility scenarios. However, it is hard to either track the channel or obtain perfect CSI in the high mobility scenario without large online computation, because the wireless channel appears to be fast time varying and double-selective in the spatial-temporal domain. In this paper, by exploiting the special characters in the high mobility scenario, we introduce an applicable low-complexity beamforming scheme with transmit diversity in the high mobility scenario with the aid of location information. The beam is generated and selected mainly based on the location information, where the beam weight is optimized to maximize the total service that one BS can provide. Moreover, to guarantee a full diversity gain in this joint scheme, an optimal beam selection algorithm is proposed. Besides, to maximize the total service competence of one base station, a closed-form power allocation solution for the multi-user scenario is derived. To solve the potential inter-beam interference in massive MIMO system, a location-aided algorithm is proposed to eliminate the interference and maximize the mobile service of the whole train at the same time. Theoretical analysis and multiple simulation results show that our scheme approaches the theoretical performance bound of adaptive beamforming scheme but with much lower complexity.

Directivity-Beamwidth Tradeoff of Massive MIMO Uplink Beamforming for High Speed Train Communication

High-mobility adaption and massive multiple-input multiple-output (MIMO) application are two primary evolving objectives for the next generation high-speed train (HST) wireless communication system. In this paper, we consider how to design a location-aware beamforming for the massive MIMO system in the high traffic density HST network. We first analyze the tradeoff between beam directivity and beamwidth, based on which we present the sensitivity analysis of positioning accuracy. Then, in order to guarantee a high efficient transmission, we derive an optimal problem to maximize the beam directivity under the restriction of diverse positioning accuracies. After that, we present a low-complexity beamforming design by utilizing location information, which requires neither eigendecomposing (ED) the uplink channel covariance matrix (CCM) nor ED the downlink CCM. Finally, we study the beamforming scheme in the future high traffic density HST network, where a two HSTs encountering scenario is emphasized. By utilizing the real-time location information, we propose an optimal adaptive beamforming scheme to maximize the achievable rate region under limited channel source constraint. Numerical simulation indicates that a massive MIMO system with less than a certain positioning error can guarantee a required performance with satisfying transmission efficiency in the high traffic density HST scenario and the achievable rate region when two HSTs encounter is greatly improved as well.