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Tuo Wu

Publications and source records attributed to Tuo Wu.

76 records · Page 5Linked to original sources

Joint Angle Estimation Error Analysis and 3D Positioning Algorithm Design for mmWave Positioning System

In this paper, we propose a comprehensive framework to jointly analyze the angle estimation error and design the three-dimensional (3D) positioning algorithm for a millimeter wave (mmWave) positioning system. First, we estimate the angles of arrival (AoAs) at the anchors by applying the two-dimensional discrete Fourier transform (2D-DFT) algorithm. Based on the property of the 2D-DFT algorithm, the angle estimation error is analyzed in terms of probability density functions (PDF). The analysis shows that the derived angle estimation error is non-Gaussian, which is different from the existing work. Second, the intricate expression of the PDF of the AoA estimation error is simplified by employing the first-order linear approximation of triangle functions. Then, we derive a complex expression for the variance based on the derived PDF. Specifically, for the azimuth estimation error, the variance is separately integrated according to the different non-zero intervals of the PDF. Finally, we apply the weighted least square (WLS) algorithm to estimate the 3D position of the MU by using the estimated AoAs and the obtained non-Gaussian variance. Extensive simulation results confirm that the derived angle estimation error is non-Gaussian, and also demonstrate the superiority of the proposed framework.

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Fingerprint Based mmWave Positioning System Aided by Reconfigurable Intelligent Surface

Reconfigurable intelligent surface (RIS) is a promising technique for millimeter wave (mmWave) positioning systems. In this paper, we consider multiple mobile users (MUs) positioning problem in the multiple-input multiple-output (MIMO) time-division duplex (TDD) mmWave systems aided by the RIS. We derive the expression for the space-time channel response vector (STCRV) as a novel type of fingerprint. The STCRV consists of the multipath channel characteristics, e.g., time delay and angle of arrival (AOA), which is related to the position of the MU. By using the STCRV as input, we propose a novel residual convolution network regression (RCNR) learning algorithm to output the estimated three-dimensional (3D) position of the MU. Specifically, the RCNR learninng algorithm includes a data processing block to process the input STCRV, a normal convolution block to extract the features of STCRV, four residual convolution blocks to further extract the features and protect the integrity of the features, and a regression block to estimate the 3D position. Extensive simulation results are also presented to demonstrate that the proposed RCNR learning algorithm outperforms the traditional convolution neural network (CNN).

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An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems

In the past as well as present wireless communication systems, the wireless propagation environment is regarded as an uncontrollable black box that impairs the received signal quality, and its negative impacts are compensated for by relying on the design of various sophisticated transmission/reception schemes. However, the improvements through applying such schemes operating at two endpoints (i.e., transmitter and receiver) only are limited even after five generations of wireless systems. Reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS) have emerged as a new and revolutionary technology that can configure the wireless environment in a favorable manner by properly tuning the phase shifts of a large number of quasi passive and low-cost reflecting elements, thus standing out as a promising candidate technology for the next-/sixth-generation (6G) wireless system. However, to reap the performance benefits promised by RIS/IRS, efficient signal processing techniques are crucial, for a variety of purposes such as channel estimation, transmission design, radio localization, and so on. In this paper, we provide a comprehensive overview of recent advances on RIS/IRS-aided wireless systems from the signal processing perspective. We also highlight promising research directions that are worthy of investigation in the future.

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Beamforming Design in Multiple-Input-Multiple-Output Symbiotic Radio Backscatter Systems

Symbiotic radio (SR) backscatter systems are possible techniques for the future low-power wireless communications for Internet of Things devices. In this paper, we propose a multiple-input-multiple-output (MIMO) SR backscatter system, where the secondary multi-antenna transmission from the backscatter device (BD) to the receiver is riding on the primary multi-antenna transmission from the transmitter to the receiver. We investigate the beamforming design optimization problem which maximizes the achievable rate of secondary transmission under the achievable rate constraint of primary transmission. In the MIMO SR backscatter system, each antenna of the SR BD reflects its received ambient radio frequency signals from all the transmitting antennas of the transmitter, which causes the globally optimal solution is difficult to obtain. In this paper, we propose a method to obtain the achievable rate upper bound. Furthermore, considering both primary and secondary transmissions, we propose an exact penalty method based locally optimal solution. Simulation results illustrate that our proposed exact penalty method based locally optimal solution performs close to the upper bound.

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