438 research outputs found
Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
Hybrid massive MIMO structures with lower hardware complexity and power
consumption have been considered as a potential candidate for millimeter wave
(mmWave) communications. Channel covariance information can be used for
designing transmitter precoders, receiver combiners, channel estimators, etc.
However, hybrid structures allow only a lower-dimensional signal to be
observed, which adds difficulties for channel covariance matrix estimation. In
this paper, we formulate the channel covariance estimation as a structured
low-rank matrix sensing problem via Kronecker product expansion and use a
low-complexity algorithm to solve this problem. Numerical results with uniform
linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to
demonstrate the effectiveness of our proposed method
Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments
Hybrid massive MIMO structures with reduced hardware complexity and power
consumption have been widely studied as a potential candidate for millimeter
wave (mmWave) communications. Channel estimators that require knowledge of the
array response, such as those using compressive sensing (CS) methods, may
suffer from performance degradation when array-inherent impairments bring
unknown phase errors and gain errors to the antenna elements. In this paper, we
design matrix completion (MC)-based channel estimation schemes which are robust
against the array-inherent impairments. We first design an open-loop training
scheme that can sample entries from the effective channel matrix randomly and
is compatible with the phase shifter-based hybrid system. Leveraging the
low-rank property of the effective channel matrix, we then design a channel
estimator based on the generalized conditional gradient (GCG) framework and the
alternating minimization (AltMin) approach. The resulting estimator is immune
to array-inherent impairments and can be implemented to systems with any array
shapes for its independence of the array response. In addition, we extend our
design to sample a transformed channel matrix following the concept of
inductive matrix completion (IMC), which can be solved efficiently using our
proposed estimator and achieve similar performance with a lower requirement of
the dynamic range of the transmission power per antenna. Numerical results
demonstrate the advantages of our proposed MC-based channel estimators in terms
of estimation performance, computational complexity and robustness against
array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS
channel estimator.Comment: This work has been submitted to the IEEE for possible publication.
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Extreme Learning Machine Based Non-Iterative and Iterative Nonlinearity Mitigation for LED Communications
This work concerns receiver design for light emitting diode (LED)
communications where the LED nonlinearity can severely degrade the performance
of communications. We propose extreme learning machine (ELM) based
non-iterative receivers and iterative receivers to effectively handle the LED
nonlinearity and memory effects. For the iterative receiver design, we also
develop a data-aided receiver, where data is used as virtual training sequence
in ELM training. It is shown that the ELM based receivers significantly
outperform conventional polynomial based receivers; iterative receivers can
achieve huge performance gain compared to non-iterative receivers; and the
data-aided receiver can reduce training overhead considerably. This work can
also be extended to radio frequency communications, e.g., to deal with the
nonlinearity of power amplifiers
Optical signal processing for fiber Bragg grating based wear sensors
In this study, we propose a simplified signal processing scheme for fiber Bragg grating (FBG) based wear sensing. Instead of using a chirped FBG and detecting the bandwidth, we use uniform gratings as sensors and measure the optical power reflected by the sensing grating to determine the length of the sensor grating, hence detect the wear. We demonstrate by the experiments that the proposed method is feasible and practical. The advantage of the proposed method lies in the fact that structure of the wear sensing system is simplified and therefore the cost can be significantly reduced. The principle of the proposed method, the design of the wear sensor, and the experiments are described
An improved channel model for ADSL and VDSL systems
This paper examines existing channel models used with xDSL systems and identifies a key shortcoming - namely, the implicit assumption that all impulse noise originates at the transmitter. Based on extensive data collected from the local loop, a new model is proposed which addresses this problem by combining a digital filter model of the transmission line with a distributed noise source. This better reflects the nature of a real telephone line, and thus provides a more solid basis for simulation and optimisation of xDSL systems
New approach to improve the performance of fringe pattern profilometry using multiple triangular patterns for the measurement of objects in motion
Fringe pattern profilometry using triangular patterns and intensity ratios is a robust and computationally efficient method in three-dimensional shape measurement technique. However, similar to other multiple-shot techniques, the object must be kept static during the process of measurement, which is a challenging requirement for the case of fast-moving objects. Errors will be introduced if the traditional multiple-shot techniques are used directly in the measurement of a moving object. A new method is proposed to address this issue. First, the movement of the object is measured in real time and described by the rotation matrix and translation vector. Then, the expressions are derived for the fringe patterns under the influence of the two-dimensional movement of the object, based on which the normalized fringe patterns from the object without movement are estimated. Finally, the object is reconstructed using the existing intensity ratio algorithm incorporating the fringe patterns estimated, leading to improved measurement accuracy. The performance of the proposed method is verified by experiments
Fringe calibration using neural network signal mapping for structured light profilometers
We present a novel neural network signal calibration technique to improve the performance of triangulation based structured light profilometers. The performance of such profilometers is often hindered by the capture of noisy and aberrated pattern intensity distributions. We address this problem by employing neural networks and a spatial digital filter in a signal mapping approach. The performance of the calibration technique is gauged through both simulation and experimentation, with simulation results indicating that accuracy can be improved by more than 80%
Spatial shift unwrapping for digital fringe profilometry based on spatial shift estimation
An approach is presented to solve the problem of spatial shift wrapping associated with spatial shift estimation-based fringe pattern profilometry (FPP). This problem arises as the result of fringe reuses (that is, use of fringes with periodic light intensity variance), and the spatial shift can only be identified without ambiguity within the range of a fringe width. It is demonstrated that the problem is similar to the phase unwrapping problem associated with the phase-detection-based FPP, and the proposed method is inspired by the existing ideas of using multiple images with different wavelengths proposed for phase unwrapping. The effectiveness of the proposed method is verified by comparing experimental results against several objects, with the last object consisting of more complex surface features. We conclude by showing that our method is successful in reconstructing the fine details of the more complex object
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