We propose adaptive channel predictors for orthogonal frequency division multiplexing (OFDM) communications over time-varying channels. Successful application of the normalized least-mean-square (NLMS) and recursive l...
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ISBN:
(纸本)0780374029
We propose adaptive channel predictors for orthogonal frequency division multiplexing (OFDM) communications over time-varying channels. Successful application of the normalized least-mean-square (NLMS) and recursiveleast-squares (RLS) algorithms is demonstrated. We also consider the use of adaptive channel predictors for delay-free equalization, thereby avoiding the need for regular transmission of pilot symbols. Simulation results demonstrate the good performance of the proposed techniques.
This paper presents a new method to reduce stray signal errors in antenna radiation pattern measurement using RLS algorithm (recursive least-squares algorithm). Stray signals create by reflection and scattering of the...
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ISBN:
(纸本)078039433X
This paper presents a new method to reduce stray signal errors in antenna radiation pattern measurement using RLS algorithm (recursive least-squares algorithm). Stray signals create by reflection and scattering of the range antenna field from fixed objects in the range and by leakage of the range RF system. This method can be used on static far-field range. In this method, an antenna with the known pattern (reference antenna) is placed in the test zone to find the optimal tap weights of the RLS algorithm. By the optimal tap weights, radiation pattern of AUT (Antenna Under Test) is corrected. The effectiveness of this method is confirmed by some simulation results.
The paper proposes a joint semi-blind algorithm for simultaneously cancelling the self-interference component and estimating the propagation channel in 5G Quasi-Cyclic Low-Density Parity-Check (QC-LDPC)-encoded short-...
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The paper proposes a joint semi-blind algorithm for simultaneously cancelling the self-interference component and estimating the propagation channel in 5G Quasi-Cyclic Low-Density Parity-Check (QC-LDPC)-encoded short-packet Full-Duplex (FD) transmissions. To avoid the effect of channel estimation processes when using short-packet transmissions, this semi-blind algorithm was developed by taking into account only a small number (four at least) pilot symbols, which was integrated with the intended information sequence and used for the feedback loop of the estimation of the channels. The results showed that this semi-blind algorithm not only achieved nearly optimal performance, but also significantly reduced the processing time and computational complexity. This semi-blind algorithm can also improve the performances of the Mean-Squared Error (MSE) and Bit Error Rate (BER). The results of this study highlight the potential efficiency of this joint semi-blind iterative algorithm for 5G and Beyond and/or practical IoT transmission scenarios.
This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identificat...
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This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identification model, the recursiveleast-squares and stochastic gradient algorithms including their multi-innovation versions by introducing an innovation vector are proposed. The simulation results of the FitzHugh-Nagumo model indicate that the proposed algorithms perform according to the expected effectiveness.
This paper proposes an online type of controller parameter tuning method by modifying the standard fictitious reference iterative tuning method and by utilizing the so-called recursiveleast-squares (RLS) algorithm, w...
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This paper proposes an online type of controller parameter tuning method by modifying the standard fictitious reference iterative tuning method and by utilizing the so-called recursiveleast-squares (RLS) algorithm, which can cope with variation of plant characteristics adaptively. As used in many applications, the RLS algorithm with a forgetting factor is also applied to give more weight to more recent data, which is appropriate for adaptive controller tuning. Moreover, we extend the proposed method to online tuning of the feed-forward controller of a two-degree-of-freedom control system. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method.
Load identification plays an important role in structural health monitoring, which aims at preventing structural failures. In order to identify load for linear systems and nonlinear systems, this paper presents method...
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Load identification plays an important role in structural health monitoring, which aims at preventing structural failures. In order to identify load for linear systems and nonlinear systems, this paper presents methods to identify load for a cantilever beam based on dynamic strain measurement by Fiber Bragg Grating (FBG) sensors. For linear systems, the proposed inverse method consists of Kalman filter with no load terms and a linear estimator. For nonlinear systems, the proposed inverse method consists of cubature Kalman filter (CKF) with no load terms and a nonlinear estimator. In the process of load identification, the state equations of the beam structures are constructed by using the finite element method (FEM). Kalman filter or CKF is used to suppress noise. The residual innovation sequences, gain matrix, and innovation covariance generated by Kalman filter or CKF are used to identify a load. To prove the effectiveness of the proposed method, numerical simulations and experiments of the beam structures are employed and the results show that the method has an excellent performance.
The vibration of vehicle is most governed by the load generated of the road roughness, which can affect comfort of vehicle and cause fatigue damage of the vehicle suspension. So knowledge of the load is very important...
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The vibration of vehicle is most governed by the load generated of the road roughness, which can affect comfort of vehicle and cause fatigue damage of the vehicle suspension. So knowledge of the load is very important for vehicle control systems to enhance vehicle stability and safety. During the process of the estimation, noise affects the identification accuracy, and maybe cause divergence. The cubature Kalman filter(CKF) has good accuracy performance, numerical stability and computational costs. So this paper presented a new methodology based on CKF and a recursive least-squares algorithm for estimating wheel-road vertical load. The state equations and measurement equations were established according to the second order vibration system, and RK4 was employed to discretize the continuous-time equations. The CKF was used to calculate the residual innovation sequences and the residual innovation sequences were used to calculate the vertical load. To verify the effectiveness of the identification method, numerical simulations of the five degrees of freedom vehicle vibration system subjected to white noises and four types of forces were employed. Simulation results validated and proved the feasibility of this approach.
A new robust and computationally efficient solution to least-squares problem in the presence of round-off errors is proposed. The properties of a harmonic regressor are utilized for design of new combined algorithms o...
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ISBN:
(纸本)9781467357159
A new robust and computationally efficient solution to least-squares problem in the presence of round-off errors is proposed. The properties of a harmonic regressor are utilized for design of new combined algorithms of direct calculation of the parameter vector. In addition, an explicit transient bound for estimation error is derived for classical recursiveleast-squares (RLS) algorithm using Lyapunov function method. Different initialization techniques of the gain matrix are proposed as an extension of RLS algorithm. All the results are illustrated by simulations.
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