This paper describes a system model of two-hop MIMO(Multiple-input and Multiple-output) relay network and introduces relaying strategies,channel models and transmit methods at the source.Then it presents the research progress on performance analysis of two-hop MIMO relay systems leading to a better understanding of actual MIMO relay systems and compares the performances of MIMO relay systems with different transmit methods through an example.Finally it discusses and expects some future research directions on the system under consideration.Performance analysis of MIMO relay system helps to guide its engineering applications.
To reduce the effect of disturbances,a nonlinear controller was designed for a DC/DC Buck converter.First,an error model with disturbances was built based on the nonlinear model of a Buck converter.Then an H∞controller and its parameters were deduced based on the passive theory.A theoretical analysis was given to prove the stability of a closed-loop control system.Finally,a numerical simulation and circuit simulation were performed for the case of parametric perturbations of a Buck converter.The simulation results demonstrated the validity and effectiveness of the proposed control strategy.
To reduce effectively channel noise, parameter mismatch, and inter-user interference, a dual unscented Kalman filter (DUFK) algorithm with combining blind extraction algorithm and different state space models for chaotic communication systems with multiuser is proposed. The simulation results indicate that the algorithm has a faster convergence speed and can realize effectively multiuser communications in a multi-input multi-output channel.
A method for generating chaotic spread-spectrum sequences based on the combined chaotic map is proposed. An optimized selection algorithm based on the property requirement of spread-spectrum sequences and the performance index of multiple access interference is also presented. Simulation is performed for the optimized chaotic sequence, and an optimized logistic sequence is applied to a direct sequence spread-spectrum CDMA system under different channel conditions. The results show that the chaotic spread-spectrum sequences generated by the proposed method have similar performance as the logistic spread-spectrum sequences and have better security.
A new approach is proposed in this paper for the hardware implementation of chaotic-signal-generation on field programmable gate array(FPGA) by the hardware description language(HDL) and the second order Runge-Kutta algorithm.Firstly,using the second order Runge-Kutta algorithm,the solution of continuous chaotic systems can be divided into a series of iteration steps.Secondly,using HDL and the state machine method,these iteration steps are implemented to generate digital chaotic sequences.Finally,using the high-speed digital-to-analog converter(DAC),a continuous analog chaotic signal can be observed.As examples,the detailed steps of realization and corresponding experimental results of a grid multi-scroll chaotic system and a classic Lorenz system are given.This method can be applied to generate chaotic signals of other systems,and do not need many FPGA resources.
In recent decades, nonlinear Kalman filtering based on Bayesian theory has been intensively studied to solve the problem of state estimation in nonlinear dynamical system. Under the Gaussian assumption, Bayesian filtering can provide a unified recursive solution to the estimation problem that is described as the calculation of Gaussian weighted integrals. However it is typically intractable to directly calculate these integrals. The numerical integration methods are required from a practical perspective. Therefore, nonlinear Kalman filters are generated by different numerical integrations. As a representative of nonlinear Kalman filter, cubature Kalman filter (CKF) utilizes a numerical rule based on the third-degree spherical-radial cubature rule to obtain better numerical stability, which is widely used in many fields, e.g., positioning, attitude estimation, and communication. Target tracking can be generalized as the estimations of the target position, the target velocity and other parameters. Hence, nonlinear Kalman filters can also be used to perform target tracking, effectively. Since the CKF based on the third-degree cubature rule has a limited accuracy of estimation, it is necessary to find a CKF based a cubature rule with higher accuracy in the case of target tracking system with a large uncertainty. High-degree cubature Kalman filter is therefore proposed to implement state estimation due to its higher numerical accuracy, which is preferred to solve the estimation problem existing in target tracking. To improve the filtering accuracy and robustness of high-degree cubature Kalman filter, in this paper we present a new filtering algorithm named Huber-based high-degree cubature Kalman filter (HHCKF) algorithm. After approximating nonlinear measurements by using the statistical linear regression model, the measurement update is implemented by the Huber M estimation. As a mixed estimation technique based on the minimum of l1-norm and l2-norm, the Huber estimator has high robustness and numerical accuracy under the assumption of Gaussian measurement noises. Therefore, the Huber-based high-degree cubature Kalman tracking algorithm is generated by combining the state prediction based on the fifth-degree spherical radial rule. In this paper, the influence of tuning parameter on the tracking performance is discussed by simulations. Simulations in the context of bearings only tracking and reentry vehicle tracking demonstrate that the new HHCKF can improve the tracking performance significantly.
Separation of multiple mixing chaotic signals is an important issue in chaos and its applications. An instantaneous blind separation method for linearly mixed chaotic signals is proposed in this paper, in which the uncorrelation characteristics of chaotic signals are utilized. In the case of unknown mixture matrix and chaotic equations, the inverse matrix for reconstructing the source chaotic signals can be directly estimated from the observation by the approach of solving eigenvectors. The results by computer simulation indicate that the multiple mixing chaotic signals, by using the method, can be effectively separated from noisy background even when the signal to noise ration is low.
A multiuser Amplify-and-Forward(AF) relay system is considered in which the source is equipped with multiple antennas,while the relay and each of multiple destinations have a single antenna.Orthogonal Space-Time Block Coding(OSTBC) is employed to achieve spatial diversity and opportunistic scheduling is used to obtain multiuser diversity.Closed-form expressions for outage probability and average Symbol Error Rate(SER) in independent but not identically distributed(i.n.i.d.) Rayleigh fading multiuser environments are derived for adaptive-gain and fixed-gain relaying methods.Asymptotic expressions for outage probability and average SER at high SNR are presented and used to determine the diversity order of the two relaying systems.The combined effect of spatial and multiuser diversity is quantified for the proposed system.Monte Carlo simulations demonstrate the accuracy of the analyses presented.Comparisons among the relaying methods and multiuser point-to-point systems are provided along with insights.
A two-hop relaying system in which the source are equipped with multiple antennas,while the relay and destination only have a single antenna is considered.OSTBC strategy is employed at the source to achieve space diversity.To make the complexity of the relay lower,fixed gain amplify-to-forward protocol is used.The closed-form expression for outage probability and average SER are derived in Rayleigh fading environment.Finally,Monte Carlo simulations are compared to numerical results to validate the theoretical expressions.
To estimate effectively parameters of nonlinear mapping, a cubature rule is used to approximate the weighted integral of this mapping. In this paper, based on these parameters modeled by a state-space model, a novel parameter estimation is proposed. Blind separation of chaotic signals is a challenging problem. The proposed method is used to solve this problem to achieve the effective reconstruction of chaotic signals. Simulation results indicate that the proposed method has a faster convergence speed and a higher numerical accuracy, and can effectively separate original chaotic signals.
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