adaptive algorithms for beam shaping of a phased array antenna and multiple-input multiple-output (MIMO) system gaining importance in today's advanced wireless networks to mitigate interference effects and distort...
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adaptive algorithms for beam shaping of a phased array antenna and multiple-input multiple-output (MIMO) system gaining importance in today's advanced wireless networks to mitigate interference effects and distortion in the receiving signal due to multipath, small scale, and large scale fading effects. This article deals with the development of reconfigurable field programmable gate array (FPGA)-based hardware for smart antenna system to explore parameter dependencies, drawbacks, and relative performance comparison of popular adaptive beamforming and interference suppression algorithms. These are least mean square, recursive least squares (RLS), and sample matrix inversion (SMI) used in real-time under laboratory environment where the existing wireless channel between transmitters and receivers is linear time-varying in nature due to presence of secondary sources giving rise to small-scale fading. For this at first, we propose a novel received signal strength indicator-based procedure to measure the radiation pattern of the antenna under an echoic indoor environment on a reconfigurable and portable FPGA system named wireless open-access research platform (WARP), controllable by generic programming codes over a user-friendly MATLAB interface. For better performance, the SMI algorithm was modified to increase block size rather than block shifting in general SMI. Later a comparative study was performed under varying conditions to observe the best utilization of three adaptive algorithms in beam shaping. In all cases, SMI performs the best with less beam shaping error and faster convergence, validating its use in a real-time fading environment.
In this paper, di_erent numerical methods to calculate the optimum coe_cients in the Volterra Series are introduced to analyze the performance of a Digital Predistorter (DPD) for Power Amplifier (PA) with memory. The ...
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In this paper, di_erent numerical methods to calculate the optimum coe_cients in the Volterra Series are introduced to analyze the performance of a Digital Predistorter (DPD) for Power Amplifier (PA) with memory. The adaptive algorithms used are Least Mean Square (LMS), Normalized LMS (NLMS), Variable Step Size (VSS), and the VSS modified. The parameters in the Volterra Model are typically calculated based on the mean square error criteria, then in this paper we compare alternatives to reduce the complexity, number of operations, and the time in the linearization of PA through DPD measured with the OFDM signal. The simulation results show that the VSS algorithm is faster and e_ective to calculate the parameters in the Volterra model.
We introduce a novel methodology for analysing well known classes of adaptive algorithms. Combining recent developments concerning geometric ergodicity of stationary Markov processes and long existing results from the...
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We introduce a novel methodology for analysing well known classes of adaptive algorithms. Combining recent developments concerning geometric ergodicity of stationary Markov processes and long existing results from the theory of Perturbations of Linear Operators we first study the behaviour and convergence properties of a class of products of random matrices, this is turn allows for the analysis of the first and second order statistics of adaptive algorithms without the need of any restrictive conditions imposed on the data las essential boundedness), Efficient estimates of the convergence rate of adaptive algorithms during the initial transient phase are also presented. These estimates do not rely on the unrealistic Independence Assumption as it is commonly the case in existing literature. (C) 1998 John Wiley & Sons, Ltd.
Constant Modulus Algorithm (CMA) is a method that has been widely known as blind adaptive beamforming because it requires no knowledge about the signal except that the transmitted signal waveform has a constant envelo...
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Constant Modulus Algorithm (CMA) is a method that has been widely known as blind adaptive beamforming because it requires no knowledge about the signal except that the transmitted signal waveform has a constant envelope. Although CMA has the merit of this blind operation, it possesses problems in its convergence property. In this paper, problems that are inherent to this, algorithm is resolved using a combination of CMA and another major adaptive algorithm SR II (Sample Matrix Inversion). The idea is to use SR II to determine the initial weights for CMA operation. Although the benefit of CMA being a blind algorithm is not fully taken advantage of, good aspects of both SMI and CMA carl be introduced. By using this approach, two major problems in convergence properties of CMA can be solved. One of these problems is the reliability and this relates to the convergence performance ill certain cases. When the interfering signal is stronger than the desired signal, the algorithm tends to come up with the wrong solution by capturing the interfering signal which has the stronger power. Also, the convergence time? of this algorithm is slow, limiting its application in dynamic environment, although the slow convergence time of CMA has been studied previously and several methods hale been proposed to overcome this defect. Using the proposed method. the deterioration due to both of these problems can be mitigated. Simulation results are shown to confirm the theory. Furthermore, evaluations are done concerning the fading characteristics. It is also confirmed from the simulation that the tracking performance of this method can be regarded as sufficient in personal mobile communication.
This workshop seeks to integrate results from different domains of computer science, computational science, and mathematics. We welcome simulation papers, either hard simulations using finite element or finite differe...
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This workshop seeks to integrate results from different domains of computer science, computational science, and mathematics. We welcome simulation papers, either hard simulations using finite element or finite difference methods, or soft simulations by means of evolutionary computations, and related methods. The workshop focuses on simulations performed by using (a) agent-oriented systems; or (b) adaptive algorithms. Simulations performed by other kind of systems are also welcome. An agent-oriented system seems are attractive tools useful for numerous domains of applications. adaptive algorithms significantly decrease on the computational cost by investing computational resources when needed by the problem.
First-order optimization algorithms play a major role in large scale machine learning. A new class of methods, called adaptive algorithms, were recently introduced to adjust iteratively the learning rate for each coor...
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First-order optimization algorithms play a major role in large scale machine learning. A new class of methods, called adaptive algorithms, were recently introduced to adjust iteratively the learning rate for each coordinate. Despite great practical success in deep learning, their behavior and performance on more general loss functions are not well understood. In this paper, we derive a non-autonomous system of differential equations, which is the continuous time limit of adaptive optimization methods. We study the convergence of its trajectories and give conditions under which the differential system, underlying all adaptive algorithms, is suitable for optimization. We discuss convergence to a critical point in the non-convex case and give conditions for the dynamics to avoid saddle points and local maxima. For convex loss function, we introduce a suitable Lyapunov functional which allows us to study its rate of convergence. Several other properties of both the continuous and discrete systems are briefly discussed. The differential system studied in the paper is general enough to encompass many other classical algorithms (such as HEAVY BALL and NESTEROV's accelerated method) and allow us to recover several known results for these algorithms.
This paper presents a new learning algorithm for multi-layered neural networks and its neural implementation. By formulating the learning problem in multi-layered neural networks as the set of least-squares equations ...
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This paper presents a new learning algorithm for multi-layered neural networks and its neural implementation. By formulating the learning problem in multi-layered neural networks as the set of least-squares equations with interconnected variables, a multi-stage adaptive algorithm is designed to solve the equations. Moreover, the Hopfield networks can be adopted to implement the proposed algorithm. The proposed algorithm may be seen as a 'Learning Algorithm for Neural Networks by Neural Networks' and provides an alternative for training neural networks in some applications.
In this article, we study split common fixed point problems with multiple output sets in real Hilbert spaces. In order to solve this problem, we present three new self-adaptive algorithms. We establish weak and strong...
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In this paper, fixed point problems of pseudocontractive operators are investigated. We present a self-adaptive algorithm for finding a fixed point of a Lipschitz pseudocontractive operator in a real Hilbert space. Ou...
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Agent-oriented system seems to be the attractive tool useful for numerous domains of applications. It gives the ability to integrate results of different domains of computer science and constitutes the powerful tool f...
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Agent-oriented system seems to be the attractive tool useful for numerous domains of applications. It gives the ability to integrate results of different domains of computer science and constitutes the powerful tool for solving various problems. The new approach to the simulation particularly in bio-computing and adaptive systems is possible to be developed mainly due to the results of the interactions among intelligent agents in complex adaptive systems. The modern agent-oriented paradigm allows understand the adaptive (e.g. finite element/finite difference) algorithms as a collection of interacting agents making local decision about refinements. This workshop focuses on the various applications of agent-oriented and adaptive systems and the roles of interactions of intelligent agents to build intelligent systems for miscellaneous, interesting applications.
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