Improving the performance of the LMS and RLS algorithms for adaptive equalizer

In this paper, we present the experiment results of three adaptive equalization algorithms: least-mean-square (LMS) algorithm, discrete cosine transform-least mean square (DCT-LMS) algorithm, and recursive least square (RLS) algorithm. Based on the experiments, we obtained that the convergence rate of LMS is slow; the convergence rate of RLS is great faster while the computational price is expensive; the performance of that two parameters of DCT-LMS are between the previous two algorithms, but still not good enough. Therefore we will propose an algorithm based on H2 in a coming paper to solve the problems.

Application of fast and robust equalization in communication technology

In this paper, the authors explore the potential of several popular equalization techniques while overcoming their disadvantages. First, extensive literature survey on equalization is conducted. The focus is on popular linear equalization algorithms such as the conventional least-mean-square (LMS) algorithm , The recursive least-squares (RLS) algorithm, the filtered-X LMS algorithm and their development. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H2 optimal initialization is proposed.

Organizing dualities and strategizing for change

This paper examines the role of organizing forms in strategizing for change. It argues that a duality approach aids in understanding how organizing influences and leverages strategizing for change. Three propositions are presented.

First, organizing is strategizing. This represents the interaction between organizing and strategizing, where organizing forms enable and constrain strategic change. Second, tension in forms of organizing can be the source of strategic advantage. The main challenge is to develop systems that function best in tension, delivering both efficiency and innovation. A dualities approach emphasizes the need not only to hold fast to routines but also to attempt to subvert them with innovations. It is this combination that facilitates strategic change. Third, heterogeneity in organizing forms offers strategic adaptability by providing increased opportunities for innovation.

This paper concludes that despite the appeal of calls for organizations to become capable of constant change, the advantages of flexibility are compromised by the disadvantages of instability and uncertainty. Furthermore, the study of organizing forms reveals that change is continuous at the micro level but discontinuous at the macro level. Thus the relationship between organizing and strategizing can subsequently be non-linear and recursive, with strategic advantages achieved through tension and heterogeneity in organizing.

Forecasting inflation, industrial output and exchange rates : a template study for India

This paper reports results from a forecasting study for inflation, industrial output and exchange rates for India. We cannot reject the null hypothesis for linearity for all series used except for the growth rate of the foreign exchange series and our analysis is based on linear models, ARIMA and bivariate transfer functions and restricted VAR. Forecasting performance is evaluated using the models’ root mean-squared error differences and Theil’s inequality coefficients from recursive origin static, fixed origin dynamic and rolling origin dynamic forecasts. For models based on weekly data, based on RMSEs, we find that the bivariate models improve upon the forecasts of the ARIMA model while for models based on monthly data the ARIMA model has almost always better performance. In choosing between the two bivariate models on the basis of RMSEs, our overall results tend to support the use of a restricted VAR, as this model had the best forecasting performance more frequently than the transfer function model.

Intersectoral size differences and migration: Kuznets revisited

The empirical evidence on the Kuznets hypothesis ranges from positive or negative support to insignificant relationships. Most studies typically try this hypothesis in domains different than the one conceived by Kuznets, which pertains to the industrialization-led urbanization (i.e., significant rural-urban migration) phase of societies. In this paper, we offer a specific channel on Kuznets' hypothesis in his suggested domain. First, we establish theoretically that intersectoral urban-rural size differences result in an intersectoral income inequality, increasing the national inequality. This, in turn, prompts an intersectoral migration, which works as an equilibriating mechanism in the economy, decreasing the inequality in due course. We then successfully test the predictions of the model. The theoretical predictions yield a recursive triangular system, in which we test, i) how the sectoral size differences influence the agricultural income, ii) how a change in agricultural income acts on migration, and iii) what happens to the income distribution as a result of migration. We find a very strong support for the theoretical predictions and the Kuznets hypothesis in its own domain.

Optimal multisensor data fusion for linear systems with missing measurements

Multisensor data fusion has attracted a lot of research in recent years. It has been widely used in many applications especially military applications for target tracking and identification. In this paper, we will handle the multisensor data fusion problem for systems suffering from the possibility of missing measurements. We present the optimal recursive fusion filter for measurements obtained from two sensors subject to random intermittent measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. Illustration example shows the effectiveness of the proposed filter in the measurements loss case compared to the available optimal linear fusion methods.

Position estimation and tracking of an autonomous mobile sensor using received signal strength

In this paper, an algorithm for approximating the path of a moving autonomous mobile sensor with an unknown position location using Received Signal Strength (RSS) measurements is proposed. Using a Least Squares (LS) estimation method as an input, a Maximum-Likelihood (ML) approach is used to determine the location of the unknown mobile sensor. For the mobile sensor case, as the sensor changes position the characteristics of the RSS measurements also change; therefore the proposed method adapts the RSS measurement model by dynamically changing the pass loss value alpha to aid in position estimation. Secondly, a Recursive Least-Squares (RLS) algorithm is used to estimate the path of a moving mobile sensor using the Maximum-Likelihood position estimation as an input. The performance of the proposed algorithm is evaluated via simulation and it is shown that this method can accurately determine the position of the mobile sensor, and can efficiently track the position of the mobile sensor during motion.

Power control in CDMA systems based on channel identification

Power control design is a critical aspect of CDMA cellular systems design. This paper develops an adaptive power controller design method for CDMA systems. The key to the power control design is on the recursive identification of the underlying wireless stochastic channel model parameters. The identification process guarantees the power controller to work well for systems in unknown or time varying network environment.

Comments on "Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties"

This note points out that the time complexity of the main multiple-surface sliding control (MSSC) algorithm in Huang and Chen [Huang, A. C. & Chen, Y. C. (2004). Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties. Automatica, 40(11), 1939-1945] is O(2^n). Here, we propose a simplified recursive design MSSC algorithm with time complexity O(n), and, using mathematical induction, we show that this algorithm agrees with this MSSC law.

A generalized data windowing scheme for adaptive conjugate gradient algorithms

The performance of the modified adaptive conjugate gradient (CG) algorithms based on the iterative CG method for adaptive filtering is highly related to the ways of estimating the correlation matrix and the cross-correlation vector. The existing approaches of implementing the CG algorithms using the data windows of exponential form or sliding form result in either loss of convergence or increase in misadjustment. This paper presents and analyzes a new approach to the implementation of the CG algorithms for adaptive filtering by using a generalized data windowing scheme. For the new modified CG algorithms, we show that the convergence speed is accelerated, the misadjustment and tracking capability comparable to those of the recursive least squares (RLS) algorithm are achieved. Computer simulations demonstrated in the framework of linear system modeling problem show the improvements of the new modifications.

Adaptive fast finite time control of a class of uncertain nonlinear systems

This paper concerns the adaptive fast finite time control of a class of nonlinear uncertain systems of which the upper bounds of the system uncertainties are unknown. By using the fast non-smooth control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite time stability of the closed-loop system. It is proved that the control input is bounded, and a simulation example is given to illustrate the effectiveness of the theoretical results.

Does monetary policy matter in a less developed economy? Evidence from Bangladesh

This paper explores the role of monetary policy in the context of a less developed economy. Monetary transmission mechanisms in less developed economies can be quite different from an industrialized economy, as unlike industrialized countries, these economies are characterized by the small size of organized financial markets, limited substitutability between money and other assets and weak fiscal and monetary institutions. We utilize the Structural VAR approach to analyze the monetary transmission process and impacts of monetary policy on different macro variables in Bangladesh. Monetary policy shocks are identified using non-recursive contemporaneous restrictions, which are based on the Central Bank's reaction function and the structure of the economy. We found strong evidence for the interest rate channel of monetary policy in Bangladesh. Our findings indicate that monetary policy shocks are important sources of fluctuations in the rate of interest, output and prices. Expansionary monetary policies are found to be harmful for achieving price stability in Bangladesh, as they not only increase the prices permanently, but also make the price level more volatile. We also found the evidence of a long lasting effect of monetary policy on output, which suggests that contractionary policy measures may create sustained recession in Bangladesh.

Optimal linear data fusion for systems with missing measurements

In this paper, we provide the optimal data fusion filter for linear systems suffering from possible missing measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. The data fusion process is made on the raw data provided by two sensors  observing the same entity. Each of the sensors is losing the measurements in its own data loss rate. The data fusion filter is provided in a recursive form for ease of implementation in real-world applications.

Fast finite-time consensus of a class of high-order uncertain nonlinear systems

This paper poses and solves a new problem of consensus control where the task is to make the fixed-topology multi-agent network, with each agent described by an uncertain nonlinear system in chained form, to reach consensus in a fast finite time. Our development starts with a set of new sliding mode surfaces. It is proven that, on these sliding mode surfaces, consensus can be achieved if the communication graph has the proposed directed spanning tree. Next, we introduce the multi-surface sliding mode control to drive the sliding variables to the sliding mode surfaces in a fast finite time. The control Lyapunov function for fast finite time stability, motivated by the fast terminal sliding mode control, is used to prove the reachability of the sliding mode surface. A recursive design procedure is provided, which guarantees the boundedness of the control input.

Channel estimation scheme for 3.9G wireless communication systems using RLS algorithm

Main challenges for a terminal implementation are efficient realization of the receiver, especially for channel estimation (CE) and equalization. In this paper, training based recursive least square (RLS) channel estimator technique is presented for a long term evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) wireless communication system. This CE scheme uses adaptive RLS estimator which is able to update parameters of the estimator continuously, so that knowledge of channel and noise statistics are not required. Simulation results show that the RLS CE scheme with 500 Hz Doppler frequency has 3 dB better performances compared with 1.5 kHz Doppler frequency.