Presence Of Estimation Errors Research Articles

Uplink single-carrier frequency division multiple access system with joint equalisation and carrier frequency offsets compensation

Similar to the orthogonal frequency division multiple access (OFDMA) system, the single-carrier frequency division multiple access (SC-FDMA) system also suffers from frequency mismatches between the transmitter and the receiver. As a result, in this system, the carrier frequency offsets (CFOs) disrupt the orthogonality between subcarriers and give rise to inter-carrier interference (ICI) and multiple access interference (MAI) among users. The authors present a new minimum mean square error (MMSE) equaliser, which jointly performs equalisation and carrier frequency offsets (CFOs) compensation. The mathematical expression of this equaliser has been derived taking into account the MAI and the channel noise. A low complexity implementation of the proposed equalisation scheme using a banded matrix approximation is presented here. From the obtained simulation results, the proposed equalisation scheme is able to enhance the performance of the SC-FDMA system, even in the presence of estimation errors.

Design of Optimized Convolutional and Serially Concatenated Convolutional Codes in the Presence of A-priori Information

In this paper, we focus on the design of optimized binary convolutional codes (CCs) and serially concatenated convolutional codes (SCCCs) in the presence of a-priori information (API) at the receiver. For large signal-to-noise ratios (SNRs), we first propose a CC design criterion based on the minimization of a union bound on the bit error probability (BEP). In this case, relevant performance gains, with respect to previously proposed CCs, are obtained. These gains persist even in the presence of estimation errors on the API. Then, we apply the same union bound-based design criterion to SCCCs. Since the BEP of SCCCs is characterized by a typical waterfall shape, the proposed union bound-based design criterion is accurate only at large SNR, to estimate the BEP floor. In order to complement this analysis, we propose a density evolution-based approach to optimize the SCCC design in terms of minimization of the SNR of the knee of the BEP curve. The obtained simulation results show substantial gains with respect to previously proposed parallel concatenated convolutional coding (PCCCing) schemes optimized under the assumption of no API at the decoder. Moreover, in the presence of strong API the proposed SCCCs allow to approach the Shannon limit (SL) more than any previously proposed turbo coding scheme.

A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms

We provide a general framework for finding portfolios that perform well out-of-sample in the presence of estimation error. This framework relies on solving the traditional minimum-variance problem but subject to the additional constraint that the norm of the portfolio-weight vector be smaller than a given threshold. We show that our framework nests as special cases the shrinkage approaches of Jagannathan and Ma (Jagannathan, R., T. Ma. 2003. Risk reduction in large portfolios: Why imposing the wrong constraints helps. J. Finance 58 1651–1684) and Ledoit and Wolf (Ledoit, O., M. Wolf. 2003. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empirical Finance 10 603–621, and Ledoit, O., M. Wolf. 2004. A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88 365–411) and the 1/N portfolio studied in DeMiguel et al. (DeMiguel, V., L. Garlappi, R. Uppal. 2009. Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Rev. Financial Stud. 22 1915–1953). We also use our framework to propose several new portfolio strategies. For the proposed portfolios, we provide a moment-shrinkage interpretation and a Bayesian interpretation where the investor has a prior belief on portfolio weights rather than on moments of asset returns. Finally, we compare empirically the out-of-sample performance of the new portfolios we propose to 10 strategies in the literature across five data sets. We find that the norm-constrained portfolios often have a higher Sharpe ratio than the portfolio strategies in Jagannathan and Ma (2003), Ledoit and Wolf (2003, 2004), the 1/N portfolio, and other strategies in the literature, such as factor portfolios.

Optimal Portfolio Choice with Dynamic Asymmetric Correlations and Transaction Constraints

This paper develops a framework for constructing portfolios with superior out-of-sample performance in the presence of estimation errors. Our framework relies on solving the classical mean-variance problem with dynamic portfolio rebalancing at a comparatively-high frequency level. With the employment of A-DCC GARCH model, we found that the usage of turnover constraints will tend to enhance the performance of the portfolios sufficiently high to overcome transaction costs in practice. For a long-only optimal portfolio based on a linear combination of two different strategies we find a return exceeding 51% per annual with annual volatility equal to 35% over the 1998-2007 period. We argue that the advantage of our framework comes from the mean-reverting nature of the stock market and the impact of the estimation errors in high frequency level. Our works indicate that one can successfully move from ordinary monthly or weekly adjusting strategies to high frequency and dynamic asset management without the significant increase of transaction costs.

Improving Performance By Constraining Portfolio Norms: A Generalized Approach to Portfolio Optimization

In this paper, we provide a general framework for determining the portfolio that has superior out-of-sample performance even in the presence of estimation error. This general framework relies on solving the traditional minimum-variance problem (based on the sample covariance matrix) but subject to the additional constraint that the norm of the portfolio weight vector must be smaller than a given threshold. We show that our general framework nests as special cases the shrinkage approaches of Jagannathan and Ma (2003) and Ledoit and Wolf (2004b), and the 1/N policy studied in DeMiguel, Garlappi, and Uppal (2007), and that all these policies can be interpreted as those of a Bayesian investor who has a certain prior belief on portfolio weights. We also use our general framework to propose several new portfolio strategies. Finally, we compare empirically (in terms of portfolio variance, Sharpe ratio, and turnover), the out-of-sample performance of the new polices we propose to ten strategies in the existing literature across ten datasets. We find that the new policies we propose can outperform the policies proposed in Jagannathan and Ma (2003) and Ledoit and Wolf (2004b), the 1/N policy evaluated in DeMiguel, Garlappi, and Uppal (2007), and also other strategies in the literature such as Brandt, Santa-Clara, and Valkanov (2005).

A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms

In this paper, we provide a general nonlinear programming framework for identifying portfolios that have superior out-of-sample performance in the presence of estimation error. This general framework relies on solving the traditional minimum-variance problem but subject to the additional constraint that the norm of the portfolio-weight vector be smaller than a given threshold. We show that our general framework nests as special cases several wellknown (shrinkage and constrained) approaches considered in the literature. We also use our general framework to propose several new portfolio strategies that we term partial minimum-variance portfolios. These portfolios are obtained by applying the classical conjugate-gradient method to solve the minimum-variance problem. We compare empirically (in terms of portfolio variance, Sharpe ratio, and turnover), the out-of-sample performance of the new portfolios to various wellknown strategies across several datasets. We find that the norm-constrained portfolios we propose outperform shortsale-constrained portfolio approaches, shrinkage approaches, the 1/N portfolio, factor portfolios, and also other strategies considered in the literature.

A BPSK decision-feedback equalization method robust to phase and timing errors

We present a new approach to binary phase shift keying (BPSK) channel equalization that uses the aggregating method of bagging to improve its ability to handle noise and estimation errors. Bagging forms a single equalized output from an average of N equalizers that are run on independently permuted sets of the received signal. We show in simulations that bagging offers improvement compared to the traditional adaptive decision-feedback equalizers (DFEs), especially in the presence of estimation errors caused by phase and timing recovery.

Precision Spacecraft Pointing Using Single-Gimbal Control Moment Gyroscopes with Disturbance

Whereas previous work has shown that gain stabilization methods in the attitude control of spacecraft can mitigate external disturbances of a e xed frequency, recent work in cases where the frequency of the disturbance is time variant is summarized here.Specie cally, theapplication ofgain stabilization in the form of a variableperiodic disturbancerejection e lter is shown to bea viablemethod ofmitigating these disturbances. To usethis e lter forthe time-variant case, the character of the source of the disturbance needs to be known a priori with some accuracy. An implementation for a spacecraft steered by single-gimbal control moment gyroscopes where the disturbance is related to the gimbal rate is demonstrated. In addition, performance of the subject e lter is demonstrated in the presence of estimation error and phase shift.

Design-time simulation of a large-scale, distributed object system

We present a case study in using simulation at design time to predict the performance and scalability properties of a large-scale distributed object system. The system, called Consul, is a network management system designing to support hundreds of operators managing millions of network devices. It is essential that a system such as Consul be designed with performance and scalability in mind, but due to Consul's complexity and scale, it is hard to reason about performance and scalability using ad hoc techniques. We built a simulation of Consul's design to guide the design process by enabling performanace and scalability analysis of various design alternatives. A major challenge in doing design-time simulation is that many parameters for the simulation are based on estimates rather than measurements. We developed analysis methods that derive conclusions that are valid in the presence of estimation errors. In this article, we describe our scalability analysis method for design simulations of distributed object systems. The main idea is to use relative and comparative reasoning to analyze design alternatives and compare transaction behaviors. We demonstrate the analysis approach by describing its application to Consul.