In this letter, we analyze performances of a frequency offset estimation based on the maximum likelihood criterion and provide a theoretical proof that the mean squared error of the estimation grows with increase in the offset. Moreover, we propose a new iterative offset estimation method based on the analysis. By computer simulations, we show that the proposed estimator can achieve the lowest estimation error after a few iterations.

In this paper, we propose a method for color error diffusion based on the Neugebauer model for color halftone printing. The Neugebauer model expresses an arbitrary color as a trilinear interpolation of basic colors. The proposed method quantizes the color of each pixel to a basic color which minimizes an accumulated quantization error, and the quantization error is diffused to the ratios of basic colors in subsequent pixels. Experimental results show that the proposed method outperforms conventional color error diffusion methods including separable method in terms of eye model-based mean squared error.

In this letter, parallel orthogonal matching pursuit (POMP) is proposed to supplement orthogonal matching pursuit (OMP) which has been widely used as a greedy algorithm for sparse signal recovery. Empirical simulations show that POMP outperforms the existing sparse signal recovery algorithms including OMP, compressive sampling matching pursuit (CoSaMP), and linear programming (LP) in terms of the exact recovery ratio (ERR) for the sparse pattern and the mean-squared error (MSE) between the estimated signal and the original signal.

In time-varying channels, the channel state information available at the transmitter (CSIT) is outdated due to inherent time delay between the uplink channel estimation and the downlink data transmission in TDD systems. In this letter, we propose an iterative precoding method and a linear decoding method which are both based on minimum mean-squared error (MMSE) criteria to mitigate the interference among data streams and users created by outdated CSIT for multiuser MIMO downlink systems. Analysis and simulation results show that the proposed method can effectively reduce the impairment of the outdated CSIT and improve the system capacity.

We investigated the suppression of multiuser interference in uplink multicarrier CDMA systems using the minimum mean squared error combining (MMSEC) method. In MMSEC, many pilot symbols are required to converge the weight vectors, and if we use just a few pilot symbols, the performance cannot be improved very much. We therefore developed a method for calculating weight vectors for MMSEC that uses just a few pilot symbols. The impulse responses of all users are first estimated using the pilot symbols in the time domain and modulated by a discrete Fourier transform. Next, the correlation matrices and correlation vectors are estimated from the impulse responses and the spreading codes of all users. Finally, the weight vectors that are obtained from the correlation matrices and correlation vectors are multiplied by the received signal to suppress the multiuser interference. The results of computer simulations indicated that the bit-error-ratio performance obtained using this method was better than that obtained when using the conventional fading compensation scheme or when using conventional MMSEC with the recursive least squares algorithm.

Most of the image restoration filters proposed so far include parameters that control the restoration properties. For bringing out the optimal restoration performance, these parameters should be determined so as to minimize a certain error measure such as the mean squared error (MSE) between the restored image and original image. However, this is not generally possible since the unknown original image itself is required for evaluating MSE. In this paper, we derive an estimator of MSE called the subspace information criterion (SIC), and propose determining the parameter values so that SIC is minimized. For any linear filter, SIC gives an unbiased estimate of the expected MSE over the noise. Therefore, the proposed method is valid for any linear filter. Computer simulations with the moving-average filter demonstrate that SIC gives a very accurate estimate of MSE in various situations, and the proposed procedure actually gives the optimal parameter values that minimize MSE.