Linear Minimum Mean Square Error Method Research Articles

A DFT-based Low Complexity LMMSE Channel Estimation Technique for OFDM Systems

The linear minimum mean square error (LMMSE) channel estimation technique is often employed in orthogonal frequency division multiplexing (OFDM) systems because of its optimal performance in the mean square error (MSE) performance. However, the LMMSE method requires cubic complexity of order O(N 3 p ), where Np is the number of pilot subcarriers. To reduce the computational complexity, a discrete Fourier transform (DFT) based LMMSE method is proposed in this paper for OFDM systems in the frequency selective channel. To validate the proposed method, the closed form mean square error (MSE) expression is also derived. Finally, a computer simulation is carried out to compare the performance of the proposed LMMSE method with the classical LS and LMMSE methods in terms of bit error rate (BER) and computational complexity. Results of the simulation show that the proposed LMMSE method achieves exactly the same performance as the conventional LMMSE method, with much lower computational complexity.

A low-complexity orthogonal time frequency space modulation method for underwater acoustic communication

Compared with the orthogonal frequency division multiplexing (OFDM) modulation, the orthogonal time frequency space(OTFS) modulation has a lower peak-to-average power ratio. It can effectively resist the time selective fading caused by the Doppler effect and has significant performance advantages over doubly dispersive channels. However, the conventional OTFS linear minimum mean square error (LMMSE) method has a high complexity and is not easy to process in real time. In order to solve this problem, we propose a low-complexity equalization algorithm with infinite norm constraints based on the optimal coordinate reduction. The equalization algorithm not only obtains the optimal solution through a certain number of iterations and avoids direct matrix inversion but also equalizes infinite norm constraints to improve the symbol detection performance gains. At the same time, the OTFS delay-Doppler channel matrix we utilize is sparse and the two-norm squares of each column vector equally reduces the complexity of optimal coordinate descent. Finally, the simulation in the underwater acoustic communication scenario we designed verify the effectiveness of the proposed equalization algorithm. The simulation results show that the performance of the proposed equalization algorithm is close to that of the LMMSE method, while its low complexity is ensured.

Performance enhancement of audio transmission based on LMMSE method

<p class="Abstract"><span>The research in wireless communication has developed rapidly for the last decades as a result of raising the demand for efficient data transmission with more security and accuracy. This paper proposed a system based on the special multiplexing (SM) technique and linear minimum mean square error (LMMSE) detection method with the assistance of the hamming code as well as the interleaving techniques for a better enhanced performance of an audio transmission. Moreover, the comparison was done between the two systems for different antenna configurations and with the presence of two types of modulation: binary phase shift key and quateradure phase shift key. These systems are employed by Matlab simulation to show significant results in terms of enhancing the Rayleigh fading channel capacity, bit error rate (BER) and security as well as in recovering the transmitting audio signals. Each system has advantages than the others in one performance term respect to the other terms. The Simulation results have provided to prove and discuss our analysis.</span></p>

A Low Complexity Optimal LMMSE Channel Estimator for OFDM System

Linear minimum mean square error (LMMSE) is the optimal channel estimator in the mean square error (MSE) perspective, however, it requires matrix inversion with cubic complexity. In this paper, by exploiting the circulant property of the channel frequency autocorrelation matrix RHH, an efficient LMMSE channel estimation method has been proposed for orthogonal frequency division multiplexing (OFDM) based on fast Fourier transformation (FFT) and circular convolution theorem. Finally, the computer simulation is carried out to compare the proposed LMMSE method with the classical LS and LMMSE methods in terms of performance measure and computational complexity. The simulation results show that the proposed LMMSE estimator achieves exactly same performance as conventional LMMSE estimator with much lower computational complexity.

An optimized non-local LMMSE approach for speckle noise reduction of medical ultrasound images

In this paper, a modified linear-minimum-mean-square-error (LMMSE)-based estimator is presented to reduce speckle noise in ultrasound (US) medical images. In order to significantly improve the performance of the LMMSE estimator, we consider the data redundancy feature which naturally exists in the US images. Since the classical LMMSE method, due to the use of local statistics, cannot perform well in areas where the intensity variation is high, we exploit the similarity between pixels to resolve this problem. In this regards, by using characteristics of the second order local statistics and Pearson distance, an optimum set of similar pixels is selected to be used in the proposed LMMSE-based estimator. Therefore, a good balance between maintaining small details and reducing speckle noise in different regions of US images can be achieved. Quantitative and qualitative results on synthetic and real US data demonstrate that the proposed method yields competitive results in despeckling process compared to the state-of-the-art methods.

An Optimized LMMSE Based Method for 3D MRI Denoising.

Post-acquisition denoising of magnetic resonance (MR) images is an important step to improve any quantitative measurement of the acquired data. In this paper, assuming a Rician noise model, a new filtering method based on the linear minimum mean square error (LMMSE) estimation is introduced, which employs the self-similarity property of the MR data to restore the noise-less signal. This method takes into account the structural characteristics of images and the Bayesian mean square error (Bmse) of the estimator to address the denoising problem. In general, a twofold data processing approach is developed; first, the noisy MR data is processed using a patch-based L(2)-norm similarity measure to provide the primary set of samples required for the estimation process. Afterwards, the Bmse of the estimator is derived as the optimization function to analyze the pre-selected samples and minimize the error between the estimated and the underlying signal. Compared to the LMMSE method and also its recently proposed SNR-adapted realization (SNLMMSE), the optimized way of choosing the samples together with the automatic adjustment of the filtering parameters lead to a more robust estimation performance with our approach. Experimental results show the competitive performance of the proposed method in comparison with related state-of-the-art methods.

Network Coded Multihop Wireless Communication Networks: Channel Estimation and Training Design

User-cooperation-based multihop wireless communication networks (MH-WCNs) as the key communication technological component of mobile social networks (MSNs) can be exploited to enhance data rates and extend coverage. As one of the most promising and efficient user cooperation techniques, network coding can increase the potential cooperation performance gains among selfishly driven users in MSNs. To take full advantages of network coding in MH-WCNs, a network coding transmission strategy and its corresponding channel estimation technique are studied in this paper. Particularly, a four-hop network coding transmission is presented first, followed by an extension strategy for the arbitrary $2N$ -hop scenario $(N\geq 2)$ . The linear minimum mean square error (LMMSE) and maximum-likelihood (ML) channel estimation methods are designed to improve the transmission quality in MH-WCNs. Closed-form expressions in terms of the mean square error (MSE) for the LMMSE channel estimation method are derived, which allows the design of the optimal training sequence. Unlike the LMMSE method, it is difficult to obtain closed-form MSE expressions for the nonlinear ML channel estimation method. In order to accomplish optimal training sequence design for the ML method, the Cramer–Rao lower bound is employed. Numerical results are provided to corroborate the proposed analysis, and the results demonstrate that the analysis is accurate and the proposed methods are effective.

Time Domain Channel Estimation for OQAM-OFDM Systems: Algorithms and Performance Bounds

In this paper, we first present a general time domain model for the channel estimation in the orthogonal frequency division multiplexing system with offset quadrature amplitude modulation (OQAM-OFDM), and utilize the frequency domain pilots to estimate the time domain channel impulse responses. Different form the conventional methods, there is no specific requirement for the length of the symbol interval compared to the the maximum channel delay spread in the proposed scheme. Furthermore, with the proposed time domain model, the channel statistic information could be utilized to improve the performance of the channel estimation. Then, we propose two channel estimation schemes, i.e., linear minimum mean square error (LMMSE) and weighted least square (WLS), and we also derive their corresponding Bayesian Cramér-Rao Bound (BCRB) and Cramér-Rao Bound (CRB) bounds, respectively. Simulation results demonstrate that the BCRB and CRB bounds could be achieved by the proposed LMMSE and WLS methods, respectively. Moreover, simulation results show that the proposed methods are much robust to the time synchronization error compared to the conventional frequency domain methods, and imply that the pulse shaping filter with waveforms concentrated in the time domain could be employed in OQAM-OFDM systems to improve the channel estimation performance and spectral efficiency.

Signal quality improvement of holographic data storage using adaptive two-dimensional filter

Holographic data storage is being widely studied for the purpose of developing next-generation large optical memories. A prospective use of this type of memory is in building image archives in large-scale data centers. In particular, demand for energy conservation at data centers, and therefore for holographic data storage, is growing. In holographic data storage, interference between bits occurs owing to wave aberration in the optical system, shrinkage of the medium, and crosstalk noise from neighboring holograms during multiplex recording; as a result of the interference, the reproduced image deteriorates and the bit error rate (BER) increases. In this study, to reduce the BER in both off-axis-type recording and coaxial-type recording, a two-dimensional finite impulse response (FIR) filter is applied to a reproduced image that has been recorded by angle multiplex recording and shift multiplex recording. First, for the optimization of the FIR filter coefficients, the linear minimum mean square error (LMMSE) method is applied; this method optimizes the coefficients by reducing the BER. Furthermore, for evaluating the optimization performance of the LMMSE method, the optimization performance is compared with that of the real-coded genetic algorithm (RCGA), which has the capability to search a wide range of coefficients. The optimization by the LMMSE method has been found to be excellent for off-axis-type recording but not for coaxial-type recording. It is speculated that this is because of the brightness irregularity in the reproduced image, resulting from crosstalk. On the other hand, a marked reduction in the BER is observed using the RCGA, despite the brightness irregularity. In this study, the effectiveness of the LMMSE method for signals recorded by coaxial-type recording, in which large brightness irregularity is expected, is examined using automatic gain control (AGC). It is found that the application of AGC reduces the BER even in the case of coaxial-type recording.

A Fast LMMSE Channel Estimation Method for OFDM Systems

A fast linear minimum mean square error (LMMSE) channel estimation method has been proposed for Orthogonal Frequency Division Multiplexing (OFDM) systems. In comparison with the conventional LMMSE channel estimation, the proposed channel estimation method does not require the statistic knowledge of the channel in advance and avoids the inverse operation of a large dimension matrix by using the fast Fourier transform (FFT) operation. Therefore, the computational complexity can be reduced significantly. The normalized mean square errors (NMSEs) of the proposed method and the conventional LMMSE estimation have been derived. Numerical results show that the NMSE of the proposed method is very close to that of the conventional LMMSE method, which is also verified by computer simulation. In addition, computer simulation shows that the performance of the proposed method is almost the same with that of the conventional LMMSE method in terms of bit error rate (BER).