Least Minimum Mean Square Error Research Articles

Channel Estimation and Equalization Using FIM for MIMO-OFDM on Doubly Selective Faded Noisy Channels

Orthogonal frequency division multiplexing (OFDM) plays an important role in wireless communication due to its high transmission rate. Information is conveyed across spatial and temporal dimensions through the space-time shift keying (STSK) technique which is basically used to handle multiplexing diversity and gains. On the other hand, index modulation integrated OFDM not only communicates information through conventional signal constellations as in classical OFDM, but also through indexes of the subcarriers. In index modulation, the subcarriers are transmitted over a particular index and can be implemented effectively. The active indices are selected and further information bits transmitted. In this paper, to handle such limitations, the deep neural network (DNN) has been proposed for end-to-end performance. Under the noisy and faded channel scenario, channel state information must be acquired to recover the transmitted signal correctly. To evaluate the channel distortion level, a deep learning model is trained offline from simulated data and then applied to online data to estimate and recover the channel state as well as the transmitted signal, respectively, in comparison to the traditional least minimum mean square error (LMMSE) channel estimation technique. The analysis results demonstrate superiority over the conventional LMMSE for channel estimation and signal detection in wireless communications with complex channel distortion and interference. The mean square error (MSE) is evaluated for carrying out performance information in each subcarrier block and to reduce the detector error rate.

A Comprehensive Review on Channel Estimation in OFDM System

In wireless communication, orthogonal frequency division multiplexing (OFDM) plays a major role because of its high transmission rate. Channel estimation and tracking have many different techniques available in OFDM systems. Among them, the most important techniques are least square (LS) and minimum mean square error (MMSE). In least square channel estimation method, the process is simple but the major drawback is it has very high mean square error. Whereas, the performance of MMSE is superior to LS in low SNR, its main problem is it has high computational complexity. If the error is reduced to a very low value, then an exact signal will be received. In this paper an extensive review on different channel estimation methods used in MIMO-OFDM like pilot based, least square (LS) and minimum mean square error method (MMSE) and least minimum mean square error (LMMSE) methods and also other channel estimation methods used in MIMO-OFDM are discussed.

Uplink spectral efficiency analysis of multi-cell multi-user massive MIMO over correlated Ricean channel

In this paper, the performance of uplink spectral efficiency in massive multiple input multiple output (MIMO) over spacially correlated Ricean fading channel is presented. The maximum ratio combining (MRC) receiver is employed at the base station (BS) for two different methods of channel estimation. The first method is based on pilot-assisted least minimum mean square error (LMMSE) estimation, the second one is based on line-of-sight (LOS) part. The respective analytical expressions of uplink data rate are given for these two methods. Due to the existence of pilot contamination, the uplink data rate of pilot-assisted LMMSE estimation method approaches to a finite value (we name it as asymptotic rate in the paper) when the BS antenna number is very large. However, the data rate of LOS method goes linearly with the number of BS antennas. The expression of the uplink rate of LOS method also show that for Ricean channel, the spacial correlation between the BS antennas may not only decrease the rate, but also increase the rate, which depends on the locations of the users. This conclusion explains why the spacial correlation may increase, rather than decrease the data rate of pilot-assisted LMMSE. We also discuss the power scaling law of the two methods, and the asymptotic expressions of the two methods are the same and both independent of the antenna correlation.

Maximum a posteriori channel estimation for cooperative diversity orthogonal frequency-division multiplexing systems in amplify-and-forward mode

Transmit diversity-orthogonal frequency-division multiplexing (OFDM) systems have been proposed to mitigate the detrimental effects of channel fading. However, owing to the space and power limitations, the use of multiple transmit antennas is not practical in certain wireless devices, such as portable terminals and wireless sensors. Therefore cooperation among users at the physical layer has been proposed recently. Here, space-time block coded in amplify-and-forward (AF) relaying mode has been proposed as cooperative diversity for OFDM systems (CO-OFDM) in the presence of perfect channel-state information. Then, the channel estimation techniques for CO-OFDM systems in AF mode based on pilot symbols are investigated over frequency-selective channels. In particular, expectation-maximisation (EM) based maximum a posteriori (MAP) channel estimation is developed and compared with comp-type pilot-aided channel estimation (PACE) based the maximum likelihood (ML) estimator and the least minimum mean-square error (LMMSE) channel estimation techniques for CO-OFDM systems. To overcome the drawback owing to the receiver complexity, the Karhunen-Loeve expansion with the optimal truncation property is also considered. Simulation results that demonstrate the overall performance advantage of the EM-MAP based receiver over the PACE-ML and PACE-LMMSE based receivers are presented.

A New Linear Group-Wise SIC Multiuser Detector

In this work, we introduce a new linear group-wise SIC multi-user detector that can converge to either the decorrelator or the least minimum mean-square error (LMMSE) detector. We study the convergence behavior of the proposed scheme and show that the latter is equivalent to the block Gauss-Seidel iterative method if the group-detection scheme used is the decorrelator detector. Moreover, we prove that the latter is convergent if the group-detection matrix is positive definite. Our simulation results are in excellent agreement with the proposed theory

Generalized phase space projection for nonlinear noise reduction

Improved phase space projection methods, adapted from related work in the linear signal processing field based on subspace decomposition, are presented for application to the problem of additive noise reduction in the context of phase space analysis. These methods improve upon existing methods such as Broomhead–King singular spectrum analysis projection by minimizing overall signal distortion subject to constraints on the residual error, rather than using a direct least-squares fit. This results in a range of weighted projections which estimate and compensate for the portion of the principal component's singular values corresponding to noise rather than signal energy, and which include least-squares (LS) and least minimum mean square error (LMMSE) as subcases. The nature of phase space covariance, the key element in construction of the projection matrix, is examined across global phase spaces as well as within local neighborhood regions. The resulting algorithm, illustrated on a noisy Henon map as well as on the task of speech enhancement, is applicable to a wide variety of nonlinear noise reduction tasks.