Conventional Linear Minimum Mean Square Error Research Articles

Channel reconstruction for mmWave Massive MIMO systems based on Channel Path Map

For the needs of high data rate and massive device access in current fifth-generation (5G) mobile communication networks and future sixth-generation (6G) mobile communication networks, Millimeter-Wave (mmWave) Massive Multiple-Input Multiple-Output (MIMO) is a crucial technical support. However, the conventional pilot based channel estimation methods are exceedingly time-consuming and substantially degrade system performance when the number of users or antennas is high. This paper proposes a Path Clustering based Channel Reconstruction (PCCR) scheme for channel reconstruction in order to solve this issue. By leveraging channel information (such as angle of arrival, angle of departure, and path gain) at a number of geographical locations that are recorded in the Channel Path Map (CPM), we aim to reconstruct the mmWave channel of any locations of interests. To be specific, the CPM is used to extract the neighborhood propagation path data, and an autonomous determination algorithm is used to find the number of path clusters at the location of interest. The recorded propagation path information is processed by the Equivalent Base Station (EBS) scheme, and the path clustering is accomplished by using the AGglomerative NESting (AGENS) algorithm. In each path cluster, Inverse Distance Weighted (IDW) algorithm is developed to forecast the path information at the user location. With all path information at user location are forecast, the channel can be reconstructed. The anticipated overhead is substantially lower than that of conventional methods because the computational cost of the procedure only depends on the total number of paths at the nearby locations. Ray-tracing based simulations are carried out to demonstrate the performance of the proposed algorithm. The results show that, when applied to a CPM with a spatial location precision of 5 m, the Normalized Mean Squared Error (NMSE) of the proposed algorithm is nearly identical to that of Linear Minimum Mean Square Error (LMMSE) estimation. The actual achievable date rate can match the needs of upcoming wireless communications and is superior over the conventional LMMSE.

Efficient Low-Complexity Optimized Channel Estimating Methods for OFDM-Based Low-Voltage Broadband Power Line Communication Systems

Broadband Power Line Communication (BB-PLC) technology enables data transmission for smart grid applications. Nevertheless, channel equalizers are required in the receiver to estimate and compensate for the nonlinear time-variant impulse response and noise interference effects introduced by the BB-PLC channel. In this paper, we append the Particle Swarm Optimization (PSO) algorithm and its proposed improved version to the commonly used Least-Square (LS) and Linear Minimum Mean Square Error (LMMSE) algorithms to advance four new blocktype pilot-aided hybrid channel estimation algorithms for low-voltage Orthogonal Frequency Division Multiplexing (OFDM)-based BB-PLC systems. Extensive numerical simulation results for four different M-QAM formats (M = 8, 16, 32, 64) show that the proposed algorithms significantly improve the performance of the traditional LS and LMMSE estimators, at least for the parameters of the BB-PLC system studied in this work. In addition, the computational load complexity of the PSO-inspired LMMSE algorithm is lower compared to the conventional LMMSE estimator.

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 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.

A Novel LMMSE-EM Channel Estimator for High Mobility STBC-OFDM System

In time-selective fading channel, the Alamouti orthogonality principle is lost due to the variation of channel from symbol-to-symbol in space–time block-coded orthogonal frequency division multiplexing (STBC-OFDM) system and causes co-channel interference (CCI) effects. To combat the CCI effects, various signal detection schemes have been proposed earlier by assuming that a priori channel state information (CSI) is known to the receiver. However, in practice, the CSI is unknown and therefore accurate estimation of channel is required for efficient signal detection. In this paper, by exploiting circulant properties of the channel frequency response (CFR) autocorrelation matrix [Formula: see text], we propose an efficient low complexity linear-minimum-mean-square-error (LMMSE) estimator. This estimator applies an expectation–maximization (EM) iterative process to reduce the computational complexity significantly. Finally, we compare the proposed LMMSE-EM estimator with conventional least square (LS) and LMMSE estimator in terms of performance and computational complexity. The simulation results show that the proposed LMMSE-EM estimator achieves exactly the same performance as the optimal LMMSE estimator with much lower computational complexity.

Nonlocal linear minimum mean square error methods for denoising MRI

Abstract The presence of noise results in quality deterioration of magnetic resonance (MR) images and thus limits the visual inspection and influence the quantitative measurements from the data. In this work, an efficient two stage linear minimum mean square error (LMMSE) method is proposed for the enhancement of magnitude MR images in which data in the presence of noise follows a Rician distribution. The conventional Rician LMMSE estimator determines a closed-form analytical solution to the aforementioned inverse problem. Even-though computationally efficient, this approach fails to take advantage of data redundancy in the 3D MR data and hence leads to a suboptimal filtering performance. Motivated by this observation, we put forward the concept of nonlocal implementation with LMMSE estimation method. To select appropriate samples for the nonlocal version of the LMMSE estimation, the similarity weights are computed using Euclidean distance between either the gray level values in the spatial domain or the coefficients in the transformed domain. Assuming that the signal dependent component of the noise is optimally suppressed by this filtering and the rest is a white and uncorrelated noise with the image, we adopt a second stage LMMSE filtering in the principal component analysis (PCA) domain to further enhance the image and the noise variance is adaptively adjusted. Experiments on both simulated and real data show that the proposed filters have excellent filtering performance over other state-of-the-art methods.

MMSE Space-Time Multi-User Detection in OFDM Systems Based on a Variety of Modulation

MIMO-OFDM is considered as candidates for future 3G/B3G broadband wireless services. High quality mobile services have to eliminate multiple-access interference (MAI) that affects multi-user receiver performance in MIMO-OFDM system. In this paper, we analyze the effect of MAI on MIMO-OFDM system and adapt a successive interference cancellation (SIC) multi-user detection algorithm which based on minimum mean square error (MMSE) algorithm for MIMO-OFDM system. This algorithm by optimizing the sorting structure enables the receiver algorithm has better performance. The simulation results show that the algorithm can obtain rate is better than the conventional linear MMSE algorithm performance in the error, and constellation mapping point spacing also affect the detection performance. As the spacing is reducing, the bit error probability will rise.

Design and implementation of channel estimation for low-voltage power line communication systems based on OFDM

An optimized channel estimation algorithm based on a time-spread structure in OFDM low-voltage power line communication (PLC) systems is proposed to achieve a lower bit error rate (BER). This paper optimizes the best maximum multi-path delay of the linear minimum mean square error (LMMSE) algorithm in time-domain spread OFDM systems. Simulation results indicate that the BER of the improved method is lower than that of conventional LMMSE algorithm, especially when the signal-to-noise ratio (SNR) is lower than 0 dB. Both the LMMSE algorithm and the proposed algorithm are implemented and fabricated in CSMC 0.18 μm technology. This paper analyzes and compares the hardware complexity and performance of the two algorithms. Measurements indicate that the proposed channel estimator has better performance than the conventional estimator.

Enhanced MIMO LMMSE Turbo Equalization: Algorithm, Simulations, and Undersea Experimental Results

In this paper, an enhanced linear minimum mean square error (LMMSE) turbo equalization scheme is proposed for multiple-input multiple-output (MIMO) communication systems with bit-interleaved coded modulation (BICM) in the time domain and multiplexing in the space domain. The proposed turbo equalization scheme outperforms the conventional LMMSE turbo equalization by adopting two new signal processing techniques. First, it performs hybrid soft interference cancellation (HSOIC) by subtracting the soft decisions of the interfering symbols, and the soft decisions are calculated by using a hybrid of the a priori information at the equalizer input and the a posteriori information at the equalizer output. Second, it employs a novel block-wise reliability-based ordering scheme such that more “reliable” symbols are detected before the less “reliable” ones to reduce error propagation in HSOIC. The symbol reliability information is based on the symbol a priori probability, as a unique byproduct of turbo detection, thus can be obtained with very small overhead. A low-complexity approximation of the enhanced MIMO LMMSE turbo equalization is also proposed to balance the tradeoff between complexity and performance. The performance of the enhanced MIMO LMMSE turbo equalization has been verified through both numerical simulations and the undersea experimental data collected in the SPACE08 experiment launched near Martha's Vineyard, Edgartown, MA, in 2008.

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).

Space-Time Equalizers for MIMO High Speed WCDMA Downlinks

Multiple-input multiple-output (MIMO) communications based on multiple transmit and receive antennae will be applied to enhance the data rates of the third generation (3G) cellular systems or wideband code division multiple access (WCDMA) and in particular, their high speed downlink packet access (HSDPA) services. This causes both spatial multiplexing interference (SMI) and downlink multiple access interference (MAI). In this paper, we derive an improved linear minimum mean square error (LMMSE) detector approximation suitable for MIMO-WCDMA systems. We also propose a new two-stage receiver architecture to suppress the effects of both MAI and SMI in HSDPA communications. SMI often dominates MAI, since it does not benefit from spreading gain. Therefore, a spatial maximum a posteriori (MAP) detector is used to suppress SMI, and a LMMSE based channel equalizer is applied as its front- end. The performance of the resulting LMMSE-MAP receiver structure is studied via Monte Carlo computer simulations with assumptions very realistically mimicking those of the HSDPA specification. The results show that it clearly outperforms the rake receiver or the improved LMMSE equalizer approximation, which, on the other hand, is also shown to be superior to the more conventional LMMSE equalizers.

A new symbol-alphabet-aware multi-user detection scheme

In this letter, a new multi-user detection scheme is proposed. It employs a novel nonlinear minimum mean square error (MMSE) estimator that exploits the knowledge of symbol alphabet to estimate the interfering signals. After cancelling the estimated interference, a conventional linear multi-user detector such as the MMSE detector or, simply, the matched-filter receiver (MFR) is then used to detect the desired user signal. Simulation results demonstrate that the proposed detector significantly outperforms the conventional linear MMSE detector

Time domain upsampling linear MMSE receiver for asynchronous MC-CDMA systems over frequency-selective Rayleigh fading channels

In this paper, a novel time domain upsampling linear receiver is proposed for an asynchronous multicarrier code division multiple access system in frequency-selective Rayleigh fading channels. After anti-aliasing filtering, the received discrete-time upsampled signal is processed by a finite-impulse-response (FIR) filter at the receiver without any pre-processing. For the FIR filter, the observation window is extended to two-symbol duration in order to capture a complete desired symbol and eliminate the knowledge of signature waveform and timing acquisition requirement, and the tap coefficients are optimised according to the minimum mean square error (MMSE) criterion by employing a normalised least-mean-square algorithm. The proposed time domain upsampling MMSE (TDU-MMSE) receiver works in a decision-directed mode after training by a pre-defined sequence. The theoretical bit error rate (BER) is derived for the proposed receiver and agrees very well with the simulation results. Moreover, it has been shown that the TDU-MMSE receiver is able to achieve better BER performance than the conventional linear MMSE (LMMSE) and partial sampling MMSE (PS-MMSE) with lower computation complexity.