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In wireless mobile networks, the signal transmitted from the transmitter to the receiver is vulnerable to many performance-degrading factors, such as distortion, noise, fading, and nonlinear distortion, due to the physical nature of the channel. To address these factors, accurate prediction and estimation must be employed to achieve optimized performance in wireless mobile networks. Several channel estimation techniques have been developed. In this article, we provide a review of some of these techniques under various categories, highlighting their strengths and limitations in different operational environments. [ABSTRACT FROM AUTHOR]

The Filter Bank Multi-Carrier (FBMC) is an interesting recent technology for next-generation wireless radio communication systems. Equalization is a fundamental technique for enhancing Bit Error Rate (BER) performance. The traditional Linear Zero Forcing (LZF) and Linear Minimum Mean Square Error (LMMSE) equalizers have large noise amplification and high complexity, respectively. As the estimated error of the Signal-to-Noise Ratio (SNR) grows, the BER performance of the LMMSE equalizer degrades. In this paper, we present a Joint Low Complexity Optimized ZF-Successive Interference Cancellation (JLCOZF-SIC) equalizer for FBMC communication systems that are based on the Walsh–Hadamard Transform (WHT) rather than the Fast Fourier Transform (FFT). As in the MMSE-Successive Interference Cancellation (MMSE-SIC) equalizer, the proposed equalizer avoids the estimating error induced by average power per symbol estimation. The simulation results show that the proposed algorithm improves the system performance more than the existing algorithms. Furthermore, the proposed equalization provides stability in the BER performance at high levels of the co-Carrier Frequency Offset (co-CFO). In terms of computational complexity, the proposed scheme has a lower computational complexity than traditional schemes. [ABSTRACT FROM AUTHOR]

Quantum state tomography (QST) is essential for characterizing unknown quantum states. Several methods of estimating quantum states already exist and can be classified mainly into three broad classes. They are based on the criteria like maximum likelihood, linear inversion, and Bayesian framework. The Bayesian framework for QST gives a better reconstruction performance. However, the existing methods of the Bayesian frameworks are computationally extensive and, most of the time require knowledge about the prior distribution of the quantum state. In this paper, we propose a Bayesian method of QST based on the linear minimum mean square error criterion, where the prior statistics are estimated and the computational complexity is comparable to that of the linear inversion based QST method. We also propose an adaptive version based on the block estimation of parameters. Extensive numerical simulations are conducted to demonstrate its efficacy over the linear inversion-based QST regarding trace distance error metric. [ABSTRACT FROM AUTHOR]

The large number of estimated parameters in a reconfigurable intelligent surface (RIS) makes it difficult to achieve accurate channel estimation accuracy in 6G. Therefore, we suggest a novel two-phase channel estimation framework for uplink multiuser communication. In this context, we propose an orthogonal matching pursuit (OMP)-based linear minimum mean square error (LMMSE) channel estimation approach. The OMP algorithm is used in the proposed algorithm to update the support set and pick the columns of the sensing matrix that are most correlated with the residual signal, which successfully reduces pilot overhead by removing redundancy. Here, we use LMMSE's advantages for handling noise to address the problem of inadequate channel estimation accuracy when the signal-to-noise ratio (SNR) is low. Simulation findings demonstrate that the proposed approach outperforms least-squares (LS), traditional OMP, and other OMP-based algorithms in terms of estimate accuracy. [ABSTRACT FROM AUTHOR]

Pilot symbol assisted (PSA) channel estimation is an important means to improve the communication quality of orthogonal frequency division multiplexing (OFDM) systems. The insertion position of the pilot in the frequency domain and time domain of the OFDM symbol is called the pilot pattern. The appropriate pilot pattern can greatly reduce channel estimation error and enhance communication quality. In this paper, the branch and bound (BnB) method is adopted to design the pilot pattern BnB-PP for the first time. Specifically, the result of the linear minimum mean square error method is taken as the target value of channel estimation in PSA-OFDM systems. For branching, pilot positions are randomly selected one by one in the form of the binary tree. For the boundary, after the pilots are filled randomly, a correction term is subtracted from the result of channel estimation at this time to present the expectation boundary. The results show that BnB-PP is better than the common pilot pattern. When signal-to-noise ratio is 36, the average MSE of channel estimation for 32 and 64 pilots in 1344 data signals is reduced by 93.24 % and 62.33 % respectively compared with the lattice-type pilot pattern. [ABSTRACT FROM AUTHOR]

Abstract 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.

Orthogonal time frequency space (OTFS) system offers a high data rate and effectively exploits full diversity compared to orthogonal frequency division multiplexing (OFDM), achieving a seamless trade-off between data rate and processing gain. However, detecting OTFS signals is challenging due to the complex conversion between delay-Doppler (DD) and time domains. In this article, we propose a stationary iteration-based approximate inversion (SIAI) technique for low-complexity detection in uplink OTFS systems.The proposed SIAI detection technique features square-order computational complexity and delivers performance close to that of a linear minimum mean square error (LMMSE) detector. Simulation results demonstrate that the SIAI technique outperforms several state-of-the-art detection methods in terms of both error performance and computational complexity. Additionally, the robustness of the SIAI technique is validated in scenarios with imperfect channel state information at the receiver. [ABSTRACT FROM AUTHOR]

In recent years, multispectral imaging systems are considerably expanding with a variety of multispectral demosaicking algorithms. The most crucial task is setting up an optimal multispectral demosaicking algorithm in order to reconstruct the image with less error from the raw image of a single sensor. In this paper, we presented a four-band multispectral filter array (MSFA) with the dominant blue band and a multispectral demosaicking algorithm that combines the linear minimum mean square error (LMMSE) and the adaptive kernel regression methods. To estimate the missing blue bands, we used the LMMSE algorithm and for the other spectral bands, the directional gradient method, which relies on the estimated blue bands. The adaptive kernel regression is then applied to each spectral band for their update without persistent artifacts. The experiment results demonstrate that our proposed method outperforms other existing approaches both visually and quantitatively in terms of peak signal-to-noise-ratio (PSNR), structural similarity index (SSIM) and root mean square error (RMSE). [ABSTRACT FROM AUTHOR]

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 lowvoltage 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. [ABSTRACT FROM AUTHOR]

The large number of estimated parameters in a reconfigurable intelligent surface (RIS) makes it difficult to achieve accurate channel estimation accuracy in 6G. Therefore, we suggest a novel two-phase channel estimation framework for uplink multiuser communication. In this context, we propose an orthogonal matching pursuit (OMP)-based linear minimum mean square error (LMMSE) channel estimation approach. The OMP algorithm is used in the proposed algorithm to update the support set and pick the columns of the sensing matrix that are most correlated with the residual signal, which successfully reduces pilot overhead by removing redundancy. Here, we use LMMSE’s advantages for handling noise to address the problem of inadequate channel estimation accuracy when the signal-to-noise ratio (SNR) is low. Simulation findings demonstrate that the proposed approach outperforms least-squares (LS), traditional OMP, and other OMP-based algorithms in terms of estimate accuracy.

The Underwater Acoustic (UWA) wireless communication system is considered one of the most challenging systems for data transmission. The Orthogonal Frequency Division Multiplexing (OFDM) system promises numerous benefits, including high spectrum efficiency and inter-Symbol interference mitigation. But, it is very sensitive to Carrier Frequency Offset (CFO). The implementation of the OFDM necessitates the use of orthogonal transforms such as the Inverse Discrete Fourier Transform (IDFT)/DFT. On the other hand, the Multiple-Input-Multiple Output (MIMO) configuration is affected by co-channel interference. In this paper, we look at another type of co-interference known as the co-CFO. The Linear Zero Forcing (LZF) equalizer suffers from noise enhancement and high computational complexity due to the direct matrix inversion. The Linear Minimum Mean Square Error Equalizer suffers from the noise enhancement problem at high values of the Signal-to-Noise Ratio (SNR), and requires the estimation of the operating SNR to work properly, besides the high computational complexity of the implementation. In this paper, we propose a Joint Low Complexity Regularized LZF equalizer for the UWA-OFDM communication system. The proposed equalizer is driven and takes the effect of the UWA co-channel effect, the noise, and the co-CFO. Simulation results show the important role of the proposed equalizer in configuring the UWA-OFDM communication system. [ABSTRACT FROM AUTHOR]

We consider multi-antenna wireless systems aided by reconfigurable intelligent surfaces (RIS). RIS presents a new physical layer technology for improving coverage and energy efficiency by intelligently controlling the propagation environment. In practice however, achieving the anticipated gains of RIS requires accurate channel estimation. Recent attempts to solve this problem have considered the least-squares (LS) approach, which is simple but also sub-optimal. The optimal channel estimator, based on the minimum mean-squared-error (MMSE) criterion, is challenging to obtain and is non-linear due to the non-Gaussianity of the effective channel seen at the receiver. Here we present approaches to approximate the optimal MMSE channel estimator. As a first approach, we analytically develop the best linear estimator, the LMMSE, together with a corresponding majorization-minimization-based algorithm designed to optimize the RIS phase shift matrix during the training phase. This estimator is shown to yield improved accuracy over the LS approach by exploiting second-order statistical properties of the wireless channel and the noise. To further improve performance and better approximate the globally-optimal MMSE channel estimator, we propose data-driven non-linear solutions based on deep learning. Specifically, by posing the MMSE channel estimation problem as an image denoising problem, we propose two convolutional neural network (CNN)-based methods to perform the denoising and approximate the optimal MMSE channel estimation solution. Our numerical results show that these CNN-based estimators give superior performance compared with linear estimation approaches. They also have low computational complexity requirements, thereby motivating their potential use in future RIS-aided wireless communication 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( Np³ ), 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. [ABSTRACT FROM AUTHOR]

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.

Quantum state tomography (QST) is essential for characterizing unknown quantum states. Several methods of estimating quantum states already exist and can be classified mainly into three broad classes. They are based on the criteria like maximum likelihood, linear inversion, and Bayesian framework. The Bayesian framework for QST gives a better reconstruction performance. However, the existing methods of the Bayesian frameworks are computationally extensive and, most of the time require knowledge about the prior distribution of the quantum state. In this paper, we propose a Bayesian method of QST based on the linear minimum mean square error criterion, where the prior statistics are estimated and the computational complexity is comparable to that of the linear inversion based QST method. We also propose an adaptive version based on the block estimation of parameters. Extensive numerical simulations are conducted to demonstrate its efficacy over the linear inversion-based QST regarding trace distance error metric.

In recent years, multispectral imaging systems are considerably expanding with a variety of multispectral demosaicking algorithms. The most crucial task is setting up an optimal multispectral demosaicking algorithm in order to reconstruct the image with less error from the raw image of a single sensor. In this paper, we presented a four-band multispectral filter array (MSFA) with the dominant blue band and a multispectral demosaicking algorithm that combines the linear minimum mean square error (LMMSE) and the adaptive kernel regression methods. To estimate the missing blue bands, we used the LMMSE algorithm and for the other spectral bands, the directional gradient method, which relies on the estimated blue bands. The adaptive kernel regression is then applied to each spectral band for their update without persistent artifacts. The experiment results demonstrate that our proposed method outperforms other existing approaches both visually and quantitatively in terms of peak signal-to-noise-ratio (PSNR), structural similarity index (SSIM) and root mean square error (RMSE).

Millimeter wave MIMO combines the benefits of compact antenna arrays with a large number of elements and massive bandwidths, so that fully digital beamforming has the potential of supporting a large number of simultaneous users with per user data rates of multiple gigabits/sec (Gbps). In this paper, we develop an analytical model for the impact of nonlinearities in such a system, and illustrate its utility in providing hardware design guidelines regarding two key challenges: the low available precision of analog-to-digital conversion at high sampling rates, and nonlinearities in ultra-high speed radio frequency (RF) and baseband circuits. We consider linear minimum mean square error (LMMSE) reception for a multiuser MIMO uplink, and provide performance guarantees based on two key concepts: (a) summarization of the impact of per-antenna nonlinearities via a quantity that we term the “intrinsic SNR”, (b) using linear MMSE performance in an ideal system without nonlinearities to bound that in our non-ideal system. For our numerical results, we employ nominal parameters corresponding to outdoor picocells operating at a carrier frequency of 140 GHz, with a data rate of 10 Gbps per user. [ABSTRACT FROM AUTHOR]

In this study we propose a novel correction scheme that filters Magnetic Resonance Images data, by using a modified Linear Minimum Mean Square Error (LMMSE) estimator which takes into account the joint information of the local features. A closed-form analytical solution for our estimator is presented and it proves to make the filtering process far simpler and faster than other estimation techniques that rely on iterative optimization scheme and require multiple data samples. An experimental validation of our correction scheme was carried out through large scale experiments using both clinical and synthetic MR images, artificially corrupted with rician noise of σ varying from 1 to 40. These noisy images were filtered using our proposed method against the classical LMMSE, the Non-Local Means filter and the Nonlocality-Reinforced Convolutional Neural Networks (NRCNN) techniques. The results show an outstanding performance of our proposed method, given the fact that from σ ≈ 12 onwards, the proposed method outperforms all other methods. Another attention-grabbing feature of our method is that its Structural Similarity does not vary sharply [0.87, 0.95] across the σ spectrum as the other three techniques, which implies that this method can work on a wider range of deteriorated images than the rest of the techniques. [ABSTRACT FROM AUTHOR]

A high transmission rate can be obtained using Multi Input Multi Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) model. The most commonly used 3D-pilot aided channel estimation (PACE) techniques are Least Square (LS) and Least Minimum Mean Square (LMMSE) error. Both of the methods suffer from high mean square error and computational complexity. The LS is quite simple and LMMSE being superior in performance to LS providing low Bit Error Rate (BER) at high Signal to Noise ratio (SNR). Artificial Intelligence when combined with these two methods produces remarkable results by reducing the error between transmission and reception of data signal. The essence of LS and LMMSE is used priory to estimate the channel parameters. The bit error so obtained is compared and the least bit error value is fine-tuned using particle swarm optimization (PSO) to obtained better channel parameters and improved BER. The channel parameter corresponding to the low value of bit error rate obtained from LS/LMMSE is also used for particle initialization. Thus, the particles advance from the obtained channel parameters and are processed to find a better solution against the lowest bit error value obtained by LS/LMMSE. If the particles fail to do so, then the bit error value obtained by LS/LMMSE is finally considered. It has emerged from the simulated results that the performance of the proposed system is better than the LS/LMMSE estimations. The performance of OFDM systems using proposed technique can be observed from the imitation and relative results.

In this treatise, we have proposed an uplink channel estimation algorithm for nested and linear array antennas with hardware imperfections, antenna position error, and channel correlation in a multi-cell, multi-user wireless system with a one-ring scattering channel model. Hardware impairments and antenna position errors assume special importance in modern wireless system due to their severe impacts on system performance. With the increase of radio frequency (RF) in the modern wireless system and the subsequent usage of a large number of antennas for beamforming and meeting ever-increasing data rates, those errors are much relevant today. We have considered a sparse nested array (NA) and uniform linear array (ULA) for array geometry at the base station (BS). In this work, closed-form expression and bounds on the mean square error (MSE) of the linear minimum mean square error (LMMSE) channel estimate are derived. We have also analyzed the spectral efficiency (SE) of a simplified system with ULA and NA at the BS, given the model with hardware distortion and antenna position error. Two cases for (a) perfect channel side information (CSI) and (b) estimated channel are evaluated and compared in terms of their SEs, while the closed-form expression for SE with ULA at the BS considering perfect CSI is derived and verified. Numerical simulations are conducted to verify the accuracies of our derived results. [ABSTRACT FROM AUTHOR]

This work investigates the realistic performance of IEEE 802.11af systems released for the efficient spectrum utilization of TV white space (TVWS). These systems are operated over many contiguous or non-contiguous channels based on the TVWS frequency band availability. Accordingly, we consider realistic channel estimation for TVWS system and analyze the corresponding system performance of linear minimum mean square error (LMMSE) and orthogonal matching pursuit (OMP) algorithms. While LMMSE estimates channel path gains assuming perfectly known tap delay locations, OMP estimates both channel path gains and delays. Owing to the realistic implementation and estimating the full channel state information (CSI) of sparse channels, we mainly assess the OMP performance together with the LMMSE estimation for comparison in terms of channel reconstruction and symbol detection errors. To address the channel estimation performance, Bayesian Cramer-Rao bound is derived theoretically for both perfect and imperfect CSI, and confirmed with the simulations. Simulation results demonstrate that the realistic OMP-based symbol detection performance is found to be only 1–2 dB inferior compared to the near-optimal LMMSE-based estimation with known delays in low and medium signal-to-noise-ratio regions, where communication mainly occurs in practice. In addition, the effects of channel multipath number, channel resolution and operation modes on the system performance are studied for different scenarios. The results of this work are important for the practical implementation of IEEE 802.11af-based systems. [ABSTRACT FROM AUTHOR]

Employing low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays at the receivers has drawn considerable interests in the millimeter wave (mm-wave) system. Since mm-wave channels are sparse in angular dimensions, exploiting the structure could reduce the number of measurements while achieving acceptable performance at the same time. Motivated by the variational Bayesian line spectral estimation (VALSE) algorithm which treats the angles as random parameters, in contrast to previous works which confine the estimate to the set of grid angle points and induce grid mismatch, this paper proposes the grid-less quantized variational Bayesian channel estimation (GL-QVBCE) algorithm for antenna array systems with low resolution ADCs. Numerical results show the near optimal performance of GL-QVBCE by comparing with the Cramèr Rao bound (CRB) and the state-of-art methods. [ABSTRACT FROM AUTHOR]

Multiple input multiple output (MIMO) system transmission is a popular diversity technique to improve the reliability of a communication system where transmitter, communication channel and receiver are the important elements. Data transmission reliability can be ensured when the bit error rate is very low. Normally, multiple antenna elements are used at both the transmitting and receiving section in MIMO Systems. MIMO system utilizes antenna diversity or spatial diversity coding system in wireless channels because wireless channels severely suffer from multipath fading in which the transmitted signal is reflected along various multiple paths before reaching to the destination or receiving section. Overwhelmingly, diversity coding drives multiple copies through multiple transmitting antennas (if one of the transmitting antenna becomes unsuccessful to receive, other antennas are used in order to decode the data) for improving the reliability of the data reception. In this paper, the MIMO channel model has been illustrated. Moreover, the vector for transmitting signal has been considered by implementing least square minimization as well as linear minimum mean square estimation. Parallel transmission of MIMO system has also been implemented where both the real part and imaginary part of the original, detected and the corresponding received data sequence has been described graphically. One of the important qualities of MIMO is a substantial increase in the capacity of communication channel that immediately translates to comparatively higher signal throughputs. The MIMO communication channels have a limited higher capacity considering the distortions for various deterministic channel recognitions and SNR. The MIMO channel average capacity is achieved more than 80% for dissimilar levels of impairments in transceiver when the value of kappa (Level of impairments in transmitter hardware) reduces from 0.02 to 0.005. The finite-SNR multiplexing gain (Proportion of MIMO system capacity to SISO system capacity) has been observed for deterministic and uncorrelated Rayleigh fading channels correspondingly. The core difference is in the high SNR level. It may occur for two reasons: (a) there is a quicker convergence to the limits under transceiver impairments (b) deterministic channels that are built on digital architectural plans or topographical maps of the propagation environment acquire an asymptotic gain superior than multiplexing gain when the number of transmitting antenna is greater than the number of receiving antenna. [ABSTRACT FROM AUTHOR]

In this treatise, we conceive a hardware impairment aware receiver design of a large multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, taking into account the error statistics of parameter estimations. The hardware impairment includes the quantization noise, phase noise etc. There are two key contributions in this work. Firstly, consideration of error statistics of residual parameters in the receiver design improves receiver performance. In this work, we propose a linear equalizer based data detector at the receiver for a large MIMO-OFDM system and the parameters of interest are the channel impulse response (CIR) and carrier frequency offset (CFO) along with the hardware impairments awareness both at transmitter and receiver. The design criterion chosen for designing the detector equalizer is based on the linear minimum mean square error (LMMSE) one and it will consider the statistics of estimation error and hardware impairments. However, in any practical communication system, the availability of statistics for these parameter errors is inconceivable. Hence, we propose further to consider the Bayesian Crammer Rao lower bound (BCRLB) as the variance of the error with the key assumption of zero mean. Numerical simulation demonstrates that, consideration of such statistics attains an SNR gain of $2$ dB with respect to a fixed symbol error ratio (SER). It also shows that the SER performance with BCRLB matches very closely with the case, when exact error variance is used. For the second contribution, an extensive theoretical analysis has been performed to evaluate the MSE improvement of data detection with the LMMSE criterion with parameter estimation error statistics consideration in the large MIMO-OFDM system. This theoretical analysis of improved MSE has been conceived with the usage of large dimension random matrix theory and a closed-form expression has been obtained. Numerical simulation demonstrates that the theoretical analysis for MSE improvement matches closely with the simulated one. [ABSTRACT FROM AUTHOR]

We consider performance comparison and application specific integrated circuit (ASIC) designs of linear minimum mean-square error (LMMSE) and K-best list sphere detector (LSD) algorithms for 4 × 4 and 8 × 8 multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Requirements for higher data rate and lower power consumption set new challenges for implementation. In order to minimize the power consumption, an optimal detector would be able to switch the detection algorithm to suit the channel conditions. The detectors are designed for three different modulation schemes using 28 nm complementary metal oxide semiconductor (CMOS) technology. The communications performance is evaluated in the Third Generation Partnership Project (3GPP) Long-Term Evolution (LTE) system. The impact of transmit precoding is considered. The ASIC designs aim at providing the hardware design aspects to the comparison of detectors. The designs are synthesized and complexity and power consumption results are found. Based on the ASIC synthesis and communications performance results, we show the performance–energy efficiency and performance–complexity comparison. We also present the most suitable scenarios for a low-power detector and show how the transmit precoding impacts the detector selection. [ABSTRACT FROM AUTHOR]

In this article, we propose a Linear Minimum Mean Squares Error-Support Vector Machine regression (LMMSE-SVR) approach which is applied to Long Term Evolution (LTE) downlink channel environment estimation under high mobility conditions. LMMSE-SVR is employed to track and estimate the rapid variations of a realistic Extended Vehicular A model channel according to 3GPP specifications. This contribution assimilates both channel estimation at reference signals and interpolation at data signals into the LMMSE-SVR method. Performances of our channel environment estimation proposal in terms of Bit Error Rate and Mean Squares Error are established via simulation for both normal and extended Cyclic Prefix scenarios in LTE downlink system with 64-QAM modulation scheme under 350 km/h mobile speed. [ABSTRACT FROM AUTHOR]

This letter considers the data detection problem in symmetric massive multiuser multiple-input-multiple-output (MU-MIMO) uplink systems, in which the number of base station (BS) antennas is equal to the number of single-antenna users. However, under uplink-heavy traffic, the conventional linear minimum mean-square-error (LMMSE)-based detectors suffer significant performance loss and thus cannot be applied. Although a detector based on Riemannian manifold optimization (RMO) provides excellent bit error rate (BER) performance that remarkably outperforms the LMMSE detector, it incurs high time complexity. Moreover, the computational complexity remains high. Thus, a simple yet computationally efficient detector based on the projected gradient descent algorithm is proposed in this letter to reduce the computational time and complexity of the detection algorithm while achieving the same BER performance as the RMO-based detector. Simulation results demonstrate that the proposed algorithm nearly yields the same performance as the RMO at low computational complexity, but the run-time complexity is considerably reduced. [ABSTRACT FROM AUTHOR]

The FBMC-OQAM waveform recently has received a lot of interest due to its outstanding spectral characteristics. However, that comes with the cost of a new built-in interference introduced between its subcarriers which triggered the need for new transceiver techniques. In this letter, we propose an efficient equalization technique that utilizes the Wiener filter. Unlike the state-of-the-art studies, we consider the channel estimation errors within the design of the Wiener filter. This will permit improving the performance accuracy of the FBMC-OQAM system in very high-speed scenarios. We propose a straightforward analytical development for our study and conduct a numerical analysis via Monte Carlo simulations where we compare the performance of the proposed technique with other recent studies. [ABSTRACT FROM AUTHOR]

A low-complexity iterative channel estimation and Reed-Solomon (RS) decoding receiver is developed. The joint channel estimation and decoding receiver is based on the linear minimum mean square error (LMMSE) channel estimation technique and the recently proposed RS parity-check transformation algorithm (PTA). A key feature of this joint receiver is the simple symbol remapping process used to refine the initial LMMSE estimate of the channel after each iterative RS-PTA decoding. The performance of the joint iterative receiver is compiled through computer simulation assuming a rectangular M-ary quadrature amplitude modulation as the underlying modulation scheme. Simulation results show significant symbol error rate (SER) improvement in the iterative receiver in comparison with the receiver without iterative feedback. Besides, the RS-PTA iterative receiver outperforms the recently proposed RS Koetter and Vardy (RS-KV) iterative receiver structure both in terms of the SER performance and the computational time complexity. Of particular interest, results verify that the computational time complexity of the RS-PTA iterative receiver can be reduced without compromising the SER performance of the receiver.

A novel hybrid channel estimator is proposed for multiple-input multiple-output orthogonal frequency- division multiplexing (MIMO-OFDM) system with per-subcarrier transmit antenna selection having optimal power allocation among subcarriers. In practice, antenna selection information is transmitted through a binary symmetric control channel with a crossover probability. Linear minimum mean-square error (LMMSE) technique is optimal technique for channel estimation in MIMO-OFDM system. Though LMMSE estimator performs well at low signal to noise ratio (SNR), in the presence of antenna-to-subcarrier-assignment error (ATSA), it introduces irreducible error at high SNR. We have proved that relaxed MMSE (RMMSE) estimator overcomes the performance degradation at high SNR. The proposed hybrid estimator combines the benefits of LMMSE at low SNR and RMMSE estimator at high SNR. The vector mean square error (MSE) expression is modified as scalar expression so that an optimal power allocation can be performed. The convex optimization problem is formulated and solved to allocate optimal power to subcarriers minimizing the MSE, subject to transmit sum power constraint. Further, an analytical expression for SNR threshold at which the hybrid estimator is to be switched from LMMSE to RMMSE is derived. The simulation results show that the proposed hybrid estimator gives robust performance, irrespective of ATSA error.

This paper presents an improved Distance Metric (DM) technique for deriving soft reliability information over Rayleigh fading channel. We compared this proposed DM technique with the conventionally used DM technique in the literature. The conventional DM method derives the soft reliability information from the output received symbols while the proposed DM method derives the soft reliability information from the Channel State Information (CSI) which result due to variations in the channel gain. Performance analysis of these DM methods are verified over flat Rayleigh fading channels, and on time-varying frequency-selective Rayleigh fading channels using rectangular M-QAM and OFDM systems respectively. Also, two channel estimation techniques are used to derived the CSI assuming different normalized Doppler frequency and frame length size. The performance of the conventional and proposed soft reliability derivation methods are documented through computer simulations assuming Koetter and Vardy, Reed- Solomon (KV-RS) soft decision decoding algorithm as the Forward Error Correction (FEC) scheme. From the computer simulation results, the proposed DM method offers significant improvement in Codeword Error Rate (CER) performance in comparison with the conventional DM method with no significant increase in computational delay and time complexity. [ABSTRACT FROM AUTHOR]

The upcoming 5G era is considered as a critical answer to future railway communication needs. In this correspondence, we analyze the performance of sliding window time domain (TD) LMMSE channel estimation technique where a generalized development is proposed and analyzed for 5G filter bank multicarrier-offset quadratic-amplitude modulation waveform candidates. In order to enhance performance, the analytical model is extended to exploit multipaths and multiantennas correlation wherever they do exist e.g., tunnels. TD-LMMSE performance for 5G waveforms and achieved enhancements are verified by Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In-phase/quadrature-phase imbalance (IQI) is one of the hardware impairments in uplink massive multiple-input multiple-output systems. In this context, by modeling the IQI parameters as random variables, this letter first analyzes the statistic property of the effective channel in which both the IQI parameters and channel state information are included. By doing so, an LMMSE-based receiver is then investigated for channel estimation and data detection. Furthermore, we provide tightness analysis on asymptotic achievable sum rate for the proposed scheme using results from random matrix theory. Finally, simulation results show that our proposed scheme has small performance loss but with the advantage that estimating IQI parameters can be avoided. [ABSTRACT FROM AUTHOR]

When the number of base station (BS) antennas is considerably larger than the number of user terminals (UTs), a simple linear minimum mean-square-error (LMMSE) data detection algorithm is near-optimal for uplink massive multiuser multiple-input and multiple-output (MIMO) systems. However, the LMMSE detector suffers significant performance loss when the number of UTs is comparable to the number of BS antennas. To alleviate the performance loss caused by the decrease in the BS-antenna-to-UT ratio, known data detection algorithm, namely “TASER,” can yield bit error rate (BER) performance close to that of the optimal detector in symmetric massive multiuser MIMO systems, where the BS-antenna-to-UT ratio is one. However, this TASER algorithm can only handle binary and quadratic phase-shift keying (PSK) modulations. Moreover, the computational complexity of the TASER algorithm is still remarkably high. Thus, a novel data detection algorithm is proposed in this study to reduce the computational complexity of the detection algorithm while achieving acceptable BER performance. The proposed algorithm is developed from the retraction-based Riemannian manifold optimization (RMO) framework for symmetric massive multiuser MIMO systems. Unlike the TASER-based detector, the proposed RMO-based detector can be applied for general $M$-ary PSK modulations. Simulation results show that the proposed detector significantly outperforms the LMMSE detector in terms of BER performance. In addition, the required complexity of RMO is substantially lower than that of TASER. These findings indicate that the proposed RMO-based detector exhibits a highly desirable tradeoff between BER performance and complexity. [ABSTRACT FROM PUBLISHER]

In this paper, we design a segment training based individual channel estimation (STICE) scheme for the classical two-way relay network (TWRN) with multi-pair sources (MPS) and amplify-and-forward (AF). We adopt the linear minimum mean square error (LMMSE) channel estimator to minimize the mean square error (MSE) without channel estimation error, where the optimal power allocation strategy from the relay for different sources is obtained. Then the MSE gains are given with different source pairs among the proposed power allocation scheme and the existing power allocation schemes. Numerical results show that the proposed method outperforms the existing ones. [ABSTRACT FROM AUTHOR]

A major challenge for uplink multiuser massive multiple-input and multiple-output (MIMO) systems is the data detection problem at the receiver due to the substantial increase in the dimensions of MIMO systems. The optimal maximum likelihood detector is impractical for such large wireless systems, because it suffers from exponential complexity in terms of the number of users. Therefore, suboptimal alternatives with reduced complexity, such as the linear minimum mean square error (LMMSE) detector, are necessary. However, the LMMSE detector still introduces high computational complexity, mainly caused by the computation of the Gram matrix and matrix inversion. To reduce the computational complexity of data detection while achieving satisfactory bit error rate (BER) performance, we initially proposed an iterative data detection algorithm that exploits the coordinate descent method (CDM)-based algorithmic framework for uplink multiuser massive MIMO systems. We then developed a reduced-complexity hardware implementation algorithm by leveraging the “one-at-a-time” update property of the CDM-based algorithmic framework. Simulation results revealed that the proposed CDM-based detector provides the same or improved BER performance than the classical LMMSE algorithm at a lower complexity for different test scenarios. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) as an efficient means has been widely applied in magnetic resonance imaging (MRI). As a regularization term to enforce the sparsity in the finite difference domain, the conventional total variation (TV) has been introduced in this field, where the staircase effect is presented. To overcome this issue, a new framework in the difference domain called joint constraint patch-based total variation (JCTV) is proposed. First, the image patch is utilized as the unit for TV norm to improve the adaptativity. Second, JCTV introduces a new nonlocal constraint term that exploits the estimated coefficients of the fully sampled image via linear minimum mean square error (LMMSE) criterion to improve the reconstruction performance. Finally, an alternative minimization algorithm is developed to seek the solution. Extensive experiments on a set of in vivo MR images demonstrate that the proposed algorithm outperforms the state-of-the-art approaches in terms of peak signal-to-noise ratio and visual quality. [ABSTRACT FROM AUTHOR]