WSS Processes and Wiener Filters on Digraphs

LMMSE and MAP estimators for reduction of multiplicative noise in the nonsubsampled contourlet domain

Minimum mean square error estimation in linear regression

Underwater acoustic channels usually suffer from the extended multipath propagation which causes inter symbol interference (ISI). At the receiving end, serious ISI would result in the decrease of the reliability of underwater acoustic communication systems. In this paper, Turbo equalization is applied to increase the reliability of communication systems. The realization of the channel equalizer in Turbo equalization is the linear minimum mean-square error (MMSE) estimate. To avoid performance loss, Turbo equalization and factor graph are combined together which provides a full-window method. Moreover, a modified extrinsic information feedback mechanism of turbo equalization is proposed. The reliability of communication systems is further enhanced by using this mechanism. Simulation results show that the proposed algorithm performs well with low complexity. This demonstrates the potential of the suggested algorithm in practice.

Physical layer (PHY) security is recently regarded as a promising technique to improve the security performance of wireless communication networks. Current developments in PHY security are often based on the assumption of perfect channel state information (CSI). In this paper, both security and reliability performance for the downlink cloud radio access network with optimal remote radio heads (RRHs) node selection are investigated in a practical scenario by considering channel estimation (CE) errors. In particular, a three-phase transmission scheme is proposed and the linear minimum mean-square error (MMSE) estimation method is utilized to obtain the CSI. Based on the CSI estimates and the statistics of CE errors, the outage probability and intercept probability are derived in closed-form expression to evaluate the security and reliability performance, respectively. In addition, two possible cases (with or without intercepting signals from baseband unit) are considered for the eavesdropper. It is found that the suggested optimal RRHs selection scheme outperforms the nonselection scheme, and that the increasing number of RRHs can lower the outage probability as well as the intercept probability. It is also shown that there exists an optimal training number to minimize the sum of the outage probability and intercept probability. Finally, simulation results are provided to corroborate our proposed studies.

This paper proposes several linear estimators for the long code CDMA problem. These proposed receivers will be compliant with the specifications issued by the 3rd Generation Partnership Project (3GPP). Using channel estimates derived from a simple cross-correlation scheme on the known pilot bits, various linear estimators are developed. Simulations are carried out over a fast-fading mobile channel model, and results are conclusive to indicate that the linear minimum mean-square error (LMMSE) estimator is the best overall linear estimator in terms of the bit-error rate (BER) performance.

This paper considers wireless uplink information and downlink power transfer in cell-free massive multiple-input multiple-output systems. The single-antenna user equipments (UEs) utilize the energy harvested in the downlink to transmit uplink pilot and information signals to the multiple-antenna access points (APs). We consider Rician fading and maximum ratio processing based on either linear minimum mean-squared error (LMMSE) or least-squares (LS) channel estimation. We derive the average harvested energy by using a practical non-linear energy harvesting circuit model for both coherent and non-coherent transmission schemes. Furthermore, the uplink spectral efficiency (SE) is derived for all the considered methods and the max-min fairness problem is cast where the optimization variables are the AP and UE power control coefficients together with the large-scale fading decoding vectors. The objective is to maximize the minimum SE of the UEs' under APs' and UEs' transmission power constraints. A novel alternating optimization algorithm with guaranteed convergence and improvement at each step is proposed to solve the highly-coupled non-convex problem.

We propose a new methodology for Linear Minimum Mean Square Error (LMMSE) filtering of Diffusion Weighted Imaging (DWI). We consider each voxel as an N-dimensional vector that comprises all the DWI volumes, and then compute the LMMSE estimator for the whole DWI data set jointly, taking into account the underlying tensor model. Our experiments, both with phantom and real data, show that this is a more convenient approach compared to the separate processing of each DWI, that translates to better removal of noise and preservation of structural information. Besides, our model has a simple algebraic formulation which makes the overall computational complexity very close to that of the scalar case, and it does not need multiple samples per DWI.

We generalize the multiuser (CDMA spread spectrum) communication systems to the fading environments. We first extend Verdu's (1986) conventional optimum receiver to Rayleigh-fading environments and then evaluate its performance. Having no knowledge of the received power at the receiving end, we therefore need an estimator to efficiently estimate the received signal strength of each user in fading environments. A linear minimum mean square error (LMMSE) unbiased estimator is proposed to attain this goal. By using the minimum mean square error (MMSE) Bayesian estimation, we further propose the LMMSE bit estimator for efficient demodulation. Its performance is close to the optimum multiuser receiver but with a much simpler polynomial complexity. To further reduce the complexity, we extend the LMMSE estimator to the sequential LMMSE estimator. In sequential estimation, we do not need to implement the matched filter banks and to perform the matrix inversion when estimating. In addition, it converges after approximately 2k iterations, where k is the number of users. With this fast convergence property and the simple structure, the sequential LMMSE estimator provides an attractive alternative to the implementation of a multiuser system.

Backscatter communication (BC) has emerged as a promising paradigm for enabling energy-efficient and low-cost Internet of Things (IoT) systems. However, one practical constraint that is often neglected in BC studies is the circuit sensitivity of the backscatter device (BD), which sets the minimum signal strength required to activate it for signal reflection. In this paper, we address the channel estimation (CE) problem in the BC system with a circuit sensitivity constraint. Specifically, we propose three channel estimators for the receiver in the BC system that is unaware of the activation states of the BD, namely the maximum likelihood (ML), linear minimum mean square error (LMMSE), and maximum a posteriori (MAP) estimators. We derive both the Cramér-Rao bound (CRB) and Bayesian Cramér-Rao bound (BCRB) to evaluate the performance of the proposed estimators. Finally, we provide simulations to validate our analysis. Our results demonstrate that the proposed LMMSE and MAP estimators outperform traditional estimators that neglect the circuit sensitivity constraint.

A Bayesian estimation approach for enhancing speech signals which have been degraded by statistically independent additive noise is motivated and developed. In particular, minimum mean square error (MMSE) and maximum a posteriori (MAP) signal estimators are developed using hidden Markov models (HMMs) for the clean signal and the noise process. It is shown that the MMSE estimator comprises a weighted sum of conditional mean estimators for the composite states of the noisy signal, where the weights equal the posterior probabilities of the composite states given the noisy signal. The estimation of several spectral functionals of the clean signal such as the sample spectrum and the complex exponential of the phase is also considered. A gain-adapted MAP estimator is developed using the expectation-maximization algorithm. The theoretical performance of the MMSE estimator is discussed, and convergence of the MAP estimator is proved. Both the MMSE and MAP estimators are tested in enhancing speech signals degraded by white Gaussian noise at input signal-to-noise ratios of from 5 to 20 dB. >

We examine the sum capacity of a cluster-based cooperative cellular network where a linear minimum-mean squared error (LMMSE) estimator is deployed across a cluster of base stations to estimate multiple in-cluster users. Different from previous works, we examine the impact of interference from out-of-cluster users whose transmit power scales with that of the in-cluster users. For such a network, we derive the sum capacity of the in-cluster users with independent Rayleigh fading and arbitrary path loss. The sum capacity expression is based on accurate approximations we derive for the characteristic function and the probability density function of the in-cluster users' signal-to-interference-plus-noise ratios (SINRs). Numerical examples demonstrate that our new analytical expressions accurately characterize the impact of out-of-cluster interference and cluster size on the sum capacity. We observe that out-ofcluster interference results in a sum capacity saturation regime when the transmit power is large. We also illustrate that the saturation threshold increases with the cluster size. Furthermore, we examine the sum capacity under different path loss exponents which highlights the significance of out-of-cluster interference.

This paper investigates turbo equalization (TE) with linear minimum mean squared error (LMMSE) equalizer, which has much lower complexity but much worse performance than TE with maximum a posteriori (MAP) equalizer because of inefficient information processing of LMMSE equalizer. Motivated by forward and backward recursions of MAP algorithm, three methods are presented to improve turbo equalization with LMMSE equalizer (TE-LMMSE). In the simulation, they all obtain more than 2.5-dB performance gain over TE-LMMSE with no or some complexity price.

In this paper, the state estimation problem of discrete-time Markov jump linear systems is considered where the noises influencing the systems are arbitrarily correlated. For this, two algorithms of state estimate for the considered systems based on the estimation criterion of linear minimum mean-square error estimate are proposed. The first algorithm is an optimal algorithm which can exactly calculate the linear minimum mean-square error estimate of system states. The second algorithm is a suboptimal algorithm which is proposed to reduce the computation and storage load of the proposed optimal algorithm. Computer simulations are carried out to evaluate the performance of the proposed suboptimal algorithm.

SummaryThis paper introduces the concept of spatial and media‐based modulated (SMBM) orthogonal frequency division multiplexing (OFDM) as a potential candidate for highly mobile next generation beyond 5G (B5G) wireless communications. The proposed SMBM‐OFDM technique utilizes not only the transmit antenna and channel state indices but also OFDM subcarriers to improve the system performance under high mobility. In addition, this study sheds light on challenging fast time‐varying channel estimation problem of MBM‐based systems by using the linear minimum mean square error (LMMSE) approach due to its optimality to investigate the achievable system performance with the aid of basis expansion modeling. The minimum lower bound on the channel estimation error (Bayesian Cramer–Rao bound) is derived theoretically and shown to be attainable by the considered LMMSE estimator. Moreover, symbol detection performance is provided for different modulation types and higher mobile velocities. Simulation results demonstrate that SMBM‐OFDM system under high mobility is able to provide around 12‐dB performance gains in terms of both channel estimation and symbol detection error compared to conventional spatial modulation (SM)‐OFDM systems without MBM. The presented framework is important due to addressing the high mobility support of SMBM‐OFDM systems for B5G wireless communications in terms of achievable channel estimation and data detection performance.

A decision-directed (DD) channel estimation based on iterative linear minimum mean square error (LMMSE) is proposed for orthogonal frequency division multiplexing systems. Existing DD channel estimation is well known to have the problem of error propagation because of symbol-by-symbol detection. The proposed algorithm can estimate the correction term of current channel state information (CSI) according to the error vector of previous CSI by applying the orthogonality principle, and corrects the current CSI with this correction term. Analysis and simulation results have shown that this method has no error propagation problem. The performance of the proposed algorithm is much better than the conventional DD channel estimation, and close to the optimal LMMSE estimator, but with much less computational complexity compared with the optimal LMMSE estimator.