Receiver implementation for long codes in 3GPP

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 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.

In this paper, we generalize the concepts of kernels, weak stationarity and white noise from undirected to directed graphs (digraphs) based on the Jordan decomposition of the shift operator. We characterize two types of kernels (type-I and type-II) and their corresponding localization operators for digraphs. We analytically study the interplay of these types of kernels with the concept of stationarity, specially the filtering properties. We also generalize graph Wiener filters and the related optimization framework to digraphs. For the special case of Gaussian processes, we show that the Wiener filtering again coincides with the MAP estimator. We further investigate the linear minimum mean-squared error (LMMSE) estimator for the non-Gaussian cases; the corresponding optimization problem simplifies to a Lyapunov matrix equation. We propose an algorithm to solve the Wiener optimization using proximal splitting methods. Finally, we provide simulation results to verify the provided theory.

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.

In this paper, we investigate the robustness of the Unique Word Orthogonal Frequency Division Multiplexing (UW-OFDM) technique for channel estimation issue. We consider the UW-OFDM technique instead of Zero-Padding (ZP) OFDM one to construct the OFDM symbols for MultiBand Orthogonal Frequency Division Multiplexing Ultra WideBand (MB-OFDM UWB) systems. Then, we propose a two-step LMMSE channel estimator. In the first step, by using the knowledge of UW sequence in the receiver side, the Channel Frequency Response (CFR) is estimated. In the second step, the Linear Minimum Mean-Squared Error (LMMSE) channel estimation is performed using the estimated CFR of the first step and the Channel Estimation Sequence (CES) included in the frame preamble. We evaluate the performance of the proposals in terms of Bit Error Rate (BER) by computer simulations. We show that the consideration of UW-OFDM technique provides, not only reduction of complexity, but also improvement in BER performance. Moreover, we show that the two-step LMMSE channel estimation does not affect the system performance mainly for low Signal-to-Noise Ratio (SNR).

A novel transmit power control (TPC) bit estimation technique for FDD-UMTS downlink is proposed. Conventional FDD-UMTS downlink TPC bit estimation algorithm is based on coherent detection, which utilizes pilot bits for channel estimation and uses this estimate to remove the wireless channel effect on TPC bits. However, joint utilization of pilot and TPC bits can improve the detection performance as well as minimize the inherent overhead caused by excessive pilot bits usage in the transmission. In the proposed algorithm, pilot and TPC bits are used collectively for non-coherent detection of the power control command. Power increase and decrease orders are represented by two orthogonal vectors which are made up of pilot and TPC bits. TPC bit error rate (BER) performance comparisons of conventional and proposed algorithms are presented for various multi-path channels and slot formats. It is shown that in all the slot formats and different channel conditions, the proposed algorithm provides superior performances in terms of TPC bit error rate. This performance gain can go up to 15 dB at practical TPC BER (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ) with certain channel conditions and slot formats.

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.

Orthogonal frequency division multiplexing (OFDM) systems may experience significant intercarrier interference (ICI) when used in time- and frequency-selective, or doubly-selective, channels. In such cases, the classical symbol estimation schemes, e.g., minimum mean-squared error (MMSE) and zero-forcing (ZF) estimation, require matrix inversion that is prohibitively complex for large symbol lengths. An analysis of the ICI generation mechanism leads us to propose a novel two-stage equalizer whose complexity (apart from the FFT) is linear in the OFDM symbol length. The first stage applies optimal linear preprocessing to restrict ICI support and the second stage uses iterative MMSE estimation to estimate finite-alphabet frequency-domain symbols. Simulation results indicate that our equalizer has significant performance and complexity advantages over the classical linear MMSE estimator in doubly-selective channels.

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.

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.

In this paper, we propose a joint channel estimation and symbol detection (JCESD) algorithm relying on message-passing algorithms (MPA) for orthogonal frequency division multiple access (OFDMA) systems. The channel estimation and symbol detection leverage the framework of expectation propagation (EP) and belief propagation (BP) with the aid of Gaussian approximation, respectively. Furthermore, to reduce the computation complexity involved in channel estimation, the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula. Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%, compared with the traditional linear minimum mean square error (LMMSE) algorithm, and can approach to the bit error rate (BER) performance bound of perfectly known channel state information within 0.1 dB.

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