Multiuser Channel Estimation Research Articles

Common structured sparsity based multi-user channel estimation for double-RIS assisted millimeter wave systems

Compared to a single reconfigurable intelligent surface (RIS) system, a double RIS system can achieve better passive beamforming gain, while requiring more accurate channel state information (CSI). However, due to the presence of double-reflection links, channel estimation with low pilot overhead is quite challenging. In this paper, we investigate channel estimation of double-RIS assisted multi-user millimeter wave (mmWave) communication systems. Firstly, the channel estimation problem for double-reflection links is formulated as a sparse signal recovery problem. Then, we analyze and exploit the common structured sparsity of the cascaded channel among multiple users, i.e., the common row and column sparsity in the angular channel matrix, and the corresponding common sparsity after being transformed into the channel vector. Inspired by this, we further propose a novel common structured sparsity based orthogonal matching pursuit (CSS-OMP) algorithm for multi-user joint channel estimation. Simulation results show that CSS-OMP algorithm can provide more accurate channel estimation with reduced pilot overhead compared to the conventional OMP algorithm.

A Powerful Joint Modulation and STBC Identification Algorithm for Multiuser Uplink SC-FDMA Transmissions

Owing to the rapid development and broad adoption of multiple antenna communication systems over the past few years, space-time block coding (STBC) identification has emerged as a crucial responsibility for smart radios. The majority of previous analysis of STBC identification assumed that the utilized modulation schemes for single-user and uncoded broadcasts were known. This paper investigates the challenge of joint STBC and modulation identification for uplink transmissions with numerous users in single-carrier frequency division multiple access (SC-FDMA) systems using coded transmissions. Multi-user channel estimation brings us one step closer to implementing the proposed design in real-world systems. We additionally employ the channel decoder’s deliverables, which are common in many real-world systems, to enhance the identifying and estimating procedures. Mathematical findings prove that a recursive approach can be utilized to tackle the maximum likelihood (ML) problem of simultaneous STBC and modulation identification with channel estimation. Distinguishing the superimposed signals that originate at the base-station (BS) is accomplished with the use of the space-alternating generalized expectation-maximization (SAGE) algorithm. After that, an expectation-maximization (EM) engine is deployed to make the necessary adjustments to the parameters being considered for each user. The success of the above-mentioned architecture for usage in practical applications is demonstrated by the simulation results obtained under various conditions.

Tensor Dictionary Manifold Learning for Channel Estimation and Interference Elimination of Multi-User Millimeter-Wave Massive MIMO Systems

Millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid analog-digital architectures can greatly increase system capacity and communicate with multiple users at the same time. Accurate channel estimation is crucial for multi-user communications, but its accuracy is limited as the number of antennas and users increases. In the matrix high-dimensional operation for multi-user channel estimation, not only is it computationally intensive, but also the estimation accuracy is low. It makes our work turn to channel estimation of the user group within a certain region to improve the accuracy of estimation. In this paper, we propose a tensor dictionary manifold learning method for channel estimation and interference elimination of the multi-user mmWave massive MIMO system. A multi-user digital-analog mixed received signal model is presented. The tensor dictionary manifold learning scheme is proposed to model the received signal as a third-order low-rank tensor to handle the high-dimensional user, antenna, and channel. After segmentation, clustering and manifold learning, multiple tensor dictionary manifold models containing a group of user signals are fitted. Tensor dictionary manifold learning can take advantage of the inherent multi-domain properties of signals in the frequency, time, code and spatial domains to maintain inter-user correlation within a user group while reducing the high-dimensional channels of the user group. Using the convex relaxation property of the tensor alternating direction method, we propose a strategy to eliminate interference from other groups. And with the help of the multi-signal classification method, the channel parameters of user groups are obtained to improve the accuracy of multi-user channel estimation. This method can perform channel estimation for multiple users with only a few pilots, and improve the performance of the system. Numerical results confirm the good performance of this method.

STBC Identification for Multi-User Uplink SC-FDMA Asynchronous Transmissions Exploiting Iterative Soft Information Feedback of Error Correcting Codes

With the advancement and widespread implementation of multiple-input multiple-output (MIMO) wireless communication systems over the last decade, space-time block coding (STBC) identification has become a critical task for intelligent radios. Previous examinations of STBC identification were focused on single-user transmissions over single-carrier and multi-carrier systems in combination with uncoded broadcasts. Practical systems, on the other hand, contain many users and employ error-correcting codes. For the first time in literature, this work explores the problem of STBC identification for multi-user uplink transmissions in single-carrier frequency division multiple access (SC-FDMA) systems. We take another step closer to real systems by addressing asynchronous transmissions and by conducting multi-user channel estimation. We also exploit the outputs of the channel decoder, which is usually used in many practical systems, to improve the identification and estimation processes. The mathematical analysis demonstrates that the maximum-likelihood (ML) solution of STBC identification, channel estimation, and synchronization can be executed by an iterative approach. The space-alternating generalized expectation-maximization (SAGE) algorithm is used to separate the overlaid signals arriving at the base-station (BS). The parameters under consideration for each user are then updated using an expectation-maximization (EM) processor. Simulation results show that the proposed architecture outperforms other identification methods published in the literature while maintaining a reasonable level of processing time.

Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL.

The compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation is sometimes “unfriendly” to the channel recovery. To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. Simulation results demonstrate that the UTAMP-SBL is able to achieve effective performance improvement than other competitors with low pilot overhead.

Towards Overfitting Avoidance: Tuning-Free Tensor-Aided Multi-User Channel Estimation for 3D Massive MIMO Communications

Channel estimation has long been deemed as one of the most critical problems in three-dimensional (3D) massive multiple-input multiple-output (MIMO), which is recognized as the leading technology that enables 3D spatial signal processing in the fifth-generation (5G) wireless communications and beyond. Recently, by exploring the angular channel model and tensor decompositions, the accuracy of single-user channel estimation for 3D massive MIMO communications has been significantly improved given a limited number of pilot signals. However, these existing approaches cannot be straightforwardly extended to the multi-user channel estimation task, where the base station (BS) aims at acquiring the channels of multiple users at the same time. The difficulty is that the coupling among multiple users' channels makes the channel estimation deviate from widely-used tensor decompositions. It gives a non-standard tensor decomposition format that has not been well tackled. To overcome this challenge, besides directly fitting the new tensor model for channel estimation to the wireless data via block coordinate descent (BCD) method, which is prone to the overfitting of noises or requires regularization parameter tuning, we further propose a novel tuning-free channel estimation algorithm that can automatically control the channel model complexity and thus effectively avoid the overfitting. Numerical results are presented to demonstrate the excellent performance of the proposed algorithm in terms of both estimation accuracy and overfitting avoidance.

Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO

This paper shows that deep neural network (DNN) can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding for a frequency-division duplex massive multiple-input multiple-output system in which a base station (BS) serves multiple mobile users, but with rate-limited feedback from the users to the BS. A key observation is that the multiuser channel estimation and feedback problem can be thought of as a distributed source coding problem. In contrast to the traditional approach where the channel state information (CSI) is estimated and quantized at each user independently, this paper shows that a joint design of pilots and a new DNN architecture, which maps the received pilots directly into feedback bits at the user side then maps the feedback bits from all the users directly into the precoding matrix at the BS, can significantly improve the overall performance. This paper further proposes robust design strategies with respect to channel parameters and also a generalizable DNN architecture for varying number of users and number of feedback bits. Numerical results show that the DNN-based approach with short pilot sequences and very limited feedback overhead can already approach the performance of conventional linear precoding schemes with full CSI.

Energy-Efficient Power Control in Cell-Free and User-Centric Massive MIMO at Millimeter Wave

In a cell-free massive MIMO architecture a very large number of distributed access points simultaneously and jointly serves a much smaller number of mobile stations; a variant of the cell-free technique is the user-centric approach, wherein each access point just serves a reduced set of mobile stations. This paper introduces and analyzes the cell-free and user-centric architectures at millimeter wave frequencies, considering a training-based channel estimation phase, and the downlink and uplink data transmission phases. First of all, a multiuser clustered millimeter wave channel model is introduced in order to account for the correlation among the channels of nearby users; second, an uplink multiuser channel estimation scheme is described along with low-complexity hybrid analog/digital beamforming architectures. Third, the non-convex problem of power allocation for downlink global energy efficiency maximization is addressed. Interestingly, in the proposed schemes no channel estimation is needed at the mobile stations, and the beamforming schemes used at the mobile stations are channel-independent and have a very simple structure. Numerical results show the benefits granted by the power control procedure, that the considered architectures are effective, and permit assessing the loss incurred by the use of the hybrid beamformers and by the channel estimation errors.

Sum-Rate of Multi-User MIMO Systems with Multi-Cell Pilot Contamination in Correlated Rayleigh Fading Channel.

This paper presents some exact results on the sum-rate of multi-user multiple-input multiple-output (MU-MIMO) systems subject to multi-cell pilot contamination under correlated Rayleigh fading. With multi-cell multi-user channel estimator, we give the lower bound of the sum-rate. We derive the moment generating function (MGF) of the sum-rate and then obtain the closed-form approximations of the mean and variance of the sum-rate. Then, with Gaussian approximation, we study the outage performance of the sum-rate. Furthermore, considering the number of antennas at base station becomes infinite, we investigate the asymptotic performance of the sum-rate. Theoretical results show that compared to MU-MIMO system with perfect channel estimation and no pilot contamination, the variance of the sum-rate of the considered system decreases very quickly as the number of antennas increases.

A Pipelined Turbo Receiver for MU-MIMO Communication Systems

The MU-MIMO (multi-user multiple-input-multiple-output) communications can enhance the capacity of mobile communication channels. This paper considers the uplink MU-MIMO transmission systems. We jointly design the multiuser detection, turbo equalization and channel estimation for the considered systems. Our proposed system is with low computational complexity. Existing methods can’t meet the requirements of practical implementation. Pipelined receiver can reduce latency and improve throughput, thus pipelined receiver is a good choice for implementation. For the very-large-scale integration (VLSI) hardware implementation, the pipelined architecture can be used to separate the equalizer and decoder into several sub-blocks. The processing time can be shortened and the throughput of system can be increased. The proposed iterative receiver is pipelined structure. The proposed method improves the accuracy of the channel impulse response estimation and the multiuser detection (MUD).

Regularized Multipath Matching Pursuit for Sparse Channel Estimation in Millimeter Wave Massive MIMO System

Sparse channel estimation is investigated for millimeter wave massive MIMO systems, where a base station equipped with a uniform planar array serves several single-antenna users. At first, the 2-D multiuser channel estimation is formulated as several sparse recovery problems. Then a regularized multipath matching pursuit (RMMP) algorithm is proposed for sparse channel estimation. Compared to the existing multipath matching pursuit (MMP) algorithm, a regularization step is introduced in RMMP to screen the candidate paths, which can reduce the computational complexity as well as the storage overhead. Simulation results show that the proposed RMMP algorithm outperforms the existing orthogonal matching pursuit and orthogonal least squares algorithms. In particular, RMMP has the same sparse channel estimation performance as MMP while the computational complexity of the former is much lower than the latter.

Channel Estimation Capacity Enhancement for Multigroup Multicasting Multimedia Networks With DnCNN

In Time Domain Duplex (TDD) massive MIMO systems, multi-group multi-casting becomes a promising technology since it supports services of mass content distribution. Based on the nature of transmitting common message to groups of users simultaneously, there exists a rich literature discussing the resource allocation under various constraints. However, the practical acquisition of CSI has not been fully explored when the number of multi-groups is large and the band is narrow. The insufficient sounding reference signal resources lead to the limited Channel Estimation Capacity (CEC). Under this case, even with Multi-User (MU) channel estimation techniques, some users still cannot be estimated in-timely, which introduces degradation. Aiming at this problem, in this paper we provide a preliminary exploration on CEC enhancement. Based on Denoising Convolutional Neuron Network (DnCNN), which is recently proposed and has succeeded in image denoising, we propose MU-DnCNN Channel Estimation (M-DnCNN CE). M-DnCNN CE includes three parts. First, we modify the utilization of SRS sequences. Then we establish the feature maps and propose M-DnCNN to denoise the signals. Finally, a matched channel restoration method is provided. The practical 3-D MIMO channel model is utilized to evaluate the performance. Compared with DFT-based and conjugated separation methods, results show that the performance of M-DnCNN CE is robust and superior, and the CEC is remarkably improved on the premise of satisfying latency constraint.

Distributed compressed sensing‐based multiuser channel estimation for frequency‐selective hybrid mmWave MIMO system

SummaryThe multiuser millimeter wave (mmWave) MIMO system has been considered as a promising technology for future 5G cellular networks since high volume of data rates can be achieved by simultaneously supporting multiple users. However, the channel estimation is challenging due to the multiuser interference in frequency‐selective hybrid mmWave MIMO system. In this paper, we focus on the multiuser uplink wideband channel estimation with frequency selectivity. Specifically, a layer‐precoder reuse strategy is proposed, in which each transmission frame is divided into several subframes and the users share a common precoder at each subchannel of different subcarriers. Then, instead of only estimating channel of one user, multiuser frequency‐selective channel can be jointly estimated via a sparse recovery formulation. Furthermore, by exploiting the common sparsity among subchannels in frequency‐selective mmWave system, a distributed compressed sensing (DCS)–based channel estimation is proposed to jointly acquire the channel state information of all users via weighted least square to further reduce the pilot overhead. In addition, the Cramer‐Rao lower bound (CRLB) is derived for multiuser frequency‐selective channel estimation. Simulation results demonstrate that the proposed DCS‐based method can asymptotically approach the CRLB of channel estimation.

Communication Using a Large-Scale Array of Ubiquitous Antennas: A Geometry Approach

The recent trends of densification and centralized signal processing in radio access networks suggest that future networks may comprise ubiquitous antennas coordinated to form a network-wide gigantic array, referred to as the ubiquitous array (UA). In this paper, the UA communication techniques are designed and analyzed based on a geometric model. Specifically, the UA is modeled as a continuous circular/spherical array enclosing target users and free-space propagation is assumed. First, consider the estimation of multiuser UA channels induced by user locations. Given single pilot symbols, a novel channel estimation scheme is proposed that decomposes training signals into Fourier/Laplace series and thereby translates multiuser channel estimation into peak detection of a derive function of location. The process is shown to suppress noise. Moreover, it is proved that estimation error due to interference diminishes with the increasing minimum user-separation distance following the power law, where the exponent is 1/3 and 1 for the circular and spherical UA, respectively. If orthogonal pilot sequences are used, channel estimation is found to be perfect. Next, consider channel-conjugate data transmission that maximizes received signal power. The power of interference between two users is shown to decay with the increasing user-separation distance sub-linearly and super-linearly for the circular and spherical UA, respectively. Furthermore, a novel multiuser precoding design is proposed by exciting different phase modes of the UA and controlling the mode weight factors to null interference. The number of available degrees of freedom for interference nulling using the UA is proved to be proportional to the minimum user-separation distance.

Asynchronous Multiuser Reception for OFDM in Underwater Acoustic Communications

Recently significant progress has been made on point-to-point underwater acoustic communications, and the interest has grown on the application of those techniques in multiuser communication settings, where the asynchronous nature of multiuser communication poses a grand challenge. This paper develops a time-asynchronous multiuser reception approach for orthogonal frequency-division multiplexing (OFDM) transmissions in underwater acoustic channels. The received data burst is segmented and apportioned to multiple processing units in an overlapped fashion, where the length of the processing unit depends on the maximum asynchronism among users on the OFDM block level. Interference cancellation is adopted to reduce the interblock interference between overlapped processing units. Within each processing unit, the residual inter-block interference from multiple users is aggregated as one external interference which can be parameterized. Multiuser channel estimation, data detection, and interference mitigation are then carried out in an iterative fashion. Simulation and emulated experimental results demonstrate the robustness of the proposed receiver with signal asynchronism among multiple users in both time-invariant and time-varying environments. It is observed that the receiver decoding performance degrades as the channel time variation and the maximum relative delay among users increase.

An adaptive least squares channel estimation based on conjugate gradient approach for multipath fading CDMA systems

Multiple access interference (MAI) is the main factor affecting the performance of channel estimation techniques for code division multiple access (CDMA) systems. Although, several multi-user channel estimation algorithms have been proposed to mitigate MAI, these algorithms require high computational complexities. In this paper, we address the problem of iterative least squares (LS) mobile channel estimation at high channel efficiency that requires a short training sequence along with the spreading sequences. We employ an efficient iterative method based on conjugate gradient (CG) algorithm to reduce the computational complexity of the estimation method. Computer simulations illustrate that the proposed method performs almost identical to the exact LS estimate for reasonable training lengths.

Jointly multi‐user detection and channel estimation with genetic algorithm

AbstractThis work aims at proposing the use of the evolutionary computation methodology in order to jointly solve the multi‐user channel estimation (MuChE) and detection problems at its maximum‐likelihood, both related to the direct sequence code division multiple access (DS/CDMA). The effectiveness of the proposed heuristic approach is proven by comparing performance and complexity merit figures with that obtained by traditional methods found in literature. Simulation results considering genetic algorithm (GA) applied to multipath, DS/CDMA and MuChE and multi‐user detection (MuD) show that the proposed genetic algorithm multi‐user channel estimation (GAMuChE) yields a normalized mean square error estimation (nMSE) inferior to 11%, under slowly varying multipath fading channels, large range of Doppler frequencies and medium system load, it exhibits lower complexity when compared to both maximum likelihood multi‐user channel estimation (MLMuChE) and gradient descent method (GrdDsc). A near‐optimum multi‐user detector (MuD) based on the genetic algorithm (GAMuD), also proposed in this work, provides a significant reduction in the computational complexity when compared to the optimum multi‐user detector (OMuD). In addition, the complexity of the GAMuChE and GAMuD algorithms were (jointly) analyzed in terms of number of operations necessary to reach the convergence, and compared to other jointly MuChE and MuD strategies. The joint GAMuChE–GAMuD scheme can be regarded as a promising alternative for implementing third‐generation (3G) and fourth‐generation (4G) wireless systems in the near future. Copyright © 2010 John Wiley & Sons, Ltd.

Effects of Channel Estimation on Multiuser Virtual MIMO-OFDMA Relay-Based Networks

A practical multiuser cooperative transmission scheme denoted as Virtual Maximum Ratio Transmission (VMRT) for multipleinput multiple-output-orthogonal frequency division multiple access (MIMO-OFDMA) relay-based networks is proposed and evaluated in the presence of a realistic channel estimation algorithm and using low-density parity-check (LDPC) codes. It is shown that this scheme is robust against channel estimation errors. It offers diversity and array gain, keeping the complexity low with a multiuser and multiantenna channel estimation algorithm that is simple and efficient. In addition, the combination with LDPC codes provides improved gains; diversity gains larger than 6 dB can be easily obtained with a reduced number of relays. Thus, this scheme can be used to extend coverage or increase system throughput by using simple cooperative OFDMA-based relays.

Frame structure for DTTB uplink systems using novel training sequences

A new frame structure based on time-domain synchronous orthogonal frequency division multiplexing for digital terrestrial television broadcasting (DTTB) uplink systems is proposed, whereby multicarrier pseudonoise (PNMC) training sequences are used as the frame header (FH). Sub-sequences of FH are inserted between signal frames as guard intervals. Simple multi-user channel estimation and equalisation can be applied and good performance is illustrated by simulations.

Performance improvement in detection and estimation of MC-CDMA systems over MIMO channels

Multiple-input Multiple-output (MIMO) multi-carrier code division multiple access (MC-CDMA) is a strong candidate for the downlink of future mobile communications to obtain high data rates. Nevertheless, during any transmission over fading channel, performance of MC-CDMA systems are highly degraded due to the presence of multiple access interference (MAI). Multi-user detection (MUD) and channel estimation play a major role in overcoming MAI and characterising the channel, respectively. In this paper, space time serial interference cancellation (STSIC) detection using random and Gold codes and turbo aided iterative channel estimation (ICE) techniques are extended for MC-CDMA system MIMO channels to overcome MAI. Simulation results show STSIC outperforms optimal MUD and linear MUD techniques in mitigating MAI and turbo aided ICE surpasses ICE in characterising the channel with reduced error rates.