Inaccurate Channel Estimation Research Articles

Joint Channel Estimation and Data Detection for IRS-Assisted OFDM Systems

Intelligent reconfigurable surface (IRS) becomes a promising technology for improving spectrum efficiency and reducing the energy consumption in the next-generation networks. To reap the passive beamforming gain of IRS, accurate channel estimation is essentially required. However, the existing channel estimation methods for the IRS-assisted orthogonal frequency division multiplexing (OFDM) systems suffer from serious performance loss, due to using the least-squares (LS) estimator and ignoring the channel sparsity in the time domain. Another drawback of the existing methods is that the channel estimation and data detection are separated from each other so that any inaccurate channel estimation would cause an error accumulation problem for the data detection. To overcome these drawbacks, we propose a novel joint sparse channel estimation and data detection method for the IRS-assisted OFDM systems. The novelties of the proposed method are twofold: (i) present a new common sparse representation model for the joint channel estimation and data detection, which can utilize the common channel sparsity between the pilots and data streams to significantly improve the channel estimation performance and reduce the training overhead; and (ii) propose an efficient variational Bayesian inference (VBI) framework to simultaneously perform the Bayesian inference for the sparse channel recovery and data detection, where the coupling effect caused by the reflection pattern is trickly evaded by adopting an additional column-independent factorization. Besides exploiting the common sparsity for the channel estimation and data detection, the proposed method also avoids using the LS estimator, and thus its performance can be greatly enhanced. Simulation results verify the superiority of the proposed method.

Sequential Anomaly Detection Against Demodulation Reference Signal Spoofing in 5G NR

In fifth generation (5G) new radio (NR), the demodulation reference signal (DMRS) is employed for channel estimation as part of coherent demodulation of the physical uplink shared channel. However, DMRS spoofing poses a serious threat to 5G NR since inaccurate channel estimation will severely degrade the decoding performance. In this correspondence, we propose to exploit the spatial sparsity structure of the channel to detect the DMRS spoofing, which is motivated by the fact that the spatial sparsity structure of the channel will be significantly impacted if the DMRS spoofing happens. We first extract the spatial sparsity structure of the channel by solving a sparse feature retrieval problem, then propose a sequential sparsity structure anomaly detection method to detect DMRS spoofing. In simulation experiments, we exploit clustered delay line based channel model from 3GPP standards for verifications. Numerical results show that our method outperforms both the subspace dimension based and energy detector based methods.

Impact of Rotary-Wing UAV Wobbling on Millimeter-Wave Air-to-Ground Wireless Channel

Millimeter-wave rotary-wing (RW) unmanned aerial vehicle (UAV) air-to-ground (A2G) links face unpredictable Doppler effect arising from the inevitable wobbling of RW UAV. Moreover, the time-varying channel characteristics during transmission lead to inaccurate channel estimation, which in turn results in the deteriorated bit error probability performance of the UAV A2G link. This paper studies the impact of mechanical wobbling on the Doppler effect of the millimeter-wave wireless channel between a hovering RW UAV and a ground node. Our contributions of this paper lie in: i) modeling the wobbling process of a hovering RW UAV; ii) developing an analytical model to derive the temporal autocorrelation function (ACF) for the millimeter-wave RW UAV A2G link in a closed-form expression; and iii) investigating how RW UAV wobbling impacts the Doppler effect on the millimeter-wave RW UAV A2G link. Numerical results show that different RW UAV wobbling patterns impact the amplitude and the frequency of ACF oscillation in the millimeter-wave RW UAV A2G link. For UAV wobbling, the temporal ACF decreases quickly and the impact of the Doppler effect is significant on the millimeter-wave A2G link.

Effects of SNR-Dependent Beam Alignment Errors on Millimeter-Wave Cellular Networks

At millimeter-wave (mm-wave) frequencies, narrow beamwidth and inaccurate channel estimation result in beam alignment errors, which degrade the achievable signal-to-noise ratio (SNR) and rate. The extent of beam misalignment depends on the various link parameters, however, prior works ignored such a dependence and considered oversimplified array patterns. This paper formulates beam alignment errors for the uniform linear array at the base station as a function of the link parameters by using the Cramér Rao lower bound of angle-of-arrival estimates and proposes a tractable array pattern approximation to better reflect the effects of such alignment errors. Utilizing stochastic geometry and the statistics of the Student’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -distribution, a novel and robust analytical technique is developed to obtain the downlink SNR and rate coverage probabilities due to beam misalignment, assuming a single antenna at the user. We show that beam alignment errors significantly affect the achievable SNR and rate for non line-of-sight (NLOS)/distant users whereas the effect is negligible for LOS/nearby users. It is also illustrated that the effects of alignment errors cannot be mitigated only by increasing the number of antennas. We also show that our proposed array pattern approximation better captures the effects of beam alignment errors as compared to the widely used flat-top pattern.

Interference-Aware Mobile Backscatter Communication: A PHY-Assisted Rate Adaptive Approach

Over the past decade, backscatter nodes have received booming interest for many emerging mobile applications, such as sports analytics and interactive gaming. However, backscatter networks are not ready to provide a high-throughput and stable communication platform for billions of such mobile nodes due to two main factors in rate adaptation. First, the common mapping paradigm that chooses the optimal rate based on RSSIs is hardly adaptable to hardware diversity. Second, the current probing processes are not optimized for mobile scenarios due to inefficient probing trigger, inaccurate channel estimation, and unique self-interference. To address those issues, we propose MobiRate, a mobility-aware rate adaptation link-layer that fully exploits the mobility hints from PHY information to deliver a high-throughput link-layer for mobile backscatter networks. The key insight is that mobility-hints can greatly benefit link-layer design, including rate selection and channel probing. Specifically, we introduce a novel velocity-based loss-rate estimation module, a mobility-assisted probing trigger, a selective probing module, and a robust self-interference detection module, significantly saving probing time and improving probing accuracy. As MobiRate is fully compatible with the current standard, we prototype it using COTS RFID readers and commercial tags. Our extensive experiments demonstrate that MobiRate can successfully identify self-interference with detection accuracy over 90% for tags of different velocities. Moreover, it achieves up to 3.8x throughput gain over the state-of-the-art methods across a wide range of mobility, channel conditions, and tag types.

An exponential smoothing channel estimation method for MIMO-OFDM system

In order to solve the problem of inaccurate channel estimation of least square (LS) algorithm for multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system under time-varying channel, a novel channel estimation method using exponential smoothing algorithm (ESA) is proposed in this paper. Based on LS estimation algorithm and ESA theory, the proposed method fully exploits the variation law of time-varying channel, adopts the predicted results of the previous time to predict the estimation value of the current time by iterative computations, and utilizes the low-pass character of the ESA equivalent filter to reduce the random disturbance. Therefore, the additive white Gaussian noise (AWGN) existing in LS algorithm is effectively suppressed and the estimation accuracy is further improved. Simulation results show that, compared with the traditional channel estimation approaches, the proposed method can provide high estimation accuracy with low computational complexity, especially under time-varying channel and low SNR conditions..

Improved Low-Resolution Quantized SIMO Estimation via Deep Learning

The signal estimation problem in a single input multiple output (SIMO) system with low-resolution analog-to-digital converters (ADCs) is studied. The optimal minimum mean square error (MMSE) estimator is difficult to compute due to the nonlinearity introduced by ADCs, while linear MMSE (LMMSE) suffers from performance degradation. Motivated by the advances in deep learning, a convolutional neural network estimator (CNNE) is proposed, leveraging the highly successful TextCNN. We simulate the system under both accurate and inaccurate channel estimation, and the numerical results suggest that the normalized mean square error performance of CNNE outperforms LMMSE in majority of the cases with less runtime.

Hybrid optimization-based pilot scheduling for reducing pilot contamination in massive MIMO systems

Massive multiple-input multiple-output (MIMO) is an emerging technology used in next-generation cellular networks. The major challenge in the massive MIMO system is the pilot contamination. The contamination of the pilot sequences causes inaccurate channel estimation leading to huge errors in the transmissions. This paper proposes an approach for pilot contamination reduction in massive MIMO systems. In order to reduce the pilot contamination, a pilot scheduling algorithm is devised by proposing an optimization algorithm named Elephant-based Spider Monkey Optimization (ESMO) for scheduling the pilots. The proposed ESMO is designed by combining Elephant Herding Optimization (EHO) into Spider Monkey Optimization (SMO). The pilot scheduling approach employs proposed ESMO and user degradation for scheduling the pilots. Moreover, the optimal pilot scheduling is carried out using the newly devised fitness function that considers achievable rate using various user grouping parameters, such as utility function, and grouping parameter. Thus, the proposed ESMO-based pilot scheduling and fitness function are responsible for initiating optimal pilot scheduling. The performance of the proposed method is compared with the existing methods, and the proposed ESMO outperformed the existing methods with maximal achievable rate value of 39.257[Formula: see text]bps/Hz, and maximal SINR with value 118.75[Formula: see text]dB, respectively.

Frequency-domain averaging-based hybrid channel estimation for short-reach discrete multitone transmission systems

Channel estimation is a key digital signal processing algorithm in optical discrete multitone (DMT) transmission systems. The least-square (LS)-based channel estimation is a simple method, but it is very sensitive to various noises and other interference. The estimation accuracy can be improved using the LS-based intrasymbol frequency-domain averaging (ISFA) channel estimation method. However, there exists inaccurate channel estimation for the edge data subcarriers caused by chromatic dispersion and nonsymmetric averaging windows. We propose a hybrid channel estimation method with LS-based intersymbol frequency-domain averaging and modified ISFA to improve the estimation accuracy of edge data subcarriers and the stability of the bit error rate (BER) performance. The proposed hybrid channel estimation method is experimentally investigated in a short-reach 16-ary quadrature amplitude modulation-encoded DMT transmission system. After 20-km standard single-mode fiber transmission, with the help of the proposed hybrid channel estimation and at a BER of 3.8 × 10 − 3, the receiver sensitivity in terms of the received optical power can be improved by about 1.8 dB compared to that of the conventional ISFA. Furthermore, the proposed hybrid channel estimation has better BER stability.

Exploiting Gaussian Mixture Model Clustering for Full-Duplex Transceiver Design

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to coherently detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit a Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital-domain channel estimation and SIC. The frame structure of the designed FDT contains two successive phases: labeling phase and data transmission phase. In particular, the designed FDT performs cluster labeling in the labeling phase and performs GMM clustering based on an expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the theoretical analysis about the detection performance, computational complexity, and convergence performance for the designed FDT are studied. Finally, simulation results show that the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) or least mean square (LMS) channel estimator.

Enhanced CSI Acquisition for FDD Multi-User Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is envisioned to meet the growing spectral efficiency (SE) requirement in next generation cellular systems. However, it has been recognized that the unaffordable channel state information reference signal (CSI-RS) overhead required for acquiring CSI is a major challenge in frequency division duplexing (FDD) massive MIMO systems. To tackle the challenge of this CSI-RS overhead limitation, certain attempts, which have been designed for the scenarios with dispersive locations of users and sparse nature of channels, suffer from inaccurate channel estimation when the nature of channels for different users becomes highly correlated and rich. In this paper, we propose an enhanced CSI acquisition scheme for FDD multi-user massive MIMO systems, which exploits beamformed CSI-RS transmission mechanism to reduce the CSI-RS overhead. We present two limited feedback algorithms which provide different tradeoffs between system performance and CSI feedback overhead, where the multistage CSI acquisition strategy is adopted to avoid prohibitive computational complexity and amount of feedback requirement. The simulation results show that our CSI acquisition scheme, without additional CSI-RS overhead and enormous CSI feedback overhead, achieves higher SE performance as compared to conventional scheme.

Wiener-based smoother and predictor for massive-MIMO downlink system under pilot contamination

One of the challenges in massive-MIMO system is pilot contamination during the channel estimation process. Pilot contamination can cause error or inaccurate channel estimation process for future fifth generation (5G) downlink transmissions. This paper considers using a Wiener-based filter to smooth and predict the channel estimation to reduce the pilot contamination for more accurate CSI during channel estimation. The simulation results show that the Wiener-based smoothing and predicting technique reduces the effect of pilot contamination and increases the accuracy of CSI during channel estimation process. Wiener smoother (WS) is implemented based on Wiener-based filtering technique. The previous estimated CSI and weight coefficient vector are used to smooth the current estimated CSI by using block data formulation to reduce the effect of pilot contamination. However, WS technique suffers from pilot contamination due to pilot training. This motivates the development of two Wiener predictors (WP), known as WP1 and WP2. The WP1 and WP2 run a prediction technique for CSI and number of pilot training during the prediction period, which is missing from the original WS. Comparison results show that the proposed WS and WP outperforms the conventional minimum mean square error and least square, in terms of channel estimation error and per-cell rate. WP2 perform better than WS and WP1 because of the algorithm complexity that required more information to be updated, stored and processed for prediction. Thus, WP2 requires large computation and matrix operation compared to WS and WP1. The results indicate that the channel estimation error due to pilot contamination can be reduced by using the Wiener-based approaches.

Impact of Mobility on the Uplink Sum Rate of MIMO-OFDMA Cellular Systems

With the fast development of vehicular technologies, the number of mobile terminals as well as their moving speeds has increased conspicuously, making it meaningful to re-examine the impact of mobility on communication performance of the existing cellular systems. In this paper, the uplink sum rate of a multiple-input multiple-output orthogonal frequency-division multiple access (MIMO-OFDMA) cellular system with nodes moving randomly at different speeds is studied. In such a multi-user environment, mobility not only causes inter sub-carrier interferences (ICI) among different users, but also brings the so-called channel aging which together with the above interferences incur outdated and inaccurate channel estimation and thus entail additional pilot overhead. Here, we first derive the relationship between the users’ mobility and the ICI power, characterize the channel aging, and derive the overall multi-user interference caused by the outdated and inaccurate channel state information as a function of mobility. Then, by capitalizing on the above results, the sum rate of the MIMO-OFDMA uplink is theoretically analyzed. Moreover, an adaptive transmission scheme in which the users adjust the pilot percentage according to mobility statistics is proposed to maximize the sum rate. Finally, numerical results are provided to validate our analyses.

Virtual Resource Allocation in Software-Defined Information-Centric Cellular Networks With Device-to-Device Communications and Imperfect CSI

In this paper, we propose an architecture of software-defined information-centric network virtualization with device-to-device (D2D) communications, which facilitates dynamic virtual resource allocation and content caching via a software-defined networking (SDN) controller with a global view of the system. In our proposed framework, substrate physical resources can be virtualized and shared among multiple mobile virtual network operators (MVNOs). Meanwhile, by means of integrating D2D communications into information-centric wireless networks, content caching is enabled not only in the air but in mobile devices as well. In addition, taking into consideration inaccurate channel estimation and measurement, we formulate the virtual resource allocation and caching optimization as a discrete stochastic optimization problem in which imperfect channel state information is incorporated. Because the formulated virtual resource allocation problem is a large-scale combinational optimization problem, we exploit discrete stochastic approximation approaches to cope with it. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme with different system parameters. Simulation results show that MVNOs can benefit from not only the sharing of physical infrastructure but from the caching functionality that exists in both the air and mobile devices as well.

Multiple-Symbol Joint Signal Processing for Differentially Encoded Single- and Multi-Carrier Communications: Principles, Designs and Applications

Bypassing the potentially excessive-complexity and yet inaccurate channel estimation, differentially encoded modulation in conjunction with low-complexity non-coherent detection constitutes a viable candidate for future multiple-antenna aided systems, where estimating all the links may become unrealistic, especially in high-speed environments. Upon exploiting the correlation between the phase distortions experienced by the consecutively transmitted symbols and/or based on mutually and iteratively utilizing the increasingly improved bit reliability information among the associated multiple symbols in the context of differentially modulated systems using channel code aided iterative receivers, the joint processing on consecutively received multiple symbols improves the system's performance. For example, an increased robustness against rapid channel fluctuation, improved flexibility in the system's performance-complexity compromise as well as a reduced performance loss is achieved in comparison to its coherent detection aided counterpart. In order to stimulate further research on differentially modulated systems and on the associated multiple-symbol signal processing based advanced receiver design, a comprehensive review on their related concepts and fundamental principles is carried out in this treatise, followed by a number of potential challenges encountered in their practical implementations in future high-spectral-efficiency wireless transmissions, such as their applications in high-order differentially modulated systems and in differential interference suppression of spatial-division multiplexing/multiple access scenarios.

Group sparse Lasso for cognitive network sensing robust to model uncertainties and outliers

To account for variations in the frequency, time, and space dimensions, dynamic re-use of licensed bands under the cognitive radio (CR) paradigm calls for innovative network-level sensing algorithms for multi-dimensional spectrum opportunity awareness. Toward this direction, the present paper develops a collaborative scheme whereby CRs cooperate to localize active primary user (PU) transmitters and reconstruct a power spectral density (PSD) map portraying the spatial distribution of power across the monitored area per frequency band and channel coherence interval. The sensing scheme is based on a parsimonious model that accounts for two forms of sparsity: one due to the narrow-band nature of transmit-PSDs compared to the large portion of spectrum that a CR can sense, and another one emerging when adopting a spatial grid of candidate PU locations. Capitalizing on this dual sparsity, an estimator of the model coefficients is obtained based on the group sparse least-absolute-shrinkage-and-selection operator (GS-Lasso). A novel reduced-complexity GS-Lasso solver is developed by resorting to the alternating direction method of multipliers (ADMoM). Robust versions of this GS-Lasso estimator are also introduced using a GS total least-squares (TLS) approach to cope with both uncertainty in the regression matrices, arising due to inaccurate channel estimation and grid-mismatch effects, and unexpected model outliers. In spite of the non-convexity of the GS-TLS criterion, the novel robust algorithm has guaranteed convergence to (at least) a local optimum. The analytical findings are corroborated by numerical tests.

Code-Rate-Optimized Differentially Modulated Near-Capacity Cooperation

It is widely recognized that half-duplex-relay-aided differential decode-and-forward (DDF) cooperative transmission schemes are capable of achieving a cooperative diversity gain, while circumventing the potentially excessive-complexity and yet inaccurate channel estimation, especially in mobile environments. However, when a cooperative wireless communication system is designed to approach the maximum achievable spectral efficiency by taking the cooperation-induced multiplexing loss into account, it is not obvious whether or not the relay-aided system becomes superior to its direct-transmission based counterpart, especially, when advanced channel coding techniques are employed. Furthermore, the optimization of the transmit-interval durations required by the source and relay is an open issue, which has not been well understood in the context of half-duplex relaying schemes. Hence, we first find the optimum transmission duration, which is proportional to the adaptive channel-code rate of the source and relay in the context of Code-Rate-Optimized (CRO) TDMA-based DDF-aided half-duplex systems for the sake of maximizing the achievable network throughput. Then, we investigate the benefits of introducing cooperative mechanisms into wireless networks, which may be approached in the context of the proposed CRO cooperative system both from a pure capacity perspective and from the practical perspective of approaching the Discrete-input Continuous-output Memoryless Channel (DCMC) capacity with the aid of the proposed Irregular Distributed Differential (IrDD) coding aided scheme. In order to achieve a near-capacity performance at a low-complexity, an adaptive-window-duration based Multiple-Symbol Differential Sphere Detection (MSDSD) scheme is employed in the iterative detection aided receiver. Specifically, upon using the proposed near-capacity system design, the IrDD coding scheme devised becomes capable of performing within about 1.8 dB from the corresponding single-relay-aided DDF cooperative system's DCMC capacity.

Rate-Compatible SFBC-OFDM under Rapidly Time-Varying Channels

An inter-carrier interference (ICI) occurs in an orthogonal frequency division multiplexing (OFDM) system when a channel is rapidly time-varying. The proposed scheme employs a polynomial cancellation code (PCC) to suppress this ICI, and uses a linear complex field (LCF) code as a transmit-diversity technique to make the transmission rate compatible up to {nT} / {(nT+1)}, where nT is the number of transmit antennas used. The conventional orthogonal code (OC), such as the Alamouti code, with accompanying PCC cannot achieve this rate. Both analysis and simulation results verify that the proposed scheme can achieve higher channel capacity as well as lower bit error rate (BER) than the other schemes in rapidly time-varying channels, even with a linear receiver and inaccurate channel estimation.

Dispensing with Channel Estimation: Differentially Modulated Cooperative Wireless Communications

As a benefit of bypassing the potentially excessive-complexity and yet inaccurate channel estimation, differentially encoded modulation in conjunction with low-complexity non-coherent detection constitutes a viable candidate for user-cooperative systems, where estimating all the links by the relays is unrealistic. In order to stimulate further research on differentially modulated cooperative systems, a number of fundamental challenges encountered in their practical implementations are addressed, including the time-variant-channel-induced performance erosion, flexible cooperative protocol designs, resource allocation as well as its high-spectral-efficiency transceiver design. Our investigations demonstrate the quantitative benefits of cooperative wireless networks both from a pure capacity perspective as well as from a practical system design perspective.

Recursive Channel Estimation Method for OFDM-Based Cooperative Systems

In this letter, we propose a recursive channel estimation scheme for orthogonal frequency division multiplexing (OFDM) over amplify-and-forward (AF) relaying channels. Among pilot-symbol aided channel estimation techniques, linear interpolation is considered to be a simple and practical method. However, linear interpolation for the source-relay-destination channel may not work well due to the severe frequency selectivity of the cascaded channels. Specifically, extrapolation for edge subcarriers significantly distorts the channel estimation performance. To mitigate the detrimental effects of extrapolation, we partially null the channel estimates at the edge subcarriers. The proposed method effectively reduces the error propagation due to inaccurate channel estimates. In addition, we design a low complexity recursive linear interpolation scheme in which each update process utilizes both the reliability of feedback information and the characteristics of linear interpolation.