Conventional Estimation Schemes Research Articles

Improved spectral efficiency in massive MIMO ultra-dense networks through optimal pilot-based vector perturbation precoding

In multi-user massive MIMO (mMIMO) Ultra Dense Networks (UDN), the acquisition of Channel State Information (CSI) is crucial at the transmitter to maximize the estimation, but it suffers from increased overhead and complexity. The processing of CSI from imperfect channels further increases the complexity of precoders. Because of the complexity, it is hard to find the optimal perturbing vector, which increases the transmit power with reduction in spectral efficiency. This research improves the spectral efficiency of mMIMO UDN using optimal pilot-based vector perturbation (OPVP) precoding. The optimal pilot design senses the CSI intelligently for feedback into the transmitter, and the perturbing signal, for transmission and efficient reception, is selected optimally using Evolutionary Chaotic Behavior (ECB). This integrated OPVP precoding uses compressive sensing to select the low-dimensional CSI, based on which the EEA allows the generation of perturbing signals within its constellation bounds to get efficiently detected with reduced transmit power, computational complexity, and overhead in comparison with the conventional CSI estimation schemes. The simulation is carried out in Matlab to evaluate if the OPVP scheme makes the transmission in MU-MIMO UDN a spectrally efficient one. Compared to state-of-the-art precoding schemes, the integrated model reduces the feedback overhead and avoids high-dimensional CSI recovery.

Time-Varying Channel Estimation Scheme for Uplink MU-MIMO in 6G Systems

Channel estimation is a challenging issue for millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) in the future sixth generation (6G) wireless systems, where the conventional estimation schemes may fail to track the fast varying channels, especially in high-speed mobile scenarios. In this paper, a novel tensor-based uplink channel estimation scheme is proposed for multi-user MIMO (MU-MIMO) systems over time-varying channels. In the proposed scheme, a low-overhead pilot transmission scheme is designed to track the varying channel. The received uplink signal at the base station (BS) is formulated as a third-order tensor which admits a CANDECOMP/PARAFAC (CP) model. The CP decomposition issue is then solved using blind matrix decomposition, in which the special structures of signals in the time dimension and the matrix subspace are utilized. By exploiting low-rank structure of the signal tensor, the channel parameters (angles of arrival/departure, path gains, and Doppler shifts) are estimated from the factor matrices. Moreover, the proposed scheme is theoretically analyzed and it is guaranteed with low pilot overhead. Simulation results verify that the proposed scheme can outperform the compressed sensing (CS) based scheme and the iteration-based scheme in terms of accuracy and stability. The uniqueness of the proposed scheme is also verified in our simulation.

Spatially-Correlated Massive MIMO Channel: An Iterative Estimation Algorithm

In this letter, we propose an iterative algorithm for the joint estimation of the channel covariance matrices and the channel vector for the spatially-correlated massive MIMO system with the following main advantages: low pilot overhead, improved mean square error (MSE), and low complexity. The results show that this scheme outperforms the conventional estimation schemes in terms of the required pilot overhead and the resulting MSE. The results also show that the achievable sum rate of the proposed scheme tends to that of the ideal case that the channel covariance matrices are assumed to be perfectly known at the base station.

Indoor Crowd Estimation Scheme Using the Number of Wi-Fi Probe Requests under MAC Address Randomization

As smartphones have become widespread in the past decade, Wi-Fi signal-based crowd estimation schemes are receiving increased attention. These estimation schemes count the number of unique MAC addresses in Wi-Fi signals, hereafter called probe requests (PRs), instead of counting the number of people. However, these estimation schemes have low accuracy of crowd estimation under MAC address randomization that replaces a unique MAC address with various dummy MAC addresses. To solve this problem, in this paper, we propose an indoor crowd estimation scheme using the number of PRs under MAC address randomization. The main idea of the proposed scheme is to leverage the fact that the number of PRs per a unit of time changes in proportion to the number of smartphones. Since a smartphone tends to send a constant number of PRs per a unit of time, the proposed scheme can estimate the accurate number of smartphones. Various experiment results show that the proposed scheme reduces estimation error by at most 75% compared to the conventional Wi-Fi signal-based crowd estimation scheme in an indoor environment.

Spectrum Sensing Scheme Measuring Packet Lengths of Interfering Systems for Dynamic Spectrum Sharing

For the dynamic spectrum sharing (DSS), this paper proposes a spectrum sensing scheme that measures packet lengths of interfering systems. DSS requires spectrum sensing techniques to measure a length of time for which the interfering systems use a channel during observation time. A ratio of the length of time to the observation time is referred to as the channel occupation ratio (COR). When the observation time is limited, a conventional estimation scheme suffers from large errors of the measured COR. To cope with this problem, another conventional scheme divides the observation time into multiple short slots so as to estimate mean squared estimation errors of COR. However, this conventional scheme cannot accurately estimate errors, because it does not consider the time length of packets. Therefore, this paper proposes a scheme to estimate the variance of measured COR by measuring how long packets from the interfering systems can be observed, which can estimate errors much more accurately than the conventional scheme. Computer simulations evaluate how the observation time affects the standard deviation of the measured COR. It is also demonstrated that the theoretical results of the proposed scheme agree with those of the simulations when the true value of COR is less than 0.4.

Iterative Carrier Frequency Offset Estimation Scheme for Faster-Than-Nyquist Signaling Systems

As a nonorthogonal mode, faster-than-Nyquist (FTN) signaling transmission can effectively improve the spectrum efficiency of a system. However, FTN signaling inevitably causes inter-symbol interference (ISI). The ISI hinders traditional synchronization methods from operating accurately, and this makes the carrier synchronization of FTN systems more complex. To overcome this issue, we propose an iterative carrier frequency offset (CFO) estimation scheme for an FTN signaling system. The proposed CFO estimation scheme consists of two steps: i) a coarse estimation step based on discrete Fourier transform and ii) a fine estimation step based on a golden-section search algorithm. The simulation results show that the mean square error of the proposed scheme is lower than that of the conventional CFO estimation schemes and approaches the Cramer-Rao Bound, which is an optimal estimation performance bound.

Semi-Blind Post-Equalizer SINR Estimation and Dual CSI Feedback for Radar-Cellular Coexistence

Current cellular systems use pilot-aided statistical-channel state information (S-CSI) estimation and limited feedback schemes to aid in link adaptation and scheduling decisions. However, in the presence of pulsed radar signals, pilot-aided S-CSI is inaccurate since interference statistics on pilot and non-pilot resources can be different. Moreover, the channel will be bimodal as a result of the periodic interference. In this paper, we propose a max-min heuristic to estimate the post-equalizer SINR in the case of non-pilot pulsed radar interference, and characterize its distribution as a function of noise variance and interference power. We observe that the proposed heuristic incurs low computational complexity, and is robust beyond a certain SINR threshold for different modulation schemes, especially for QPSK. This enables us to develop a comprehensive semi-blind framework to estimate the wideband SINR metric that is commonly used for S-CSI quantization in 3GPP Long-Term Evolution (LTE) and New Radio (NR) networks. Finally, we propose dual CSI feedback for practical radar-cellular spectrum sharing, to enable accurate CSI acquisition in the bimodal channel. We demonstrate significant improvements in throughput, block error rate and retransmission-induced latency for LTE-Advanced Pro when compared to conventional pilot-aided S-CSI estimation and limited feedback schemes.

Iterated posterior linearization filters and smoothers with cross-correlated noises

In the article, several concise and efficient iterated posterior linearization filtering and smoothing methodologies are proposed for nonlinear systems with cross-correlated noises. Based on the Gaussian approximation (GA), the presented methods are derived via performing statistical linear regressions (SLRs) of the nonlinear state-space models w.r.t the current posterior distribution in an iterated way. Various posterior linearization methods can be developed by employing different approximation computation approaches for the Gaussian-weighted integrals encountered in SLRs. These new estimation methods enjoy not only the accuracy and robustness of the GA filter but also the lower computational complexity. Estimation performances of the designed methods are illustrated and compared with conventional estimation schemes by two common numerical examples.

Machine-Learning-Based Read Reference Voltage Estimation for NAND Flash Memory Systems Without Knowledge of Retention Time

To achieve a low error rate of NAND flash memory, reliable reference voltages should be updated based on the accurate knowledge of program/erase (P/E) cycles and retention time, because those severely distort the threshold voltage distribution of memory cell. Due to the sensitivity to the temperature, however, a flash memory controller is unable to acquire the exact knowledge of retention time, meaning that it is challenging to estimate accurate read reference voltages in practice. In this article, we propose a novel machine-learning-based read reference voltage estimation framework for the NAND flash memory systems without the knowledge of retention time. To establish an unknown input-output relation of the estimation model, we derive input features by sensing and decoding memory cells in the minimum read unit. In order to define the relation between unlabeled input features and a pre-assigned class label, namely label read reference voltages, we propose three mapping functions: 1) $k$ -nearest neighbors-based, 2) nearest-centroid-based, and 3) polynomial regression-based read reference voltage estimators. For the proposed estimation schemes, we analyze that the storage overhead and computational complexity are increasing function of the exploited feature dimension. Accordingly, we propose a feature selection (or dimension reduction) algorithm to select the minimum dimension and corresponding features to reduce the overhead and complexity while maintaining high estimation accuracy. Based on extensive numerical analysis, we validate that the derived features successfully replace unknown knowledge of retention time, and the proposed feature selection algorithm precisely adjusts the trade-off between overhead/complexity and estimation accuracy. Furthermore, the simulation and analysis results show that the proposed framework not only outperforms the conventional estimation schemes but also achieves the near-optimal frame error rate while sustaining low latency performance.

Efficient Weighted Least-Squares Frequency Synchronization Scheme for Digital Terrestrial Broadcasting System

The integrated services digital broadcasting-terrestrial (ISDB-T) system provides the interesting advantages of hierarchical transmission and partial reception using band segmented transmission orthogonal frequency division multiplexing scheme. In ISDB-T, the hierarchical configuration varies depending on whether a segment is modulated coherently or differentially. Thus, it is important to accomplish frequency synchronization regardless of segment type. For this purpose, an effective weighted least-squares (WLS) carrier frequency synchronization method is presented using transmission and multiplexing configuration control (TMCC) and auxiliary channel (AC) in a blind manner. Thus, the proposed estimation scheme is performed without resorting to a priori knowledge on segment type that can be known after correct TMCC decoding. To eliminate bias caused by using non-uniformly distributed TMCC and AC subcarriers, an appropriate subset is selected to form the basis of robust WLS estimation. Extensive simulations are conducted to verify the efficiency of the proposed WLS estimation method and compare its performance with that of the conventional WLS estimation scheme.

Reliable State Estimation of an Unmanned Aerial Vehicle Over a Distributed Wireless IoT Network

Unmanned aerial vehicles (UAVs) have attracted a lot of attention due to their enormous potentiality in civil and military applications over the past years. In order to allow accurate control action of UAV, a robust and real-time state estimation technique is required. In this paper, we propose a Kalman filter based UAV state estimation technique when the communication takes place over wireless links in an Internet of Things (IoT) network. We consider that a set of sensors observes the state of the UAV and transmits the observation to a control center (central server) over a distributed wireless IoT network. To deal with the communication impairments due to wireless communication links between the UAV's sensors and the IoT system components, e.g., IoT gateways, a Bose–Chaudhuri–Hocquenghem coded communication system is presented. Based on the received signals at the IoT gateways, a global state estimation technique is proposed. Performance of the proposed communication and estimation scheme is demonstrated through numerical results for different conditions. From the comparison with a conventional estimation scheme, it is observed that the proposed scheme significantly outperforms the conventional scheme in terms of state estimation and error performance.

Joint Estimation of Symbol Timing and Sampling Frequency Offset for CDD-OFDM-Based DRM Systems

In this paper, an efficient symbol timing offset (STO) and sampling frequency offset (SFO) estimation scheme is proposed in an orthogonal frequency division multiplexing-based digital radio mondiale system using cyclic delay diversity. By using a periodic nature of channel transfer function, cyclic delay and pilot pattern with a maximum channel power are chosen, which aids to enable a robust estimation of STO and SFO against frequency selectivity of the channel. As a performance measure, the mean squared error of the proposed scheme is theoretically derived and is verified through simulations. By simulation results, the proposed estimation scheme is proved to profit from properly selected cyclic delay and pilot pattern, with a superior performance over the conventional estimation scheme.

Measuring bandwidth and buffer occupancy to improve the QoE of HTTP adaptive streaming

Live and on-demand video streaming service systems consume significant portion of Internet traffic all over the world. HTTP adaptive streaming is becoming the de-facto standard for adaptive video streaming solutions. Conventional estimation scheme for bandwidth estimation is not appropriate to estimate the bandwidth when multiple clients compete for a common bottleneck link, due to the ON–OFF traffic pattern. They overestimate the network bandwidth which leads to degradation of quality of experience by unnecessary changes in video quality, average video quality and unfairness of video quality. In this paper, we proposed receiver-side bandwidth-measured method to achieve a better quality of experience in multiple-client scenario. The proposed method estimates the obtainable network bandwidth based on the buffer status and segment throughput. The video buffer model is associated with three thresholds (i.e., one for initial start-up and two for operating thresholds). NS-3 network simulator has been deployed to measure the performance of HTTP adaptive streaming. Simulation outputs reflect that the proposed method enhances the quality of experience than conventional methods.

Anti-Disturbance Control for Nonlinear Systems Based on Interval Observer

This paper proposes a disturbance interval estimation approach for nonlinear systems. Distinct from the conventional estimation schemes aiming to estimate the magnitude of the disturbance, the disturbance interval estimation approach is capable of approximating the time-varying upper and lower boundaries for the disturbance. To achieve this, the dynamics of the boundary estimation errors are constructed as positive systems by introducing a time-invariant coordinate transformation. The boundary estimation errors are guaranteed to be nonnegative and bounded. Then, the generated interval is capable of covering all possible values of the current disturbance and the interval width can provide a quantized guidance on the estimation accuracy at any time. Furthermore, a general antidisturbance control scheme is summarized in light of the proposed disturbance interval estimation approach. As an illustrative case, an antidisturbance control method is developed by integrating the proposed estimation approach and the sliding mode control. Finally, the proposed disturbance interval estimation approach and the anti-disturbance control method are demonstrated through both the numerical and the experimental validation.

Complexity Effective Sampling Frequency Offset Estimation Method for OFDM-Based HomePlug Green PHY Systems

The HomePlug Green PHY (HomePlug GP) specification provides an attractive solution to enable smart grid power line communication (PLC) applications by using robust orthogonal frequency division multiplexing (ROBO) mode. This paper proposes a computationally efficient sampling frequency offset (SFO) estimation technique in the HomePlug GP system without relying on pilot symbols. For this purpose, the proposed estimation scheme utilizes the redundant information contained within the repeat coding in the HomePlug GP ROBO mode, thus eliminating the need of dedicated pilots. Computer simulations are conducted to assess the performance of the proposed SFO estimation scheme and to compare it with the conventional decision-directed (DD) estimation schemes. Simulations indicate that the repeat coded ROBO signals are effectively used for the proposed estimation scheme, which provides an affordable estimation accuracy while reducing the complexity compared to the conventional DD estimation schemes.

Robust, Intrinsic Tracking of a Laparoscopic Ultrasound Probe for Ultrasound-Augmented Laparoscopy.

In situ visualization of laparoscopic ultrasound in both conventional and robot-assisted laparoscopic surgery requires robust and efficient computation of the pose of the laparoscopic ultrasound probe with respect to the laparoscopic camera. Image-based intrinsic methods of computing this relative pose need to overcome challenges due to irregular illumination, partial feature occlusion, and clutter that are unavoidable in practical laparoscopic surgery. In this paper, we propose an accurate image-based method that is robust to partial occlusion of the fiducials and outliers. The method is extended to multi-view imaging model with applications in stereoscopic laparoscopy and robot-assisted surgery. Rather than treating the model-to-image correspondence and pose computation as separate problems, we solve them jointly using the Kalman Filter-based framework that demonstrates video rate running time (~24fps). By keeping the optical tracking measurements as a reference, we demonstrate that the proposed methods result in clinically acceptable tracking accuracy, reaching target registration errors well below 1.5mm on average. In addition, our multi-view tracking method is compared to a conventional stereo triangulation-based pose estimation scheme that commercial optical tracking systems are based on, to experimentally demonstrate its superiority in terms of accuracy. Finally, we qualitatively demonstrate the suitability of our methods for practical laparoscopic applications by conducting a phantom-based experiment.

Sliding‐mode observer‐based speed‐sensorless vector control of linear induction motor with a parallel secondary resistance online identification

This study proposes a speed estimation scheme for the sensorless-vector-controlled linear induction motor (LIM) drives for medium-low-speed maglev applications, which is composed of two parts: (i) a sliding mode model reference adaptive system observer for speed estimation; and (ii) a parallel secondary resistance online identification for achieving the improvements of the proposed speed estimation scheme performance. The sliding mode observer (SMO) is established on the basis of the state space-vector model of the LIM considering the dynamic end effect. Based on SMO, both speed and secondary resistance estimation algorithms are obtained by utilising Popov's hyperstability theory. Moreover, the Lyapunov stability theory is adopted for the stability analysis of the proposed speed estimation scheme. The effectiveness of the proposed speed estimation algorithm has been verified and compared with the performance of the conventional speed estimation scheme based on single-manifold SMO by the simulation and hardware-in-the-loop tests.

Simultaneous Mean and Covariance Correction Filter for Orbit Estimation.

This paper proposes a novel filtering design, from a viewpoint of identification instead of the conventional nonlinear estimation schemes (NESs), to improve the performance of orbit state estimation for a space target. First, a nonlinear perturbation is viewed or modeled as an unknown input (UI) coupled with the orbit state, to avoid the intractable nonlinear perturbation integral (INPI) required by NESs. Then, a simultaneous mean and covariance correction filter (SMCCF), based on a two-stage expectation maximization (EM) framework, is proposed to simply and analytically fit or identify the first two moments (FTM) of the perturbation (viewed as UI), instead of directly computing such the INPI in NESs. Orbit estimation performance is greatly improved by utilizing the fit UI-FTM to simultaneously correct the state estimation and its covariance. Third, depending on whether enough information is mined, SMCCF should outperform existing NESs or the standard identification algorithms (which view the UI as a constant independent of the state and only utilize the identified UI-mean to correct the state estimation, regardless of its covariance), since it further incorporates the useful covariance information in addition to the mean of the UI. Finally, our simulations demonstrate the superior performance of SMCCF via an orbit estimation example.

A Bandwidth Estimation Scheme to Improve the QoE of HTTP Adaptive Streaming in the Multiple Client Environment

HTTP adaptive streaming (HAS) is a promising technology for delivering video content over the Internet. HAS-based video streaming solutions rely on bandwidth estimation to select the appropriate video bitrate. Video streaming solutions that consider network conditions provide users with seamless video playback. However, when multiple clients compete for a common bottleneck link, conventional bandwidth estimation schemes that consider only one client overestimate the network bandwidth due to the ON-OFF traffic pattern. The bandwidth overestimation can cause Quality of Experience (QoE) degradation, such as unnecessary changes in video quality, and unfairness of video quality. In this paper, we propose a client-side bandwidth estimation scheme to obtain a better QoE of HAS in the multiple-client environment. The proposed scheme differentiates the client buffer status according to the buffer occupancy, and then estimates the available network bandwidth based on the buffer status and segment throughput. We evaluate the performance of HAS implemented in the ns-3 network simulator. Simulation results show that compared with the conventional schemes, the proposed scheme can enhance the QoE.

Robust Sampling Frequency Offset Estimation for OFDM over Frequency Selective Fading Channels

Digital radio mondiale (DRM) is a terrestrial radio broadcasting standard to replace existing analogue AM and FM broadcasting, which is based on an orthogonal frequency division multiplexing (OFDM) technique. This paper focuses on the issue of estimating a sampling frequency offset (SFO) in OFDM-based broadcasting systems under frequency selective fading channels. In order to design a robust SFO estimation scheme and to benchmark its performance, the performance of the various conventional SFO estimation schemes is discussed and some improvements on the conventional estimation algorithms are highlighted. The simulation results show that such a design enhances the robustness of the proposed scheme against frequency selective fading.