Channel Tracking Research Articles

V2I 통신에서 기계학습 기반 밀리미터파 채널 추정 및 추적 알고리즘

V2I 통신에서 기계학습 기반 밀리미터파 채널 추정 및 추적 알고리즘

Time-Varying Downlink Channel Tracking for Quantized Massive MIMO Networks

This paper proposes a Bayesian downlink channel estimation framework for time-varying massive MIMO networks. In particular, the quantization effects at the receiver are considered. In order to fully exploit the sparsity and time correlations of channels, we formulate the time-varying massive MIMO channel as the simultaneously sparse signal model. Then, we propose a sparse Bayesian learning (SBL) framework to estimate the model parameters of the sparse virtual channel. The expectation maximization (EM) algorithm is employed to reduce complexity. Specifically, the factor graph and the general approximate message passing (GAMP) algorithms are used to compute the desired posterior statistics in the expectation step, so that high-dimensional integrals over the marginal distributions can be avoided. The non-zero supporting vector of the virtual channel is then obtained from channel statistics by a k-means clustering algorithm. After that, the reduced dimensional GAMP-based scheme is designed to make the full use of the channel temporal correlation so as to enhance the virtual channel tracking accuracy. Finally, the efficacy of the proposed framework is demonstrated through simulations.

Investigation of a channel tracking based time reversal processing for underwater acoustic communication

This paper presents an investigation of a channel tracking based passive time reversal (PTR) communication method for mobile single-input/multiple-output communications in rapidly time-varying underwater acoustic channel. To investigate its performance in the non-stationary channels, the method is applied to a synthetic dataset generated by the underwater acoustic moving source model using normal mode method. The transmitted signal is modulated by the single-carrier modulation scheme using the carrier frequency of 20 kHz and bandwidth of 4, 8, and 12 kHz. From the comparisons with conventional PTR approach, the following advantages were found: (1) the quality of the equalization result hardly degrades even if the sound source moving speed increased, (2) suboptimal diversity combining is performed in the sense of the energy utilization of the multipath, (3) the dependence on the bandwidth is relatively small, and a significant performance improvement is seen especially for narrowband signals.

Fault Detection and Exclusion Method for a Deeply Integrated BDS/INS System.

The Inertial Navigation System (INS) is often fused with the Global Navigation Satellite System (GNSS) to provide more robust and superior navigation service, especially in degraded signal environments. Compared with loosely and tightly coupled architectures, the Deep Integration (DI) architecture has better tracking and positioning performance. Information is shared among channels, and the assistant information from INS helps to reduce the dynamic stress of tracking loops. However, this vector tracking architecture may result in easy propagation of errors among tracking channels. To solve this problem, a Fault Detection and Exclusion (FDE) method for the deeply integrated BeiDou Navigation Satellite System (BDS)/INS navigation system is proposed in this paper. This method utilizes pre-filters’ outputs and integration filter’s estimations to form test statistics. These statistics can help to detect and exclude both step errors and Slowly Growing Errors (SGEs) correctly. The monitoring capability of the method was verified by a simulation which was based on a software receiver. The simulation results show that the proposed FDE method works effectively. Additionally, the method is convenient to be implemented in real-time applications because of its simplicity.

BEM-PSP for Single-Carrier and SC-FDMA Communication Over a Doubly Selective Fading Channel

In this paper, we consider pilot-aided channel estimation and equalization for single-carrier and single-carrier frequency division multiple-access (SC-FDMA) transmission over doubly-selective channels (DSC). To reduce the channel estimation (CE) parameters, the DSC is modelled using a complex-exponential basis expansion model (CX-BEM) with non-uniform BEM frequencies. We optimize the CX-BEM basis functions using CE error minimization as the objective. As a result, the channel modelling error is greatly reduced. Next, we propose a BEM-based per-survivor processing (PSP) technique and combine it with a decision-directed channel estimator to obtain a channel-tracking equalizer at the receiver. The resultant BEM-PSP receiver significantly improves the channel estimation mean square error (MSE) and the bit error rates (BER) performance in fast fading multi-path channel, thanks to the channel tracking capability embedded within its Viterbi equalizer. Finally, we employ cross-frame channel interpolation, and power distribution between the data and pilot symbols to further improve the system performance at high fading rates. Extensive simulation results show that our BEM-PSP receiver outperforms many existing methods and approaches close to an ideal receiver with perfectly known CSI under various fading scenarios.

On maximizing information reliability in wireless powered cooperative networks

Unpredictable nature of fading channels and difficulty in tracking channel state information pose major challenge in wireless energy harvesting communication system design. In this work, we address relay selection problem for wireless powered communication networks, where the relays harvest energy from the source radio frequency signals. A single source–destination pair is considered without a direct link. The connecting relay nodes are equipped with storage batteries of infinite size. We assume that the channel state information (CSI) on the source–relay link is available at the relay nodes. Depending on the availability of the CSI on the relay–destination link at the relay node, we propose two relay selection schemes and evaluate their outage probability. Availability of the CSI at the relay node on the relay–destination link considerably improves the performance due to additional flexibility in the relay selection mechanism. Due to absence of CSI throughout the network at the time of transmission path selection, the analysis of the problem is not tractable. Therefore, we relax our assumptions on availability of CSI and closed-form expressions of the outage probability as a function of the amount of the available harvested energy are derived for both CSI availability cases. Finally, we numerically quantify the performance for the proposed schemes and compare the outage probability for fixed and equal number of wireless powered forwarding relays.

Deep RNN-Based Channel Tracking for Wireless Energy Transfer System

In this article, we study channel tracking for a wireless energy transfer (WET) system. This problem is practically very important, but challenging. Regarding time-varying channels as a sequence to be predicted, we exploit the deep learning technique for channel tracking. Particularly, by constructing a recurrent neural network (RNN) architecture based on long short-term memory (LSTM) and feedforward neural network (FNN), we develop a novel channel tracking scheme for the WET system. This scheme sequentially estimates the channel state information (CSI) at the ET based on the previous CSI estimates and the harvested energy feedback information from the ER. Numerical results demonstrate the superior performance and effectiveness of the proposed scheme.

LEO–BDS–GPS integrated precise orbit modeling using FengYun-3D, FengYun-3C onboard and ground observations

The new FengYun-3 (FY-3) meteorological satellite, FengYun-3D (FY-3D), carries an enhanced version of the GNSS Occultation Sounder (GNOS) instrument with increased BDS and GPS tracking channels. These high-quality onboard BDS observations together with FengYun-3C (FY-3C) data can serve as an effective supplement to overcome the weakness in BDS tracking geometry. To assess the scope of the low earth orbit (LEO)-induced improvements on the BDS satellite orbits, we processed the ground network and two FY-3 satellites (FY-3C and FY-3D) in a common least-squares adjustment. Three integrated precise orbit determination (POD) schemes with the individual LEO and a combination of two LEOs are designed to investigate the contribution of the new FY-3D satellite. The performance of FY-3D POD is discussed first. Due to the increase in observation redundancy, the FY-3D orbit presents smaller overlap differences than FY-3C. The corresponding precision improvement can reach 72% for the BDS-only POD, 13% for the GPS-only POD and 25% for the GPS and BDS POD. The overlap result of integrated POD indicates that FY-3D contributes a stronger enhancement to GPS and BDS orbits than FY-3C because of its noticeable increase in onboard observations. The most pronounced benefit can be observed in BDS GEO orbits, which is improved by 44% for the regional solution and 41% for the global solution compared to the FY-3C solution. As expected, a further reduction in the overlap differences can be noted when adding FY-3C to the FY-3D global solution. Compared with the FY-3D solution, the orbit precision of BDS GEO, IGSO and MEO for the 2-LEO solution is slightly improved by 3%, 3%, and 1%, respectively, which is mainly limited by the few observations contributed by FY-3C.

Frame splitting and data-aided decision direct channel estimation for OFDM in fast varying fading channels

This article proposes a novel wireless channel estimation method suited for fast fading environment. Generally, forward error correction (FEC) requires long block code length to raise error correction capability while it deteriorates the channel tracking accuracy in pilot-aided estimation. To meet these trade-off demands, we propose a frame splitting and decision directed channel estimation (DDCE). It splits information data in half and estimate channel state information (CSI) of the former part. That of latter is simply extrapolated using the former CSI. Computer simulation result shows that the proposed method can improve bit error rate performance even at fast fading environment.

Fast Accurate Beam and Channel Tracking for Two-Dimensional Phased Antenna Arrays

The sparsity and the severe attenuation of millimeter-wave (mmWave) channel imply that highly directional communication is needed. The narrow beam produced by large array requires accurate alignment, which is difficult to achieve when serving fast-moving users. In this paper, we focus on accurate two-dimensional (2D) beam and channel tracking problem aiming at minimizing exploration overhead and tracking error. Using a typical frame structure with periodic exploration and communication, a proven minimum overhead of exploration is provided first. Then tracking algorithms are designed for three types of channels with different dynamic properties. It is proved that the algorithms for quasi-static channels and channels in Dynamic Case I are optimal in approaching the minimum Cramer-Rao lower bound (CRLB). The computational complexity of our algorithms is analyzed showing their efficiency, and simulation results verify their advantages in both tracking error and tracking speed.

A Novel Chip Pulse Employed by Ranging Code Based on Simultaneous Transmitting CPM Modulation and PN Ranging in Inter-Satellite Links of GNSS

The construction of a navigation constellation with inter-satellite links (ISLs) has become one of the important development trends for new-generation global navigation satellite systems (GNSSs), and ISLs currently realize navigation and communication functions through separate low-rate omnidirectional telemetry, tracking, command and high-rate data service channels, respectively. If the above two functions are integrated into one channel, this will result in simplification of the onboard equipment, improvement of the electromagnetic compatibility, power consumption reduction and frequency resources savings, and we speculate that autonomous navigation will be achieved by ISLs with navigation and communication fusion. In this paper, a system capable of simultaneous high-data rate communication transmission and precision ranging is investigated, and a specific scheme is introduced by combining continuous phase modulation (CPM) and a pseudonoise (PN) ranging code denoted as CPM + PN. The chip pulse is one of the key factors to design the ranging code, which not only affects the ranging performance but also influences the properties of the CPM + PN scheme, such as its spectral characteristics, communication reliability, and acquisition time. To consider the above performance indexes, a new chip pulse based on a normally distributed wave is proposed. Theoretical analysis and simulation results show that compared to square-wave and half-sine wave cases, the normally distributed wave attains great advantages in the ranging accuracy and communication reliability, which become more notable with reasonable selection of the energy distribution index. Moreover, the proposed chip pulse achieves a similar acquisition time as the traditional wave. As a result, the normally distributed wave can be used as a better alternative to the ranging chip pulse for the CPM + PN waveform.

High-Precision Trajectory Data Reconstruction for TT&C Systems Using LS B-Spline Approximation

Existing trajectory data reconstruction algorithms for tracking, telemetry, and command (TT&C) channel simulators cannot achieve continuity and noise reduction simultaneously. By combining the B-spline interpolation algorithm with the least-square (LS) approximation algorithm, we propose a novel trajectory data reconstruction algorithm using LS B-spline approximation. In addition, a polyphase structure is proposed to implement the algorithm. Compared with existing algorithms, the proposed algorithm achieves higher reconstruction precision when the known trajectory data include measurement errors; moreover, it preserves continuity. Numerical simulation results verified the validity and effectiveness of our proposed algorithm.

Stationarity Analysis of V2I Radio Channel in a Suburban Environment

Due to rapid changes in the environment, vehicular communication channels no longer satisfy the assumption of wide-sense stationary uncorrelated scattering. The non-stationary fading process can be characterized by assuming local stationarity regions with finite extent in time and frequency. The local scattering function (LSF) and channel correlation function (CCF) provide a framework to characterize the mean power and correlation of the non-stationary channel scatterers, respectively. In this paper, we estimate the LSF and CCF from measurements collected in a vehicle-to-infrastructure radio channel sounding campaign in a suburban environment in Lille, France. Based on the CCF, the stationarity region is evaluated in time as 567 ms and used to capture the non-stationary fading parameters. We obtain the time-varying delay and Doppler power profiles from the LSF, and we analyze the corresponding root-mean-square delay and Doppler spreads. We show that the distribution of these parameters follows a lognormal model. Finally, application relevance in terms of channel capacity and diversity techniques is discussed. Results show that the assumption of ergodic capacity and the performance of various diversity techniques depend on the stationarity and coherence parameters of the channel. The evaluation and statistical modeling of such parameters can provide a way of tracking channel variation, hence, increasing the performance of adaptive schemes.

Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction, and Restoration

To estimate time-varying MIMO channel at base station, traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to extremely high overhead in massive MIMO systems. To tackle this problem, we propose a novel scheme for DL channel tracking in this paper. First, by utilizing virtual channel representation (VCR), we develop a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise expectation maximization (EM) algorithm is adopted for capturing model parameters, including the spatial signatures, time-correlation factors, off-grid bias, channel power, and noise power. By exploiting the UL/DL angle reciprocity, the spatial signatures, time-correlation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL. However, channel power and noise power are closely related with the carrier frequency, which cannot be perfectly inferred from the UL. Instead of discovering these two parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with partial prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.

Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers

This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multihop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labelled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.

Analysis of a federal Kalman filter-based tracking loop for GPS signals

Kalman filter is widely used in modern GPS receivers. A traditional receiver based on Kalman filter (KF) tracks the GPS signals from each satellite separately, and so its tracking performance could be inferior in challenging situations. The vector delay frequency-locked loop (VDFLL) combines all tracking channels into one Kalman filter and so has a better tracking performance. But VDFLL has two primary drawbacks: high computational cost and low robustness. To solve this problem, the federal tracking loop is proposed. The single Kalman filter of the traditional federal loop is separated into a least-squares block and a master filter in consideration of reducing the computational complexity at the cost of a slight performance reduction. In this tracking loop, the GPS signal from each satellite is tracked independently in the subfilters. The master filter works as a navigation processor, which estimates the acceleration of the receiver and feeds it back to the subfilters. The thermal jitter and dynamic stress error of the federal tracking loop are analyzed theoretically and demonstrated with simulation data. The dynamic performance, robustness and computational complexity of the traditional KF loop, the proposed federal tracking loop and the VDFLL are compared. Analysis and simulation results show that the dynamic tracking performance of the federal loop is much better than that of the KF loop and is almost close to that of the VDFLL. Also, the federal loop has advantages over the VDFLL in terms of robustness and the computational complexity. Therefore, the federal tracking loop can be implemented as a new method in the practical implementation for high-performance applications in the future.

A GPS Spoofing Generator Using an Open Sourced Vector Tracking-Based Receiver.

Spoofing can seriously threaten the use of the Global Positioning System (GPS) in critical applications such as positioning and navigation of autonomous vehicles. Research into spoofing generation will contribute to assessment of the threat of possible spoofing attacks and help in the development of anti-spoofing methods. However, the recent commercial off-the-shelf (COTS) spoofing generators are expensive and the technology implementation is complicated. To address the above problem and promote the GPS safety-critical applications, a spoofing generator using a vector tracking-based software-defined receiver is proposed in this contribution. The spoofing generator aims to modify the raw signals by cancelling the actual signal component and adding the spoofing signal component. The connections between the spreading code and carrier, and the states of the victim receiver are established through vector tracking. The actual signal can be predicted effectively, and the spoofing signal will be generated with the spoofing trajectory at the same time. The experimental test results show that the spoofing attack signal can effectively mislead the victim receiver to the designed trajectory. Neither the tracking channels nor the positioning observations have abnormal changes during this processing period. The recent anti-spoofing methods cannot detect this internal spoofing easily. The proposed spoofing generator can cover all open-sky satellites with a high quality of concealment. With the superiority of programmability and diversity, it is believed that the proposed method based on an open source software-defined receiver has a great value for anti-spoofing research of different GNSS signals.

Angle Domain Channel Tracking With Large Antenna Array for High Mobility V2I Millimeter Wave Communications

In millimeter wave communication systems, channel estimation consumes many resources, in terms of time, bandwidth, and hardware. However, the good news is that the millimeter wave channels are generally sparse in space and channel states at consecutive time slots are highly correlated in angle domain. Therefore, it is possible to track the channel state with less resources, instead of re-estimating it for every time slot. For millimeter wave communications, the antenna array form is important, and we employ a lens antenna array in this paper, because it has much lower complexity and it allows the base station to use only a few RF (Radio Frequency) chains for beamforming. Based on the lens antenna array and the spatial sparsity of the millimeter wave channels, we propose a data-aided channel tracking scheme for vehicle-to-infrastructure (V2I) communication systems, where high-mobility of mobile users is expected. The basic idea is to employ the channel estimate from the previous time slot for the data detection at the current one. After data detection, the detected data sequence can be used for channel update, and this process iterates. By doing this, the overhead of channel estimation can be reduced to great extent. We evaluate the performance of the proposed algorithm through theoretical analysis and simulations, and they both show promising results.

Tracking FDD Massive MIMO Downlink Channels by Exploiting Delay and Angular Reciprocity

The massive multiple-input multiple-output (MIMO) technology considerably enhances the spectral efficiency of a wireless communication system. However, it suffers from substantial performance degradation caused by the movement of users and the difficulty in acquiring downlink channel state information when combined with frequency division duplexing (FDD). This paper addresses these two fundamental problems by proposing a downlink channel tracking scheme that traces the multiuser downlink time-varying channel of an FDD massive MIMO system. By leveraging the delay and angular reciprocity between the uplink and downlink, a low-complexity module initially traces the frequency-independent parameters in each user's channel in the uplink. With the tracing results in previous instances, the module can efficiently trace the parameters of the existing paths, find paths that disappeared, and detect new paths from sparse uplink sounding reference signals. Then, in the downlink, a low-overhead module estimates the downlink gains for each user from a limited amount of cell-common sparse pilots. The K-means clustering algorithm is utilized to design these pilots and restrict them within a predefined amount. Accurate downlink channel tracking results can be obtained by using the frequently updated frequency-independent parameters and downlink gains, thereby compensating the performance degradation caused by movement. The numerical results validate the effectiveness and robustness of the proposed tracking scheme and suggest that we should frequently apply this scheme to ensure a high multiuser sum spectral efficiency performance.

A Novel Self-Interference Cancellation Method Using an Optimized LMS Algorithm in CCFD Systems for a 5G Communication Network

The Co-frequency Co-time Full Duplex (CCFD) is a key concept in 5G wireless communication networks. The biggest challenge for CCFD wireless communication is the strong self-interference (SI) from near-end transceivers. Aiming at cancelling the SI of near-end transceivers in CCFD systems in the radio frequency (RF) domain, a novel time-varying Least Mean Square (LMS) adaptive filtering algorithm which is based on step-size parameters gradually decrease with time varying called the DTV-LMS algorithm is proposed in this paper. The proposed DTV-LMS algorithm in this paper establishes the non-linear relationship between step factor and the evolved arct-angent function, and using the relationship between the time parameter and error signal correlation value to coordinately control the step factor to be updated. This algorithm maintains a low computational complexity. Simultaneously, the DTV-LMS algorithm can also attain the ideal characteristics, including the interference cancellation ratio (ICR), convergence speed, and channel tracking, so that the SI signal in the RF domain of a full duplex system can be effectively cancelled. The analysis and simulation results show that the ICR in the RF domain of the proposed algorithm is higher than that in the compared algorithms and have a faster convergence speed. At the same time, the channel tracking capability has also been significantly enhanced in CCFD systems.