Channel Tracking Performance Research Articles

3D On and Off-Grid Dynamic Channel Tracking for Multiple UAVs and Satellite Communications

The space-air-ground integrated network (SAGIN) has drawn increasing attention for its benefits, such as wide coverage, high throughput for 5G and 6G communications. As one of the links, space-air communications between multiple unmanned aerial vehicles (UAVs) and Ka-band orbiting low earth orbit (LEO) satellites face a crucial challenge in tracking the 3D dynamic channel information. This paper exploits a statistical dynamic channel model called the multi-dimensional Markov model (MD-MM), which investigates the more realistic spatial and temporal correlation in the sparse UAVs-satellite channel. Specifically, the spatial and temporal probabilistic relationships of multi-user (MU) hidden support vector, single-user (SU) joint hidden support vector, and SU hidden value vector are investigated. The specific transition probabilities that connect the SU and MU hidden support vector for both azimuth and elevation directions are defined. Moreover, based on the proposed MD-MM, we derive a novel multi-dimensional dynamic turbo approximate message passing (MD-DTAMP) algorithm for tracking the 3D dynamic channel in multiple UAVs systems. Furthermore, we also develop a gradient update scheme to recursively find the azimuth and elevation offset for 3D off-grid estimation. Numerical results verify that the proposed algorithm shows superior 3D channel tracking performance with smaller pilot overhead and comparable complexity.

3D Channel Tracking for UAV-Satellite Communications in Space-Air-Ground Integrated Networks

The space-air-ground integrated network (SAGIN) aims to provide seamless wide-area connections, high throughput and strong resilience for 5G and beyond communications. Acting as a crucial link segment of the SAGIN, unmanned aerial vehicle (UAV)-satellite communication has drawn much attention. However, it is a key challenge to track dynamic channel information due to the low earth orbit (LEO) satellite orbiting and three-dimensional (3D) UAV trajectory. In this paper, we explore the 3D channel tracking for a Ka-band UAV-satellite communication system. We firstly propose a statistical dynamic channel model called 3D two-dimensional Markov model (3D-2D-MM) for the UAV-satellite communication system by exploiting the probabilistic insight relationship of both hidden value vector and joint hidden support vector. Specifically, for the joint hidden support vector, we consider a more realistic 3D support vector in both azimuth and elevation direction. Moreover, the spatial sparsity structure and the time-varying probabilistic relationship between degree patterns named the spatial and temporal correlation, respectively, are studied for each direction. Furthermore, we derive a novel 3D dynamic turbo approximate message passing (3D-DTAMP) algorithm to recursively track the dynamic channel with the 3D-2D-MM priors. Numerical results show that our proposed algorithm achieves superior channel tracking performance to the state-of-the-art algorithms with lower pilot overhead and comparable complexity.

Channel Tracking and Transmission Design in 5G Large-Scale MIMO System

The 5G wireless channel in the high-speed rail scene has the characteristics of fast time-varying and double selection, that is, frequency selectivity and time selectivity. The change in the instantaneous channel parameters is much faster than that in the cellular cell scene, so it is more difficult to obtain and track the instantaneous channel. This paper first researches the time and spatial correlation of the channel in 5G large-scale MIMO system and proposes a channel tracking scheme to conduct wave velocity training. Based on the predicted channel information, this paper proposes a complete downlink transmission scheme, which effectively balances 5G system resource overhead and channel tracking performance.

Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction

This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, numeric variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in $$2\times 1$$2?1 space---time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.

다중사용자 상향링크 환경을 위한 적응형 다중안테나 채널추정 기법

In this letter, an adaptive multi-antenna channel estimation scheme is proposed for uplink multiuser environments such as LTE-A systems to accurately estimate time-varying channels within an affordable complexity. It is confirmed that the proposed channel estimator can achieve accurate channel tracking performance even when various time-varying channel environments and traffic patterns are provided. Ⅰ. 서 론

Model-Based Signal Subspace Channel Tracking for Correlated Underwater Acoustic Communication Channels

Many underwater acoustic channels exhibit correlated (semideterministic) multipath arrivals. Such channels are often time-varying with extensive delay spread and yet have a limited number of degrees of freedom due to cross-path (cross-tap) correlation. Traditional least square algorithms used for channel estimation do not exploit this correlation structure. In this paper, a model-based channel tracking algorithm is proposed for correlated underwater acoustic communication channels. To exploit the cross-tap correlation, the channel impulse response (CIR) is projected into a lower dimensional signal subspace consisting of a set of uncorrelated channel components. Assuming constant bases, the channel variations are then represented via an autoregressive (AR) model of these uncorrelated components. This leads to a dynamic model of reduced dimension which can be effectively processed using a Kalman filter, yielding improved channel tracking performance. By tracking only a small number of degrees of freedom in the signal subspace, significant savings in computations can be achieved compared with the conventional recursive least square (RLS) algorithm. Performance is demonstrated with at-sea data.

Channel interpolation for LTE uplink systems with high mobility using Slepian sequences

Single Carrier Frequency Division Multiple Access (SC-FDMA) has been adopted for 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) uplink communication since it combines the low peak to average power ratio feature of single-carrier transmission systems with the multipath resistance and flexible frequency allocation of orthogonal frequency-division multiple access (OFDMA). Unfortunately, SC-FDMA transmission over time-varying multipath fading channels for very high speed environments the subcarrier orthogonality is destroyed giving rise to intercarrier interference (ICI) due to channel variation within an SC-FDMA symbol. This paper is, therefore, focused on the use of time-domain channel interpolator for LTE uplink systems in large Doppler spread scenarios to improve channel tracking performance. The resulting interpolation algorithm is based on the discrete prolate spheroidal wave functions (DPSWF) which is particularly well suited to represent the rapidly time-varying fading channel due to optimum finite time and frequency support feature. The variations of the channel taps are tracked first by a Kalman filter in time domain during training symbols. Then, a DPSWF based channel interpolator is applied to recover the time variation of channel taps between training symbols within two consecutive slots in a single subframe. The performance of the proposed interpolator is compared with a polynomial interpolator whose order is adjusted adaptively to achieve the best fit under particular user mobilities. Moreover, the analytical mean square error (MSE) performance of DPSWF is derived and compared with the simulations. The results show that the DPSWF represents the time-varying fading channel very effectively and the proposed algorithm has excellent channel estimation performance in high SNR even with a very small number of channel interpolation parameters employed in the algorithm.

Numeric Variable Forgetting Factor RLS Algorithm for Second-Order Volterra Filtering

Nonlinear adaptive filtering techniques for system identification (based on the Volterra model) are widely used for the identification of nonlinearities in many applications. In this correspondence, the improved tracking capability of a numeric variable forgetting factor recursive least squares (NVFF-RLS) algorithm is presented for first-order and second-order time-varying Volterra systems under a nonstationary environment. The nonlinear system tracking problem is converted into a state estimation problem of the time-variant system. The time-varying Volterra kernels are governed by the first-order Gauss–Markov stochastic difference equation, upon which the state-space representation of this system is built. In comparison to the conventional fixed forgetting factor recursive least squares algorithm, the NVFF-RLS algorithm provides better channel estimation as well as channel tracking performance in terms of the minimum mean square error (MMSE) for first-order and second-order Volterra systems. The NVFF-RLS algorithm is adapted to the time-varying signals by using the updating prediction error criterion, which accounts for the nonstationarity of the signal. The demonstrated simulation results manifest that the proposed method has good adaptability in the time-varying environment, and it also reduces the computational complexity.

Space-time block-coded systems using numeric variable forgetting factor least squares channel estimator

In this correspondence, the bit-error-rate (BER) performance evaluation of the space-time block-coded (STBC) communication systems using the numeric-variable-forgetting-factor (NVFF) least-squares (LS) channel estimator is presented. The polynomial channel paradigm is incorporated in LS algorithm in conjunction with NVFF to improve the channel tracking performance under the nonstationary wireless environment. The implementation of NVFF precludes the usage of LMS based VFF updating, and it also reduces the computational complexity. The simulation results are presented to demonstrate that the BER performance of STBC communication system using the linear least squares algorithm based channel estimator with NVFF outperforms the higher-order polynomial model-based and conventional VFF-based approaches, when M-ary QAM signal constellations are used for the wireless transmission. Key words: Space-time block-codes, least squares algorithm, Rayleigh fading, Markov process, variable forgetting factor.

A Minimum-Complexity High-Performance Channel Estimator for MIMO-OFDM Communications

Channel estimation is critical to the performance of a multiple-input multiple-output and orthogonal frequency-division-multiplexing (MIMO-OFDM) communication system. Current MIMO-OFDM channel estimation techniques suffer from either high complexity or poor performance. In fact, this issue has become one of the bottlenecks of a practical MIMO-OFDM system. This paper solves the problem by introducing a new technique that has minimum complexity and yet provides superior channel tracking performance close to a minimum mean square error (MMSE) estimate over a wide range of channel selectivity and noise variance. The performance was evaluated via numerical simulations.

Wiener design of adaptation algorithms with time-invariant gains

A design method is presented that extends least mean squared (LMS) adaptation of time-varying parameters by including general linear time-invariant filters that operate on the instantaneous gradient vector. The aim is to track time-varying parameters of linear regression models in situations where the regressors are stationary or have slowly time-varying properties. The adaptation law is optimized with respect to the steady-state parameter error covariance matrix for time-variations modeled as vector-ARIMA processes. The design method systematically uses prior information about time-varying parameters to provide filtering, prediction, or fixed lag smoothing estimates for arbitrary lags. The method is based on a transformation of the adaptation problem into a Wiener filter design problem. The filter works in open loop for slow parameter variations, whereas a time-varying closed loop has to be considered for fast variations. In the latter case, the filter design is performed iteratively. The general form of the solution at each iteration is obtained by a bilateral Diophantine polynomial matrix equation and a spectral factorization. For white gradient noise, the Diophantine equation has a closed-form solution. Further structural constraints result in very simple design equations. Under certain model assumptions, the Wiener designed adaptation laws reduce to LMS adaptation. Compared with Kalman estimators, the channel tracking performance becomes nearly the same in mobile radio applications, whereas the complexity is, in general, much lower.

Combined channel estimation and data detection using soft statistics for frequency-selective fast-fading digital links

A novel adaptive nonlinear equalizer for fast time-varying multipath channels that combines the channel estimation and data detection tasks is presented. The a posteriori probabilities (APPs) of the states of the intersymbol interference (ISI) channel are recursively computed from the received data by a symbol-by-symbol (SbS) detector and are then employed by a Kalman-type nonlinear channel estimator. Robust channel tracking and good data-detection performance are obtained, with a reasonable receiver complexity.