Fast Time-varying Channels Research Articles

Sliced Sensing System: Toward 5G Cognitive Radio Applications Under Fast Time-Varying Channels

Cognitive radio technology is an effective tool to alleviate spectrum scarcity problems for future wireless communication systems. Spectrum sensing is the core technique of cognitive radio, because it directly influences the utilization efficiency of spectrum resources. However, the fifth generation communications will encounter many scenarios with fast time-varying channels, in which traditional sensing systems cannot maintain a satisfactory performance as that in traditional quasi-static channels. Motivated by this circumstance, in this paper, we propose a novel sensing system, i.e., sliced sensing system, to dedicatedly solve the challenge above. The proposed system includes an operational concept, an architecture, six specific core modules, and detailed workflows for all the modules. Numerical results are provided to testify the effectiveness of sliced sensing system. It demonstrates that the proposed system can obviously strengthen the sensing performance under fast time-varying channels. Furthermore, it also reveals that the sensing performance of the proposed system is robust, even with inaccurate channel information.

A New Data Pilot-Aided Channel Estimation Scheme for Fast Time-Varying Channels in IEEE 802.11p Systems

The channel estimation in the IEEE 802.11p vehicle-to-everything (V2X) communication systems is generally a challenging task due to high mobility and insufficient number of pilots. Conventional data pilot-aided channel estimation schemes are difficult to apply in practice due to high complexity and the error propagation problem. In this correspondence paper, we propose a new data pilot-aided channel estimation scheme to overcome both issues. To this end, we develop a state feedback decision algorithm that enables us to extract reliable data pilots within a few received symbols. As a result, we can minimize both the error propagation effect and the computational complexity. We verify the complexity gain of the proposed scheme through the complexity analysis. Finally, we demonstrate the accuracy of the proposed channel estimation scheme in terms of the packet error rate performance.

머신 러닝을 이용한 고속 시변 OFDM 채널 예측의 성능 개선

CE-BEM을 이용하는 저복잡도 고속 시변 OFDM채널 예측은 성능 개선에 있어 시스템 파라미터 값을 결정하는 문제가 매우 복잡한 양상을 띠고 있다. 본 논문에서는 머신 러닝에 의한 CE-BEM 문제점을 개선하는 방안을 제시한다. The determination of system parameter values is very complicated for improving the performance of fast time-varying OFDM channel estimation with low complexity CE-BEM. In this letter, we propose a method to mitigate the problem of the CE-BEM by machine learning.

Markov Decision-Based Pilot Optimization for 5G V2X Vehicular Communications

This paper proposes a Markov decision process (MDP)-based pilot placement optimization approach for the radio access in 5G vehicle to everything communications to support Internet of Vehicles applications. The optimal placement problem of pilot symbols is based on a typical pilot-assisted frequency-division multiplexing transmission and simplified to a finite state-space representation. We propose and formulate a finite MDP so as to determine an appropriate pilot pattern from a set of candidate pilot configurations. Also, an enhanced pilot placement scheme is developed to reduce the complexity for solving the formulated MDP problems. Furthermore, we derive analytical expressions of the mutual information, which to some extent allow us to jointly evaluate the dynamics of the channel state in time and frequency domains. Numerical results generated by Monte Carlo simulations show that the proposed pilot optimization policy is capable of improving the channel estimation in fast time-varying vehicular channels, and the mutual information-based measurement criteria can yield more accurate evaluations in fast time-varying vehicular channels than other conventional schemes.

Performance analysis of channel estimation based on base extended model under fast time-varying channels

Performance analysis of channel estimation based on base extended model under fast time-varying channels

Novel Channel Quality Indicator Prediction Scheme for Adaptive Modulation and Coding in High Mobility Environments

In this paper, a novel channel quality indicator (CQI) prediction scheme for adaptive modulation and coding in high-speed mobility environments is proposed, where the wavenumber spectrum segmentation filters' bank is utilized to improve the estimation accuracy of channel prediction algorithm by enhancing the correlation of wireless channels in each orthogonal subspace. In the proposed method, the channel estimation, channel prediction, and signal-to-noise-rate (SNR) prediction are processed on the orthogonal subspace level, which can significantly decrease the mismatch level of the feedback CQI under the fast time-varying channels. Based on the predicted SNR after subspace signal combination, the effective SNR is obtained and is utilized to calculate the feedback CQI parameter. The autocorrelation function and spatial fading rate are analyzed with different feedback delays, which show that our proposed scheme provides stronger correlation of channels with the order of wavenumber spectrum segmentation filters' bank increasing. The simulation results show that the proposed scheme can achieve better throughput performance even with larger feedback delay in high-speed mobility environments.

Angle-Domain Approach for Parameter Estimation in High-Mobility OFDM With Fully/Partly Calibrated Massive ULA

In this paper, we consider a downlink orthogonal frequency division multiplexing (OFDM) system from a base station to a high-speed train (HST) equipped with fully/partly calibrated massive uniform linear antenna-array (ULA) in wireless environments with abundant scatterers. Multiple Doppler frequency offsets (DFOs) stemming from intensive propagation paths together with transceiver oscillator frequency offset (OFO) result in a fast time-varying frequency-selective channel. We develop an angle domain carrier frequency offset (CFO, general designation for DFO and OFO) estimation approach. A high-resolution beamforming network is designed to separate different DFOs into a set of parallel branches in angle domain such that each branch is mainly affected by a single dominant DFO. Then, a joint estimation algorithm for both maximum DFO and OFO is developed for fully calibrated ULA. Next, its estimation mean square error (MSE) performance is analyzed under inter-subarray mismatches. To mitigate the detrimental effects of inter-subarray mismatches, we introduce a calibration-oriented beamforming parameter (COBP) and develop the corresponding modified joint estimation algorithm for partly calibrated ULA. Moreover, the Cramer-Rao lower bound of CFO estimation is derived. Both theoretical and numerical results are provided to corroborate the proposed method.

ML-Type EM-Based Estimation of Fast Time-Varying Frequency-Selective Channels Over SIMO OFDM Transmissions

This paper investigates the problem of fast time-varying frequency-selective (i.e., multipath) channel estimation over single-input multiple-output orthogonal frequency-division multiplexing (SIMO OFDM)-type transmissions. We do so by tracking the variations of each complex gain coefficient using a polynomial-in-time expansion. To that end, we derive the log-likelihood function (LLF) both in the data-aided (DA) and non-data-aided (NDA) cases. The DA maximum likelihood (ML) estimates over fast SIMO OFDM channels are derived here for the first time in closed-form expressions and hereby shown to be limited to applying over each receive antenna the DA least squares (LS) estimator tailored in [1] to fast SISO OFDM channels. This DA ML is used to initialize periodically, over a relatively large number of data blocks (i.e., with further reduced and relatively close-to-negligible pilot overhead compared to DA ML), a new expectation maximization (EM) ML-type solution we developed here in the NDA case to iteratively maximize the LLF. We also introduce an alternative regularized DA ML (RDM) initialization solution no longer requesting - in contrast to DA ML - more per-carrier pilot frames than the number of paths to further reduce overhead without incurring significant performance losses. Simulation results show that the proposed hybrid ML-EM estimator (i.e., combines all new NDA ML-EM and DA ML or RDM versions) converges within few iterations, thereby providing very accurate estimates of all multipath channel gains. Most importantly, this increased estimation accuracy translates into very significant BER and link-level per-carrier throughput gains over the best representative benchmark solution available so far for the problem at hand, the SISO DA LS technique in [1] with its new generalization here to SIMO systems.

Cluster-Based Fast Time-Varying MIMO Channel Fading Prediction in the High-Speed Scenario

Accurate channel state information (CSI) is important for the coherent detection of multiple-input-multiple-output (MIMO) system. Especially in a high-speed scenario, fast time-varying CSI gotten by the conventional channel estimation schemes tend to be out of date, thus tracking and predicting CSI are more attractive and indispensable. Motivated by those, a time-varying MIMO channel fading prediction framework is proposed in this paper. The principle behind our scheme is that the cluster-based fading channel has the spatial consistency property, which means the small-scale parameters of channel clusters evolve continuously and smoothly in the time and spatial domains. Thus CSI can be tracked and predicted within several or tens of wavelengths. The proposed scheme is composed of an extended Bayesian Estimation Kalman Filter to track the time-varying CSI evolution, and a Cluster Drifting Based Prediction to obtain the small-scale parameters of channel clusters. The performance of the proposed scheme is simulatively verified by a standard clustered-based channel model.

Sliced Spectrum Sensing—A Channel Condition Aware Sensing Technique for Cognitive Radio Networks

Cognitive radio technology, which helps to alleviate spectrum scarcity problem, plays an important role in future wireless communication systems. Spectrum sensing is the key technique of cognitive radio. The upcoming fifth generation (5G) communications need to deal with fast time-varying channels (i.e., channel state is non-static within a sensing window), while existing sensing methods are based mainly on conventional quasi-static channels. Consequently, traditional sensing methods may not work well in 5G communications. This paper aims to propose a sliced sensing technique, which can support 5G cognitive radio applications working in fast time-varying channels. We analyze the impact of non-static channels on the performance of traditional sensing methods and disclose a negative factor, i.e., out-of-phase feature distortion effect. Accordingly, we propose a sliced spectrum sensing scheme and probe its feasibility via several relevant trials. Based on the aforementioned works, a complete version of the sliced sensing technique is designed, which can adapt to channel variation and choose the optimal slicing strategy. Simulation results manifest that the sliced sensing technique outperforms traditional sensing methods in both Rayleigh fading and 3GPP spatial channel models.

A Virtual Pilot-Assisted Channel Estimation Algorithm for MIMO-SCFDE Systems Over Fast Time-Varying Multipath Channels

This paper considers a novel virtual pilot-assisted channel estimation algorithm for multiple input multiple output single carrier frequency domain equalization (MIMO-SCFDE) systems over fast time-varying multipath channels. Given that the fast time-varying channels can no longer support the assumption that the channel frequency-domain response remains invariant for the duration of one SC-FDE symbol, most of the channel estimation methods cannot be directly applied to MIMO-SCFDE systems. We put forward an iterative interfrequency interference cancellation method for the time-varying multipath channels over one SC-FDE symbol. The proposed method utilizes the virtual pilot-assisted channel estimation and partial minimum-mean-square-error equalization to improve the accuracy of the estimation. Simulation results validate the improved performance of the proposed method for MIMO-SCFDE systems over fast time-varying multipath channels.

EM‐based poor slot update method for MIMO IDD receiver in fast time‐varying channels

In this study, the authors propose an expectation-maximisation (EM)-based poor slot update method for multiple-input multiple-output (MIMO) iterative detection and decoding (IDD) receivers to increase their channel tracking capability in fast time-varying channels. The poor slots that trigger error propagation in a frame are selected, the channel state information is updated, and the updated recursive least squares channel tracking algorithm is executed. The accurate channel tracking capability of this new channel updating technique contributes to a 3 dB improvement in the bit error rate performance of an IDD receiver in a fast mobile environment ( > 180 km / h ). The authors also demonstrate that an ∼ 20 − 25 % complexity reduction can be achieved under various conditions.

High mobility channel estimation method based on improved basis expansion model

In this paper, the problem of high mobility channel estimation in the Long-Term Evolution for Railway (LTE-R) communication system is investigated. By using a Basis Expansion Model (BEM), the channel impulse response is modeled as the sum of several basis functions multiplied by coefficients. By estimating the basis function coefficients, the fast time-varying channel can be approximated. In order to reduce the estimation error resulting from the high frequency basis function, the Generalized Complex Exponential BEM (GCE-BEM) is modified to form an Improved GCE-BEM (IGCE-BEM) by adding a correction coefficient to the basis function. Moreover, an Improved Baseline Tilting (IBT) method is proposed to reduce the Gibbs effect. In addition, linear interpolation, Gauss interpolation, and three-order Hermite interpolation are adopted to obtain the channel impulse response at non pilot locations based on the channel estimation results at pilot positions. The simulation results show that the IGCE-BEM outperforms the CE-BEM and GCE-BEM in terms of the Normalized Mean Squared Error (NMSE). The IBT method is better than the BT method in reducing the Gibbs effect. In addition, combined with the IBT, the IGCE-BEM also has low NMSE under high moving speed and high noise power. The performance of the three-order Hermite interpolation method is higher than that of the linear interpolation and Gauss interpolation approaches.

A robust ICI suppression based on an adaptive equalizer for very fast time-varying channels in LTE-R systems

High-speed railway communication implemented based on Long-Term Evolution-Railway (LTE-R) Platform has strict requirements for quality of services to support high data-rate transmission for numerous mobile users. In such environment, there often exists inter-carrier interference. In this paper, we exploit a robust channel estimation method in the time domain for very fast time-varying LTE-railway channels, and then, we propose an effective and adaptive frequency-domain equalizer that can offer a tradeoff between performance and computational complexity. The simulation results show the robustness of our proposed method compared with previous ones. The computational complexity of the proposed method retain at a decent number by exploiting specified properties of channel matrices.

Robust Interference Cancellation of Chirp and CW Signals for Underwater Acoustics Applications

We focus on mitigating strong interferences that can jam underwater acoustics emissions aimed for detection or communications. We consider two types of interferences: narrowband like continuous waveform and wideband like chirp. Both the types are assumed to be strong, such that, without interference cancellation, performance is poor. We offer two interference canceling algorithms, each corresponding to a different interference type. The two algorithms are designed to mitigate strong interference, while maintaining the desired signal intact. These algorithms can be executed sequentially to manage both types of interference simultaneously. Our solution takes advantage of the sparsity of the underwater acoustic channel, as well as the assumed correlation of the interference signals. Numerical simulations, as well as results from a sea experiment, show that our algorithms significantly reduce the effect of strong interferences for the fast time-varying and long-delay spread underwater acoustic channel.

Prefix-Free Frequency Domain Equalization for Underwater Acoustic Single Carrier Transmissions

Prefixes, in forms of cyclic-prefixes or zero-padding, have often been viewed as indispensable in underwater acoustic single-carrier transmissions to avoid inter-block interference (IBI) and to formulate circular convolution. However, the prefix introduces overhead and reduces spectral efficiency. This is especially true in fast time-varying channel conditions. Here, we propose a prefix-free scheme to facilitate frequency-domain equalization (FDE) in an underwater acoustic communication system, based on the time-reversal processing. In the proposed scheme, three main procedures, block partitioning, IBI cancellation, and prefix reconstruction, precede the FDE operation. The block partitioning at the receiver provides flexibility to support channel equalization in different channel fluctuation rates. The utilized prefix reconstruction scheme requires a strong first arrival to minimize the noise enhancement. Our solution is to use the time-reversal processing, because the resultant equivalent impulse response, also known as the $q$ -function, has a stable and compact peak. To further enhance receiver performance, we incorporate two strategies. One is overlapping partitioning. The other is iterative prefix reconstruction. The proposed schemes have been tested using the field measurements obtained from the Gulf of Mexico in August 2016. Communications over four ranges at the carrier frequency of 85 kHz with a symbol rate of 17 kHz have been demonstrated. The overlapping partitioning and iterative prefix reconstruction strategies have been shown to generate improvements in the receiver performance.

Iterative Joint Channel Estimation and Signal Detection for OFDM System in Double Selective Channels

Intercarrier interference caused by fast time-varying multipath fading channels degrades the system performance of high-mobility orthogonal frequency division multiplexing systems. This study consi...

Adaptive Signal Detection for Statistical Signal Transmission in Fast Time-Varying Channels

Statistical signal transmission (SST) is an emerging orthogonal frequency-division multiplexing (OFDM) technology that can carry additional information over conventional OFDM signal streams without extra bandwidth cost. This additional information can be exploited to carry control information, location/speed data, or cooperative schedule signals, etc., to improve the performance of wireless communications. However, existing detection methods used in this technique work-based mainly on time-invariant channels (i.e., channel state is quasi-static within a detection period), and cannot work well in diverse channel variations caused by different user velocities. In this paper, we propose a mobility adaptive detection method for SST. First, we analyze the influence of nonstatic channels on SST detection and identify a negative factor, called cyclic autocorrelation function whitening effect. Correspondingly, we probe the feasibility of antiwhitening concept and carry out relevant experiments for verification purpose. Based on this, a complete version of mobility adaptive detection method is proposed. Numerical results manifest that the proposed method outperforms the existing ones in high velocity scenarios. In addition, the properties of the proposed method are investigated, based on which we show a specific application for SST to reveal the potential for SST to be used in 5G wireless communications.

Channel Condition Aware Detection in Statistical Signal Transmission

Heterogeneous network (HetNet) plays an important role in the upcoming 5G communications. A HetNet in futuristic communications is envisaged to provide full access data services, such that users will always have the best service with their available radio resources. To realize a full access HetNet, statistical signal transmission (SST) can be an important enabling technique. SST exploits statistical features of ordinary signals as a data transmission scheme to carry information data. Due to its inherent unique properties, SST signal is more robust than ordinary signals in conventional quasi-static channels. However, detection of SST signals in fast time-varying channels will face challenges. Motivated by this fact, this paper aims to analyze the impact of fast time-varying channels to SST, and reveals negative effects of burst of deep fading channel-state (BDFC) and feature dissipation with out-of-phase (FDOP). Accordingly, we design an automatic re-transmission scheme and a fragmental feature capturing method to mitigate the aforementioned issues, respectively. Based on these studies, we propose a channel condition aware detection scheme for SST, aiming to enhance SST detection performance in fast time-varying channels. Numerical results show that with the proposed scheme, a maximum gain of more than 10 dB can be obtained under either BDFC or FDOP condition. Furthermore, it is also demonstrated that the proposed scheme is robust with imperfect channel coherence time information, which is attractive for practical applications.

Fast Time-Varying Multipath Channel Estimation and Signal Detection for of OFDM Systems with Interference Compensation

Orthogonal frequency division multiplexing (OFDM) is a widely adopted system for broadband wireless transmission, However, time-varying channels degrade the performance owing to the intercarrier interference (ICI). This study discusses the performance enhancement problems of channel estimation and signal detection in view of fast time-varying multipath fading channel characteristics of high mobility. A two-stage method is presented. In the coarse stage, initial channel estimation is performed using the signals at pilot subcarriers based on least-square (LS) estimate is performed. However, the initial estimate still needs to be improved because of the data-induced ICI. The coarse stage improves estimate accuracy by compensating the interferences. The refining stage, applies interference compensation and q-banded successive interference suppression (qSIS) methods to enhance the detection and re-estimate accuracy. Based on robustness, the detected data are called the “pseudo-pilots”, which together with the pilots are treated as a “visual-training” (VT) to improve channel re-estimation. Simulation results indicate that the proposed method is more robust than the traditional method in fast time-varying multipath fading channels with lower computational complexity. The proposed method is independent of the delay and fading properties of the channel.