Adaptive Channel Estimation Scheme Research Articles

An adaptive channel estimation scheme based on redundancy minimization for filtered OFDM networks

Filtered orthogonal frequency division multiplexing (F-OFDM), a technology which is being considered as a promising platform for beyond 4G era is expected to help deliver the new features at millimeter wave in the new 5 th generation of cellular communication. Some of its key features notably better spectral utilization, enhanced throughput and immunity to interference can be enabling for the new cellular standards. These features of filtered OFDM comes with strict requirements of filter design, guard tone managements, and efficient channel state information harness. This paper is intended to propose an intuitive channel estimation scheme which will allow efficient acquisition of channel state information (CSI) through exploiting the redundant steps of the conventional pilot training-based algorithms and by also using an adaptive weight to expedite the minimization of the error between the estimated values and the actual values. Various simulations will follow to demonstrate the superiority of the scheme over traditional pilot-based algorithms and thus prove its utility in the current 5G cellular era.

Ball's-Based Adaptive Channel Estimation Scheme Using RLS Family-Types Algorithms

Ball's-Based Adaptive Channel Estimation Scheme Using RLS Family-Types Algorithms

Supervised learning and frequency domain averaging‐based adaptive channel estimation scheme for filterbank multicarrier with offset quadrature amplitude modulation

Filterbank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is an attractive alternative to the orthogonal frequency division multiplexing (OFDM) modulation technique. In comparison with OFDM, the FBMC-OQAM signal has better spectral confinement and higher spectral efficiency and tolerance to synchronization errors, primarily due to per-subcarrier filtering using a frequency-time localized prototype filter. However, the filtering process introduces intrinsic interference among the symbols and complicates channel estimation (CE). An efficient way to improve the CE in FBMC-OQAM is using a technique known as windowed frequency domain averaging (FDA); however, it requires a priori knowledge of the window length parameter which is set based on the channel's frequency selectivity (FS). As the channel's FS is not fixed and not a priori known, we propose a k-nearest neighbor-based machine learning algorithm to classify the FS and decide on the FDA's window length. A comparative theoretical analysis of the mean-squared error (MSE) is performed to prove the proposed CE scheme's effectiveness, validated through extensive simulations. The adaptive CE scheme is shown to yield a reduction in CE-MSE and improved bit error rates compared with the popular preamble-based CE schemes for FBMC-OQAM, without a priori knowledge of channel's frequency selectivity.

Per-Subcarrier RLS Adaptive Channel Estimation Combined with Channel Equalization for FBMC/OQAM Systems

Because of the intrinsic imaginary interference in filter bank multicarrier employing offset quadrature amplitude modulation (FBMC/OQAM) systems, accurate channel estimation remains an open issue, which will be studied in this letter. By using the real-time reconstructed pseudo-data as pseudo-pilots, the authors propose a per-subcarrier recursive least squares (RLS) adaptive channel estimation scheme combined with channel equalization (denoted by RLSACE-CE). The proposed RLSACE-CE scheme not only improves channel estimation performance but also achieves high transmission efficiency, which is validated by simulations and analyses in terms of the normalized mean square error (NMSE) of estimated channels and the bit error rate (BER).

Compressed Sensing-Based Sparsity Adaptive Doubly Selective Channel Estimation for Massive MIMO Systems

By exploiting the sparsity of the channel in the delay and angle domains, compressed sensing (CS) algorithms can be used for channel estimation of massive multiple-input multiple-output (MIMO) systems to reduce pilot overhead. Due to the Doppler frequency shift, however, the intercarrier interference (ICI) and the rapid change of the channel state result in the poor estimation effect of doubly selective (DS) channel. In this paper, we propose the block sparsity adaptive matching pursuit (B-SAMP) algorithm to solve this problem. Firstly, the complex exponential basis expansion model (CE-BEM) is used to convert numerous channel tap coefficients into BEM parameter vectors and then the sparsity adaptive channel estimation scheme based on compressed sensing is proposed. Specifically, the ICI-free model is obtained by using the proposed equally placed pilot group scheme, and the B-SAMP algorithm is proposed by using the spatio-temporal common sparsity of the channel to complete the estimation of DS channel. Finally, a linear smoothing method is used to reduce the error caused by CE-BEM, thereby further improving the accuracy of the estimation. The simulation results show that the proposed method not only improves the estimation accuracy compared with the existing scheme but also requires fewer pilots.

Adaptive Channel Estimation Scheme Based on LTE Uplink in V2V Environments

Vehicle communication can facilitate efficient coordination among vehicles on the road and enable future vehicular applications such as vehicle safety enhancement, infotainment, or even autonomous driving. In the 3rd Generation Partnership Project (3GPP), many studies focus on long term evolution (LTE)-based vehicle communication. Because vehicle communication is closely related to safety, it requires low latency and improved reliability. However, vehicle speed is high enough to cause severe channel distortion in vehicle-to-vehicle (V2V) environments. We can utilize channel estimation methods to approach a reliable vehicle communication systems. Conventional channel estimation schemes can be categorized as least-squares (LS), decision-directed channel estimation (DDCE), spectral temporal averaging (STA), and smoothing methods. In this study, we propose a smart channel estimation scheme in LTE-based V2V environments. The channel estimation scheme, based on an LTE uplink system, uses a demodulation reference signal (DMRS) as the pilot symbol. Unlike conventional channel estimation schemes, we propose an adaptive smoothing channel estimation scheme (ASCE) using quadratic smoothing (QS) of the pilot symbols, which estimates a channel with greater accuracy and adaptively estimates channels in data symbols. In simulation results, the proposed ASCE scheme shows improved overall performance in terms of the normalized mean square error (NMSE) and bit error rate (BER) relative to conventional schemes.

Performance Analysis of Adaptive Channel Estimation Scheme in V2V Environments

Performance Analysis of Adaptive Channel Estimation Scheme in V2V Environments

ODFM-Based Adaptive Channel Estimation Algorithms for IEEE 802.11ad WLAN

This paper proposes an adaptive channel estimation scheme for OFDM-based IEEE 802.11ad wireless local area network (WLAN). The standard supports two types of information of OFDM packets for estimating the communication channels, which are the channel estimation field (CEF) of preamble and pilot subcarriers. The CEF-based channel estimation provides better BER (bit error rate) performance at slow fading channel state, whereas the pilot-based channel estimation is good at fast fading channel state. Hence, a combined channel estimation method is introduced to improve the performance. The prediction of the channel state to select the proper channel estimation method is required. In this work, an adaptive channel estimation scheme is also proposed to improve the performance of channel estimation (CE). Basing on a channel quality indicator (CQI), the proper channel estimation method corresponding to the channel type is decided.

Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér-Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

Low-complexity WLMMSE channel estimator for MB-OFDM UWB systems

Widely linear (WL) minimum mean square error (MMSE) channel estimation scheme have been proposed for multiband orthogonal frequency division multiplexing ultra-wideband (MB-OFDM UWB) systems dealing with non-circular signals. This WLMMSE channel estimation scheme provides significant performance gain, but it presents a high computational complexity compared with the linear one. In this paper, we derive an adaptive WLMMSE channel estimation scheme that significantly reduces the computational complexity. The complexity reduction is done in two stages. The first stage consists of a real evaluation of the WLMMSE channel estimator and the second stage follows with a reduced-rank filtering based on the singular value decomposition (SVD). Computational complexity evaluation shows that the proposed low-rank real-valued WLMMSE channel estimator has computation cost comparable with the linear MMSE. Additionally, simulations of the bit error rate (BER) performance show comparable performance with the WLMMSE channel estimator especially at high signal-to-noise ratio (SNR).

Adaptive Reduced-Rank Estimation of Nonstationary Time-Variant Channels Using Subspace Selection

In this paper, we focus on adaptive time-variant channel estimation for vehicle-to-vehicle (V2V) communications in intelligent transportation systems (ITS) using the IEEE 802.11p physical layer. The IEEE 802.11p pilot pattern is identical to that in the well-known IEEE 802.11a/g (WiFi) standard, which was initially designed for indoor environments with little or no mobility. However, in a V2V drive-by situation, the channel impulse response rapidly changes due to the high relative velocity between transmitter and receiver, as well as the changes in the scattering environment. Hence, for such V2V channels, advanced decision directed channel estimation methods are needed to reach a frame error rate (FER) smaller than 10-1 . Even more importantly, the channels are nonstationary, which implies that the Doppler power spectral density (DSD) and the power delay profile (PDP) change on a timescale comparable with the frame length, which complicates the estimator design. In this paper, we develop an adaptation method for the channel estimation filter that is suitable for the following: 1) the short frame length in IEEE 802.11p; 2) the given pilot structure; and 3) the requirement of only a single received short frame for filter adaptation. We define a set of hypotheses on the support of the DSD and a second set of hypotheses on the support of the PDP. Each hypothesis is represented by a specific subspace spanned by orthogonal basis vectors. For basis vector calculation, we develop a numerically stable algorithm utilizing generalized discrete prolate spheroidal sequences. The adaptation algorithm chooses a hypothesis from both sets such that a probabilistic bound on the channel estimation error is minimized. We implement the hypothesis test by means of a novel subspace selection algorithm that allows utilizing correlated observations of a time- and frequency-selective (2-D) fading process. We validate the adaptive channel estimation scheme in an IEEE 802.11p compliant link level simulation for a relative velocity range from 0 to 111 m/s ≈ 400 km/h ≈ 249 mi/h. Adaptive filtering enables an up to fourfold reduction in the number of required iterations to reach an FER below 10-1 for an Eb/N0 = 12 dB.

Adaptive low-rank channel estimation for multi-band OFDM ultra-wideband communications

In this paper, an adaptive channel estimation scheme based on the reduced-rank (RR) Wiener filtering (WF) technique is proposed for multi-band (MB) orthogonal frequency division multiplexing (OFDM) ultra-wideband (UWB) communication systems in multipath fading channels. This RR-WF-based algorithm employs an adaptive fuzzy-inference-controlled (FIC) filter rank. Additionally, a comparative investigation into various channel estimation schemes is presented as well for MB-OFDM UWB communication systems. As a consequence, the FIC RR-WF channel estimation algorithm is capable of producing the bit-error-rate (BER) performance similar to that of the full-rank WF channel estimator and superior than those of other interpolation-based channel estimation schemes.

Iterative frequency-domain channel estimation and equalization for single-carrier transmissions without cyclic-prefix

Compared to conventional time-domain equalization, frequency-domain equalization (FDE) presents a computationally efficient alternative for the reception of single carrier (SC) transmissions. In this paper, we consider iterative FDE (IFDE) with explicit frequency-domain channel estimation (FDCE) for non-cyclic-prefixed SC systems. First, an improved IFDE algorithm is presented based on soft iterative interference cancellation. Second, a new adaptive FDCE (AFDCE) algorithm based on per-tone Kalman filtering is proposed to track and predict the frequency-domain channel coefficients. The AFDCE algorithm employs across-tone noise reduction, exploits temporal correlation between successive blocks, and adaptively updates the auto-regressive model coefficients, bypassing the need for prior knowledge of channel statistics. Finally, block-overlapping is used to facilitate the joint operation of IFDE and AFDCE. Simulation results show that, compared to related IFDE and adaptive channel estimation schemes, the proposed schemes offer lower mean-square error (MSE) in channel prediction, lower bit error rate (BER) after decoding, and robustness to non-stationary channels.

Block Iterative/Adaptive Frequency-Domain Channel Estimation for Cyclic-Prefixed Single-Carrier Broadband Wireless Systems

This paper presents a new block iterative/adaptive frequency-domain channel estimation scheme, in which a channel frequency response (CFR) is estimated iteratively by the proposed weighted element-wise block adaptive frequency-domain channel estimation (WEB-CE) scheme using the soft information obtained by a soft-input soft-output (SISO) decoder. In the WEB-CE, an equalizer coefficient is calculated by minimizing a weighted conditional squared-norm of the a posteriori error vector with respect to its correction term. Simulation results verify the superiority of the WEB-CE in a time-varying typical urban (TU) channel.

Joint Estimation of Carrier Frequency Offset and Statistical Parameters of the Multipath Fading Channel

In this paper, we focus on the joint estimation of the carrier frequency offset and statistical parameters of the multipath mobile channel. By modeling the multipath fading channel with a complex bandpass autoregressive (AR) model, we show how the estimates for the frequency offset, Doppler spread, and power profile of the multipath channel can be extracted from the estimated complex AR coefficients obtained via the expectation-maximization algorithm. A main advantage of the proposed joint estimator is that while it has a capability of performing equally well in all scattering environments, it can provide accurate estimates even in high-mobility channel conditions. We also demonstrate how the complexity of the estimator can be significantly reduced, while only slightly trading off performance, by applying the mean field approximation technique. Moreover, we derive a fully adaptive joint synchronization and channel-estimation scheme, as well as a novel Kalman-smoother-based frequency-error detector that can be used in feedback frequency-recovery schemes and is particularly well suited for fast-fading channel conditions. Finally, we revisit the Cramer-Rao lower bound analysis, and show how the Fisher information matrix can be conveniently computed in the presence of a frequency-selective Rayleigh fading channel

A Novel Adaptive Channel Estimation Scheme for DS-CDMA

This paper proposes an adaptive channel estimation scheme, which uses different moving average length and pilot gain for different mobile environments. It is based on MSE method and extensive simulations under various environments for WCDMA physical layer. The scheme applies a computationally efficient and easily implemented pilot filter on WCDMA forward channel. For different mobile channel environments, the optimal combination of moving average length and pilot gain for low SNR is achieved. The simulation results illustrate that the adaptive scheme can achieve much lower BER compared with two other adaptive schemes, especially when the speed of mobile user is high. And the BER performance of the proposed scheme is insensible to the mobile speed.

Sequence Detection and Adaptive Channel Estimation for ISI Channels Under Class-A Impulsive Noise

In this paper, sequence detection and channel estimation for frequency-selective, intersymbol interference (ISI)-producing channels under Class-A impulsive noise are considered. We introduce a novel suboptimum sequence detection (SSD) scheme and show that although SSD employs a simplified metric, it achieves practically the same performance as maximum-likelihood sequence detection (MLSD). For both SSD and MLSD, a lower bound on the achievable performance is derived, which is similar to the classical matched-filter bound for frequency-selective (fading) channels under Gaussian noise. For channel estimation, we adopt a minimum entropy criterion and derive efficient least-mean-entropy and recursive least-entropy algorithms. For both adaptive algorithms, we analyze the steady-state channel-estimation error variance. Theoretical considerations and simulation results show that in Class-A impulsive noise, the proposed sequence detection and adaptive channel-estimation schemes yield significant performance gains over their respective conventional counterparts (designed for Gaussian noise). Although the novel algorithms require knowledge of the Class-A noise-model parameters, their computational complexity is comparable to that of the corresponding conventional algorithms.