Adaptive sparse channel estimator for OFDM systems

Multicarrier code-division multiple access (MC-CDMA) combines multicarrier transmission with direct-sequence (DS) spread spectrum techniques. In this study, a novel multiple channel estimator, which consists of several possible channel models and can be self-adjusted to fit different channel uncertain conditions, is proposed for MC-CDMA systems to effectively estimate channel coefficients in the frequency domain over time-varying multipath fading channels. Based on the proposed channel estimation, an enhanced minimum mean-square error (MMSE) equalizer is employed for symbol detection of MC-CDMA systems. Simulation results demonstrate that the proposed multiple channel estimator can track the channel more accurately than the conventional channel estimators. With the consideration of channel estimation error, the enhanced equalizer based on the proposed multiple channel estimator performs much better than those based on the conventional channel estimators in bit error rate (BER).

Orthogonal time frequency and space (OTFS) modulation is a promising technology that satisfies high-Doppler requirements for future mobile systems. OTFS encodes information symbols and pilot symbols into the two-dimensional (2D) delay-Doppler (DD) domain. The received symbols suffer from inter-Doppler interference (IDI) in fading channels with fractional Doppler shifts sampled at noninteger indices in the DD domain. The IDI has been treated as an unavoidable effect because the fractional Doppler shifts cannot be obtained directly from the received pilot symbols. This paper provides a solution to channel estimation for fractional Doppler channels with lower computational complexity than a conventional channel estimation method using a pseudo sequence. The proposed estimation provides new insight into the OTFS input-output relation in the DD domain as a 2D circular convolution with a small approximation. According to the input-output relation, we also provide a low-complexity channel equalization method using the estimated channel information. We demonstrate the error performance of the proposed channel estimation and equalization in a high-Doppler channel by simulations. The simulation results show that the proposed channel estimation method outperforms the conventional channel estimation. The results also show that the proposed equalization method has a similar performance to the minimum mean square error equalizer using matrix inversion.

Multipath channels often exhibit sparse structures in multicarrier underwater acoustic (MC-UWA) communication systems. Conventional linear channel estimators, such as least squares (LS) and minimum mean square error (MMSE) are considered as optimal solutions under the assumption of rich multipath. However, they often result in low spectral efficiency due to neglect of the sparsity in multipath channels. In this paper, we propose a novel sparse channel estimation method with compressive sensing. The proposed estimator has better estimation performance and low complexity when compared with the LS estimator. Simulations verified the proposed method with respect to the mean square error (MSE) and the computational complexity. Key words: Underwater acoustic (UWA), orthogonal frequency division multiplexing (OFDM), sparse multipath, compressive sensing (CS), sparse channel estimator.

The number of transmit and receive antennas in multi-input multi-output (MIMO) systems is increasing rapidly to enhance the throughput and reliability of next-generation wireless systems. Benefits of large size MIMO systems, however, can be realized only when the quality of estimated channels is ensured at the transmitter and receiver side alike. In this paper, we introduce a new decision-directed channel estimation technique dealing with pilot shortage in the MIMO-OFDM systems. The proposed channel estimator uses soft symbol decisions obtained by iterative detection and decoding (IDD) scheme to enhance the quality of channel estimate. Using the soft information from the decoders, the proposed channel estimator selects reliable data tones, subtracts interstream interferences, and performs re-estimation of the channels. We analyze the optimal data tone selection criterion, which accounts for the reliability of symbol decisions and correlation of channels between the data tones and pilot tones. From numerical simulations, we show that the proposed channel estimator achieves considerable improvement in system performance over the conventional channel estimators in realistic MIMO-OFDM scenarios.

Low-resolution ADC MIMO-Constant Envelope Modulation, MIMO-CEM, is one of the alternative candidates to the well-known MIMO-OFDM, suitable for wireless backhaul networks. One of the major problems withstands the real application of MIMO-CEM is the channel estimation. This comes from the low resolution ADC used in MIMO-CEM receiver. Intermediate Frequency (IF) based Adaptive channel estimator was previously proposed for MIMO-CEM. This channel estimator is based upon replicating the received preamble signal to exactly match the effect of high quantization error ADC upon the received MIMO signal. Because MIMO-CEM ADC is IF sampled, the whole IF MIMO-CEM transceiver is replicated to exactly replicate the received preamble signal. Although this channel estimator has an excellent estimation performance, its computational complexity is very high. This high complexity comes from the IF (high frequency) calculations. In this paper, a low complexity MIMO-CEM adaptive channel estimator is introduced. The proposed channel estimator is based upon linearly modeling the quantization error occurred at the low resolution IF sampled ADC in baseband. Hence, a baseband adaptive channel estimator can be designed, which has a much lower complexity than the IF estimator (previously designed). The effectiveness of the proposed estimator is proven under different channel scenarios using Minimum Shift Keying (MSK) and Gaussian MSK (GMSK) modulations.

This work proposes symbol and block level adaptive channel estimation schemes, based on the least mean squares (LMS) and block-LMS (BLMS) approaches, respectively, for multiuser-MIMO (MU-MIMO) systems. The proposed schemes do not require knowledge of the first and second order statistics of the time-varying MU-MIMO channel, while also having a lower computational complexity in comparison to the Kalman filter based channel estimation approaches present in the existing literature. Another important aspect of the proposed MU-MIMO framework is that channel estimation is carried out at the base station (BS), which simplifies the receiver architecture. Analytical expressions are derived for the error covariance matrix at each time instant and the asymptotic mean square error (MSE) of the proposed LMS and BLMS frameworks. Further, a superimposed pilot (SIP) framework for MU-MIMO channel estimation been developed, which transmits data symbols to a group of selected users during the training phase, thus leading to a significant improvement in the sum-rate performance. Simulation results are presented to demonstrate the improved sum-rate and MSE performance of the proposed schemes and also to verify the analytical results.

In the next-generation wireless communication systems, the broadband signal transmission over wireless channel often incurs the frequency-selective channel fading behavior and also results in the channel sparse structure which is dominant by very few large coefficients. To ensure the stable wireless propagation, various linear adaptive channel estimation algorithms, e.g., recursive least square (RLS) and least mean square (LMS), have been developed. However, the traditional algorithms are unable to exploit the channel sparsity. Supporting by the compressive sensing (CS) theory, channel estimation performance can be further improved by taking advantage of the sparsity. In this paper, RLS based fast adaptive sparse channel estimation algorithm is proposed by introducing two sparse constraint functions, L1-norm and L0-norm. To improve the flexibility of the proposed algorithms, this paper introduces a regularization parameter selection method to adaptively exploit the channel sparsity. Finally, Monte Carlo based computer simulations are conducted to validate the effectiveness of the proposed algorithms based robust channel estimation either for sparse or non-sparse channel.

In this paper, we propose a channel estimation method based on a complex valued regression of the neural network for the IEEE 802.11p standard. It consists of the complex weighted summation optimized by feedforward neural network with backpropagation algorithm using initial estimated channel of the pilot and the long preamble. It also exploits the shift matrix in order to mitigate the effect from a systemic problems in IEEE 802.11p standards. The major problems of IEEE 802.11p standard are wide bandwidth of 10 MHz consisting of 64 subcarriers and relatively insufficient four pilot subcarriers at single ODFM symbol, which are unsuitable for a channel of vehicular environment. Despite these problems, the proposed method performs better than the conventional channel estimation methods. The performance of proposed scheme is provided with the comparison between constructed data pilots (CDP), Spectral Temporal Averaging (STA), and proposed scheme. The proposed channel estimation scheme has low mean square error (MSE) and bit error rate (BER) throughout the whole SNR region. It is the result from properly trained weight. At the low SNR region, especially, the performance of proposed scheme is much better than CDP and STA scheme. It is because of the noise suppression effect caused by a weighted summation algorithm.

To support extremely high data rates in 6G wireless networks, reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communications have gained much attention in recent years. By manipulating the phase shifts of reflecting elements, the RIS can proactively adjust the wireless propagation environment of THz systems, thereby enhancing the overall throughput significantly. To realize the full potential of RIS-assisted THz systems, an acquisition of accurate channel information is of great importance. However, since the wavefront of the THz electromagnetic signal is spherical, the conventional channel estimation techniques using the planar wavefront assumption suffer from severe performance degradation in the near-field RIS-assisted THz systems. An aim of this work is to propose an efficient channel estimation technique for near-field RIS-assisted wideband THz systems. Key idea of the proposed polar-domain frequency-dependent RIS-assisted channel estimation (PF-RCE) scheme is to estimate the sparse multipath components (i.e., angles, distances, and path gains) of the near-field THz channel by exploiting the polar-domain sparsity and common support properties. We demonstrate from the numerical evaluations that PF-RCE achieves a significant performance gain over the conventional THz channel estimation schemes in terms of the normalized mean square error (NMSE).

This paper is focusing on the performance of the proposed exponentially weight recursive least square (EW-RLS) and normalized least mean square (NLMS) channel estimator using simulations, in the context of the communication system with MIMO antenna operating in outdoor environments. Different training rates and different Doppler frequencies are used to track time-variations of the channel. Three adaptive algorithms are considered; namely the least squares (LS), RLS algorithm and the least mean square (LMS) algorithm. In channel estimation, RLS is widely used especially for slow time-varying. Even though RLS is little bit complex compare to LMS, but it has faster convergence speed. The performance is evaluated in terms of the mean square error (MSE) of the channel estimate, and the system bit error rate (BER), for different Doppler frequencies (correspond to different mobility speeds). Simulation results have demonstrated that time-domain adaptive channel estimation and tracking in MIMO OFDM systems based on the decision directed (DD) EW-RLS and DD-NLMS is very effective in slowly to moderate time-varying fading channels. This paper provides analysis, evaluation and computer simulations in MATLAB.

In this paper, OFDM data-aided channel estimation based on the decimation of the Channel Impulse Response (CIR) through the selection of the Most Significant Samples (MSS) is addressed. Our aim is to approach the Minimum Mean Square Error (MMSE) channel estimation performance, while avoiding the need for a-priori knowledge of channel statistics (KCS). The optimal set of samples is defined in the instantaneous and average senses. We derive lower bounds on the estimation mean-square error (MSE) performance for any MSS selection strategy. We show how MSS-based channel estimation can approach these MSE lower bounds. We introduce novel MSS strategies oriented towards instantaneous decimation (Instantaneous Energy Selection - IES), and windowed decimation (Average Energy Selection - AES). We also consider decimation via Threshold Crossing Selection (TCS), which we characterize analytically, to derive the optimum threshold in the minimum MSE sense. We also propose a sub-optimal method for threshold setting that does not require KCS. Finally, we provide numerical results in terms of both MSE estimation performance and Bit Error Rate (BER) of a coded OFDM system using the proposed channel estimators, to show that they indeed approach MMSE performance.

This paper proposes a reinforcement learning-aided channel estimator for time-varying multi-input multi-output systems. The basic concept of the proposed channel estimator is the selection of the detected data symbol in the data-aided channel estimation. To achieve the selection successfully, we first formulate an optimization problem to minimize the data-aided channel estimation error. However, in time-varying channels, the optimal solution is difficult to derive because of its computational complexity and the time-varying nature of the channel. To address these difficulties, we consider a sequential selection for the detected symbols and a refinement for the selected symbols. A Markov decision process is formulated for sequential selection, and a reinforcement learning algorithm that efficiently computes the optimal policy is proposed with state element refinement. Simulation results demonstrate that the proposed channel estimator outperforms conventional channel estimators by efficiently capturing the variation of the channels.

Objectives: Novel channel estimation technique of MIMO-OFDM system based on Modified Kalman Filter (MKF).The performance metrics are bit error rate, types of modulation method, size of the antenna, types of the Kalman filter. Methods: The performance methods considered are MKF, Single Kalman Filter (SKF), Double Kalman Filter (DKF).We have evaluated channel effect with respect to QPSK modulation technique and Rayleigh channel. Using MATLAB simulation tool, we have plot the bit error rate versus signal to noise ratio graph for MKF, SKF and DKF method. Results: The proposed channel estimation method improves the bit error rate performance compare to conventional channel estimation method. We have compared the proposed MKF method with the SKF and DKF method. Theoretical analysis and simulation results shows that the proposed channel estimation and tracking method based on MKF offer the excellent improvement in bit error rate compare to conventional channel estimation method. Improvements: We have achieved lowest bit error rate in the proposed MKF method compare to SKF and DKF method. As we increase the number of antenna at transmitter and receiver side we have also achieved the lowest bit error rate in the proposed MKF method.

In OFDM systems, channel estimation is a crucial consideration for improving system performance. The channel fading effects are eliminated by the equalization of received signal, and the equalizer coefficients are determined according to the results of channel estimation. In this paper, the channel estimation of OFDM systems in time domain for fast fading channel is investigated and developed. The time-domain channel estimation scheme is achieved by inserting pseudo-noise (PN) sequences in the prefix of each OFDM symbol, and therefore the channel impulse response can be estimated. These proposed algorithms include both least-squares and maximum-likelihood estimation techniques. The proposed techniques can perform better than the conventional channel estimation with correlators. Some comparative simulations are given to illustrate the superior performance of the proposed channel estimation techniques.

The performance of channel-estimation-based maximum likelihood sequence estimator (MLSE) depends on the accuracy of the channel estimate. Conventional least-squares channel estimators preset a fixed length for the channel. For wireless communications, however, the actual length of the channel is environment-dependent and in general unknown. It is therefore desirable to modify conventional channel estimation algorithms so that the length of the channel estimator adapts to the actual channel. We propose a least-squares algorithm for joint synchronization, channel length estimation, and channel estimation. Simulation results show that the proposed scheme is robust against propagation environment and achieves the best trade-off between performance and complexity.