Taps Of Channel Impulse Response Research Articles

Sparse Channel Estimation in Wideband Systems With Geometric Sequence Decomposition

The sparsity of multipaths in the wideband channel has motivated the use of compressed sensing for channel estimation. In this letter, we propose a different approach to sparse channel estimation. We exploit the fact that $L$ taps of channel impulse response in time domain constitute a non-orthogonal superposition of $L$ geometric sequences in frequency domain. This converts the channel estimation problem into the extraction of the parameters of geometric sequences. Numerical results show that the proposed scheme is superior to existing algorithms in high signal-to-noise ratio (SNR) and large bandwidth conditions.

Adaptive Channel Prediction, Beamforming and Scheduling Design for 5G V2I Network: Analytical and Machine Learning Approaches

The fifth-generation wireless network (5G) is expected to support vehicle to infrastructure (V2I) communication which requires an accurate understanding of its highly mobile and dynamic propagation environment for efficient physical layer design. Towards this end, this paper proposes a joint design of adaptive channel prediction, beamforming and scheduling for 5G V2I communications. The channel prediction algorithm is designed without using pilot signals and assuming the channel impulse response (CIR) model. In this regard, we first propose an adaptive recursive least squares (RLS) CIR prediction approach that uses few of the past estimated CIRs to predict one or more future orthogonal frequency division multiplexing (OFDM) block CIR coefficients. Then, we jointly design beamforming and vehicle equipment (VE) scheduling for each sub-carrier to maximize the uplink channel average sum spectrum efficiency (SE) by employing the predicted CIR. The beamforming problem is formulated as a Rayleigh quotient optimization where its global optimal solution can be obtained. Moreover, the VE scheduling design is formulated as an integer programming problem solved using a greedy search. To enhance the prediction performance, we propose a model-free Q-learning technique to learn a strategy for selecting the best CIR predictor at the current OFDM block. In addition, as the CIR coefficients might contain undesired taps with zero (negligible) average powers, we further examine a dominant CIR tap index identification problem (formulated as convex) from the past observed signals. We carry out extensive simulations to validate analytical expressions and examine the effects of different parameters including the VE speed on the achievable sum SE. Numerical simulations corroborate the superiority of the proposed channel prediction and scheduling algorithms over the existing ones and the relevance of identifying dominant CIR taps.

Time-Varying Sparse Channel Estimation Based on Adaptive Average and MSE Optimal Threshold in STBC MIMO-OFDM Systems

Channel estimation is still a challenge for space time block coding (STBC) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in time-varying environments. To estimate the channel state information (CSI) precisely without increasing complexity in any significant way, this paper utilizes the sparsity and the inherent temporal correlation of the time-varying wireless channel, and proposes a novel channel estimation method applied to STBC MIMO-OFDM systems. The proposed method consists of two schemes: adaptive multi-frame averaging (AMA) and improved mean square error (MSE) optimal threshold (IMOT). First, the temporal correlation of the time-varying channel is modeled by a linear Gauss-Markov (LGM) model, and the AMA scheme is incorporated to refine the initial estimated channel impulse response (CIR) through noise reduction. Based on the LGM model, the optimal average frame number is adaptively determined by minimizing the MSE of the denoised CIR. Then, the sparsity of the wireless channel is utilized to model the CIR as a sparse vector, and the IMOT scheme is performed to further remove the noise effect by discarding most of the noise-only CIR taps. Specifically, the IMOT scheme is achieved by recovering the CIR support across the optimal “tap-to-tap” threshold derived by minimizing the MSE of each CIR tap. Moreover, the prior confidence level of the tap to be active is calculated through multi-frame statistics to further improve the performance of the IMOT scheme. Simulation results verify that the proposed AMA-IMOT channel estimation method can achieve better performance than comparison methods.

Enhanced DFT-Based Channel Estimator for Leakage Effect Mitigation in OFDM Systems

The leakage effect occurs in a DFT-based channel estimator (CE) due to the presence of guard bands, where the energy of the channel impulse response (CIR) is dispersed to other estimated taps. Furthermore, some of the CIR energy leaks outside of the estimation range and fails to be captured in the channel estimation. This letter presents an enhanced DFT-based CE for orthogonal frequency-division multiplexing (OFDM) systems. The proposed method is composed of two techniques ( spectral extrapolation and circular shift ) to mitigate the leakage effect, followed by a denoising post-processing to enhance the estimation accuracy. Through performance analysis, it is demonstrated that the spectral extrapolation eliminates the leakage effect corresponding to the first CIR tap, and the circular shift improves estimation accuracy by incorporating the leaked CIR energy in the channel estimation. The simulation results show that the proposed method has superior performance compared with conventional CEs. In addition, the proposed method is feasible from a computational complexity perspective.

Degrees of Freedom and Achievable Rate of Wide-Band Multi-Cell Multiple Access Channels With No CSIT

This paper considers a $K$-cell multiple access channel with inter-symbol interference. The primary finding of this paper is that, without instantaneous channel state information at the transmitters (CSIT), the sum degrees-of-freedom (DoF) of the considered channel is $\frac{\beta -1}{\beta}K$ with $\beta \geq 2$ when the number of users per cell is sufficiently large, where $\beta$ is the ratio of the maximum channel-impulse-response (CIR) length of desired links to that of interfering links in each cell. Our finding implies that even without instantaneous CSIT, \textit{interference-free DoF per cell} is achievable as $\beta$ approaches infinity with a sufficiently large number of users per cell. This achievability is shown by a blind interference management method that exploits the relativity in delay spreads between desired and interfering links. In this method, all inter-cell-interference signals are aligned to the same direction by using a discrete-Fourier-transform-based precoding with cyclic prefix that only depends on the number of CIR taps. Using this method, we also characterize the achievable sum rate of the considered channel, in a closed-form expression.

A Novel Channel Estimation Algorithm for Underwater Acoustic Systems

To decrease the computational complexity and improve the performance of channel estimation for underwater acoustic (UWA) sparse multipath channels, a sparse least square (SLS) channel estimation algorithm is proposed. The proposed algorithm combines advantages of both generalized akaike information criterion (GAIC) estimation and estimation using effective order of channel impulse response (CIR). Known pilot data is used to estimate effective order of CIR and then the position of taps of CIR is estimated. In order to adapt to the environment with low SNR and reduce the dimension of signal space, adaptive threshold is also used. Simulation results indicate that the proposed method has good performance of channel estimation and low complexity.

Parallel-interference-cancellation-assisted decision-directed channel estimation for OFDM systems using multiple transmit antennas

The number of transmit antennas that can be employed in the context of least-squares (LS) channel estimation contrived for orthogonal frequency division multiplexing (OFDM) systems employing multiple transmit antennas is limited by the ratio of the number of subcarriers and the number of significant channel impulse response (CIR)-related taps. In order to allow for more complex scenarios in terms of the number of transmit antennas and users supported, CIR-related tap prediction-filtering-based parallel interference cancellation (PIC)-assisted decision-directed channel estimation (DDCE) is investigated. New explicit expressions are derived for the estimator's mean-square error (MSE), and a new iterative procedure is devised for the offline optimization of the CIR-related tap predictor coefficients. These new expressions are capable of accounting for the estimator's novel recursive structure. In the context of our performance results, it is demonstrated, for example, that the estimator is capable of supporting L=16 transmit antennas, when assuming K=512 subcarriers and K/sub 0/=64 significant CIR taps, while LS-optimized DDCE would be limited to employing L=8 transmit antennas.