An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation

Abstract

In this work we design a receiver that iteratively passes soft information between the channel estimation and data decoding stages. The receiver incorporates sparsity-based parametric channel estimation. State-of-the-art sparsity-based iterative receivers simplify the channel estimation problem by restricting the multipath delays to a grid. Our receiver does not impose such a restriction. As a result it does not suffer from the leakage effect, which destroys sparsity. Communication at near capacity rates in high SNR requires a large modulation order. Due to the close proximity of modulation symbols in such systems, the grid-based approximation is of insufficient accuracy. We show numerically that a state-of-the-art iterative receiver with gridbased sparse channel estimation exhibits a bit-error-rate floor in the high SNR regime. On the contrary, our receiver performs very close to the perfect channel state information bound for all SNR values. We also demonstrate both theoretically and numerically that parametric channel estimation works well in dense channels, i.e., when the number of multipath components is large and each individual component cannot be resolved.

A Novel Noise Suppression Channel Estimation Method Based on Adaptive Weighted Averaging for OFDM Systems

“…This can be expressed as Figure 3, whereby the fluctuation range of the optimal value of γ corresponding to different R and f d are indicated by the vertical bars. Noted that the optimal value of γ simulated in Figure 3 (solid line) is obtained by searching step by step with step length 0.1 according to Equation (22). It can be observed that there is an approximate Symmetry 2019, 11, 997 9 of 20 exponential function relationship between γ and R, and the exponential curve shifts up and down with the increase and decrease of f d .…”

“…Combining channel soft information and Turbo decoding in an iterative way can greatly improve the accuracy of channel estimation, which can be realized by the expectation maximization (EM) algorithm or belief propagation (BP) algorithm [21,22]. In [22], an iterative channel estimation based on a factor graph is proposed; this method iteratively passes soft information between the channel estimation and data decoding stages through the BP algorithm. The main disadvantage of this method is the high computational complexity caused by multiple iterations.…”

“…Since L is unknown, this paper chooses N P ≥ N G which guarantees that the above constraint is respected. At the receiver, the fast Fourier transform (FFT) output of the pilot symbols is expressed as [22]:…”

“…is obtained by searching step by step with step length 0.1 according to Equation (22). It can be observed that there is an approximate exponential function relationship between γ and R , and the exponential curve shifts up and down with the increase and decrease of f d .…”

“…It can be seen that the Equation (23) can be acceptable for R ∈ [0, 25] dB and low to medium Doppler spread conditions according to simulation. Therefore, combining Equations (22) and (23), the average frame number B can be determined as:…”