Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions

Abstract

Channel estimation scheme for OFDM modulated transmissions usually combines an initial block-pilot-assisted stage with a tracking one based on comb or scattered pilots distributed among user data in the signal frame. The channel reconstruction accuracy in the former stage has a significant impact on tracking efficiency of the channel variations and the overall transmission quality. The paper presents a new block-pilot-assisted channel reconstruction procedure based on the DFT-based approach and the Least Square impulse response estimation. The proposed method takes into account a compressibility feature of the channel impulse response and restores its coefficients in groups of automatically controlled size. The proposition is analytically explained and tested in a OFDM simulation environment. The popular DFT-based methods including compressed sensing oriented one were used as references for comparison purposes. The obtained results show a quality improvement in terms of Bit Error Rate and Mean Square Error measures in low and mid ranges of signal-to-noise ratio without significant computational complexity growth in comparison to the classical DFT-based solutions. Moreover, additional multiplication operations can be eliminated, compared to the competitive, in terms of estimation quality, compressed sensing reconstruction method based on greedy approach.

Equalizer Parameters’ Adjustment Based on an Oversampled Channel Model for OFDM Modulation Systems

“…Suppose the time response of the channel is not known, but its compressible nature is acknowledged. In that case, the OMP algorithm can be employed with a stop criterion based on the balance between the estimated MSE and noise power, as suggested by Dziwoki et al [18]. Alternatively, if the nature of the time response is known, we recommend using the algorithm described later in this paper.…”

“…When these conditions are met, iterative compressive sensing algorithms such as OMP (Orthogonal Matching Pursuit) [14,15] and CoSaMP (Compressive Sampling Matching Pursuit) [16] perform well. However, if the synchronization is not perfect, the observed channel response is the result of convolving the actual channel response (which has a limited number of coefficients) with the baseband filter response (which is theoretically infinite) of the transmission system [17,18].…”

“…The CoSaMP algorithm aims to discover a predetermined number of paths that are not initially known. The basic implementation of OMP also seeks a fixed number of paths, although alternative stopping conditions have been proposed in the literature [18]. These conditions are based on the power of the residue in successive iterations.…”

“…A reliable stopping criterion occurs when the residue power is less than the noise power; however, the receiver needs to estimate the noise level [27]. An alternative approach involves comparing the power of the residue in consecutive iterations and terminating when the value changes minimally [17,18].…”

“…However, the compressive sensing algorithms mentioned above do not yield the expected results. The main challenge lies in accurately estimating the number of relevant channel paths [18].…”