Compressed Sensing Channel Estimation for OFDM With Non-Gaussian Multipath Gains

Abstract

This paper analyzes the impact of non-Gaussian multipath component (MPC) amplitude distributions on the performance of Compressed Sensing (CS) channel estimators for OFDM systems. The number of dominant MPCs that any CS algorithm needs to estimate in order to accurately represent the channel is characterized. This number relates to a Compressibility Index (CI) of the channel that depends on the fourth moment of the MPC amplitude distribution. A connection between the Mean Squared Error (MSE) of any CS estimation algorithm and the MPC amplitude distribution fourth moment is revealed that shows a smaller number of MPCs is needed to well-estimate channels when these components have large fourth moment amplitude gains. The analytical results are validated via simulations for channels with lognormal MPCs such as the NYU mmWave channel model. These simulations show that when the MPC amplitude distribution has a high fourth moment, the well known CS algorithm of Orthogonal Matching Pursuit performs almost identically to the Basis Pursuit De-Noising algorithm with a much lower computational cost.

Wireless fading channels performance based on Taylor expansion and compressed sensing: A comparative approach

“…A key element differentiating this work to our paper is the consideration of second moment tending to infinity while the fading cases in our paper always assume finite moments. Additionally, compressibility has also been considered 15 focusing on fourth moment, when the latter is large required multipath components required are in agreement to the number required by CS reconstruction. Finally, in our previous work 16 fading channel coding is investigated employing CS to conduct optimization problem via Lagrange multiplier also solving the inverse distribution identification problem whereas in this paper extended metrics are used to evaluate channel performance via the CS and Taylor approximations.…”

Millimeter-Wave Based Localization Using a Two-Stage Channel Estimation Relying on Few-Bit ADCs

“…Moreover, the beamspace (in angular domain) mmwave channels are in general sparse, as the result that, in addition to the line-of-sight (LOS) component, there are usually only a very few strong non-LOS (NLOS) components [14]. Hence, the compressed sensing techniques can be efficiently applied for estimating mmwave channels, for which significant research effort has been invested [15][16][17][18][19][20][21][22][23][24]. To be more specific, in [15], the beamspace representation of multi-path mmwave channels was formulated, and an overview of the compressed channel sensing approaches was provided.…”

“…In [19], the authors proposed to estimate mmwave channels in the frequency domain by the weighted orthogonal matching pursuit (SW-OMP) algorithm. Furthermore, in [21,23], the orthogonal matching pursuit with binary-search refinement (OMPBR) algorithm was employed to reconstruct mmwave channels. In this contribution, we consider the off-grid (or grid-mismatch) based channel estimation for localization purpose.…”

Compressed Sensing Channel Estimation for OTFS Modulation in Non-Integer Delay-Doppler Domain

“…For example, finer delay resolution than expected according to the Shannon-Nyquist sampling constraint is possible in sparse multipath channel estimation. CS has gained interest in recent years as it has enabled to exploit the very large number of antennas and bandwidth in Massive MIMO and mmWave architectures for 5G [1], [14]. The DD representation of signals is sparse in both delay and Doppler dimensions, and the OTFS modulation is a candidate technology for beyond 5G systems.…”