- The paper introduces compressive sensing techniques that leverage delay-Doppler sparsity to significantly reduce the number of required pilot symbols.
- It meticulously analyzes leakage effects from finite bandwidth and block length, proposing an iterative basis expansion to enhance sparsity.
- Simulation results confirm reduced mean square errors and improved bit error rates while maintaining computational feasibility.
Compressive Estimation of Doubly Selective Channels in Multicarrier Systems
The paper "Compressive Estimation of Doubly Selective Channels in Multicarrier Systems: Leakage Effects and Sparsity-Enhancing Processing" addresses the challenge of estimating doubly selective channels using compressed sensing (CS) techniques. This work focuses on pulse-shaping multicarrier (MC) systems, a category that encompasses Orthogonal Frequency-Division Multiplexing (OFDM) systems as a special case. Given the increasing demand for spectral efficiency, the authors exploit the inherent sparsity of channels in the delay-Doppler domain to reduce the number of pilot symbols required for channel estimation.
The fundamental contribution of this paper lies in harnessing CS to reconstruct sparse signals efficiently from a limited number of measurements. Traditional pilot-based methods such as least-squares and minimum mean-square error estimators do not capitalize on this sparsity, thereby potentially overlooking opportunities for spectral efficiency improvements. By focusing on the sparsity of doubly selective channels, this paper proposes novel approaches for channel estimation that combat leakage effects, which adversely affect delay-Doppler sparsity.
The problem of leakage effects is meticulously analyzed, showing how finite channel bandwidth and block length can lead to a spread in the delay-Doppler representation, thereby reducing sparsity. The authors propose a computationally efficient basis expansion method designed to enhance sparsity by optimizing the basis functions via an iterative procedure. This method is extended to incorporate prior statistical information on the channel to further fine-tune the basis functions for enhanced performance.
For channels exhibiting significant time-frequency dispersion, the paper introduces an alternative CS-based estimator capable of estimating both diagonal and off-diagonal channel coefficients, addressing intersymbol and intercarrier interference (ISI/ICI). Here, the authors leverage a hybrid basis combining Fourier and prolate spheroidal sequences for enhanced performance in dispersive environments.
Simulation results confirm significant performance improvements through the proposed sparsity-enhancing techniques and ISI/ICI estimation capabilities, with gains evidenced by reduced mean square errors and improved bit error rates. Importantly, these gains do not come at the expense of computational feasibility, given the manageable increased complexity of the proposed methods.
The implications of this research are substantial for future wireless communication systems, particularly those striving for higher spectral efficiencies in mobile environments. Future work might focus on extending these techniques to multicarrier systems with more complex modulation schemes or exploring the impacts of different types of channel models that exhibit varying degrees of sparsity.
In conclusion, this paper contributes valuable insights into the estimation of doubly selective channels through innovative use of compressive sensing, promising advancements in reducing pilot overhead and enhancing spectral efficiency in multicarrier communications.