Data-aided autoregressive sparse channel tracking for OFDM systems

Buyuksar, Ayse Betul; Şenol, Habib; Erkucuk, Serhat; Çırpan, Hakan Ali

doi:10.1109/iswcs.2016.7600941

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By considering transmission over the time-varying channel in downlink orthogonal frequency division multiplexing (OFDM)-massive MIMO system, quasi-block simultaneous orthogonal matching pursuit (QBSO) algorithm is employed in [30] to recover the sparse MIMO channels. For rapidly changing dynamic and frequency selective scenarios, as we have in aeronautical systems and high-speed trains, Space Alternated Generalized Expectation Maximization Maximum a Posteriori (SAGE-MAP) based channel estimator is proposed in [31] for tracking of the Autoregressive (AR) modeled dynamic sparse channels. A Variable Step Size Sign Data Sign Error NLMS (VSS-SDSENLMS)-based estimator is developed in [32] by exploiting the time-varying broadband wireless channel's sparsity for tracking of sparse channels.…”

Section: Introductionmentioning

confidence: 99%

Iterative Hard Thresholding with Combined Variable Step Size & Momentum-Based Estimator for Wireless Communication Systems with Dynamic Sparse Channels

2021

View full text Add to dashboard Cite

The channel of the broadband wireless communications system can be modeled as a dynamic sparse channel. Such a channel is difficult to reconstruct by using linear channel estimators that are normally employed for dense channels’ estimation because of their lack of capacity to use the inherent channel’s sparsity. This paper focuses on reconstructing this type of time-varying sparse channel by extending a recently proposed dynamic channel estimator. Specifically, variable step size’s mechanism and variable momentum parameter are incorporated into traditional Iterative Hard Thresholding-based channel estimator to develop the proposed Iterative Hard Thresholding with Combined Variable Step Size and Momentum (IHT-wCVSSnM)-based estimator. Computer simulations carried out in the context of a wireless communication system operating in a dynamic sparse channel, show that the proposed IHT-wCVSSnM-based estimator performs better than all the other estimators significantly. However, the computational complexity cost of the proposed estimator is slightly higher than the closely performing channel estimator. Nevertheless, the inherent complexity cost of the proposed estimator could be compromised in a situation where the system’s performance is of higher priority when compared with the computational complexity cost.

show abstract

Section: Introductionmentioning

confidence: 99%