Subspace algorithm for blind channel identification and synchronization in single-carrier block transmission systems

Tsai, Tzung-Ru; Tseng, Der-Feng

doi:10.1016/j.sigpro.2007.08.001

Cited by 4 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where and are feasibility tolerances for the primal and dual feasibility conditions (20) and (21), respectively. These tolerances are designed to vary with iterations such as…”

Section: Compressive-sensing Based Receivermentioning

confidence: 99%

“…However, rather than discarding it, this proposed impulse noise mitigation receiver additionally exploits the corrupted CP-length signals wedged in between two OFDM blocks to fend off the previously mentioned rank deficiency drawback in compressive sensing. Noteworthily, this mechanism was employed in [21] as well; nevertheless, the subspace algorithm was contrived and proved to be capable of estimating the CIR blindly-though in the context of AWGN only. Following the same system architecture laid out in [21], and capitalizing on this additional CP in a similar fashion as it managed to prevent rank deficiency problem, it is stressed here that one of the primary work conducted in this section (but neglected in previous work such as [21]) is to enable the impulse noise hindrance via convex programming.…”

Section: Compressive-sensing Based Receivermentioning

confidence: 99%

“…Noteworthily, this mechanism was employed in [21] as well; nevertheless, the subspace algorithm was contrived and proved to be capable of estimating the CIR blindly-though in the context of AWGN only. Following the same system architecture laid out in [21], and capitalizing on this additional CP in a similar fashion as it managed to prevent rank deficiency problem, it is stressed here that one of the primary work conducted in this section (but neglected in previous work such as [21]) is to enable the impulse noise hindrance via convex programming. The relative mathematical notations are detailed accordingly in the following.…”

Section: Compressive-sensing Based Receivermentioning

confidence: 99%

See 2 more Smart Citations

Impulse Noise Suppression by Deep Learning-Based Receivers in OFDM System

Tseng

Lin

Tseng

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Due to inevitable corruption on signals across all subcarriers, strong and frequently occurring impulses pose a grim threat to Orthogonal Frequency Division Multiplexing (OFDM) systems, where system performance is likely to be further degraded by multipath fading phenomenon. Recently, a deep neural network (DNN) receiver is shown to be capable of not only implicitly estimating channel state information but also explicitly recovering the transmitted symbols without assuming the signal-to-noise ratio (SNR) level. In principle, the DNN model is trained using data generated from computer simulations–according to WINNER II channel model as fading channels, and the additive white Gaussian noise (AWGN) as background noise. As with its conventional peers which assume the inter- fering source from AWGN only, the aforementioned DNN receiver is prone to substantial performance loss when subject to impulse noise. To address this obstacle, this paper incor- porates the fine-tuning notion into the DNN model–by substituting impulse noise-laced data samples for succeeding training data–with an aim at enhancing representation learning. To attest the efficacy of the proposed DNN receiver, the bit error rate (BER) result derived from a compressive sensing-based receiver, enabled by consensus alternating direction method of multipliers (ADMM), is used as a benchmark for performance comparison. Noteworthily, the induced BER performance is comparable to that of the clipping-featured receiver, the threshold of which however solicits the knowledge of the SNR value, an assumption that is otherwise relaxed by the proposed DNN receiver. Furthermore, through extensive simulations, the deep learning based approach demonstrates promising robustness against impulse noise model mismatch between training and testing counterparts.

show abstract

“…where and are feasibility tolerances for the primal and dual feasibility conditions (20) and (21), respectively. These tolerances are designed to vary with iterations such as…”

Section: Compressive-sensing Based Receivermentioning

confidence: 99%

Section: Compressive-sensing Based Receivermentioning

confidence: 99%

Section: Compressive-sensing Based Receivermentioning

confidence: 99%

See 1 more Smart Citation

Impulse Noise Suppression by Deep Learning-Based Receivers in OFDM System

Tseng

Lin

Tseng

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Indeed, blind identification techniques have been recognized as a bandwidth efficient means for increasing network capacity. Since the celebrated work of Moulines et al (1995), the subspace (SS) based estimation framework has played a critical role in blind channel identification for single-carrier systems (e.g., Giannakis et al, 2001;Tsai and Tseng, 2008) and for OFDM systems (e.g., Muquet et al, 2002a,b). It is also useful in remotely continuous monitoring of medical patients where fast and accurate estimation of large volume of medical images such as ECG is critical (e.g., Hu and Han, 2009).…”

Section: Introductionmentioning

confidence: 99%