Symbol Timing Estimation for Physical-Layer Network Coding

Dang, Xiaoyu; Li, Qiang; Yu, Xiangbin

doi:10.1109/lcomm.2015.2412931

Cited by 9 publications

(21 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 and 5, the estimation error is dominated by the estimation error of the integral symbol misalignment; however, in the high-SNR regime (above 0 dB) where the integral symbol misalignment is well estimated, the error of estimating fractional symbol misalignment is the main source of error in RMSE. The performance of our method is comparable to the performance presented in [12] 4 with oversampling rate eight (note that [12] uses MSE as their performance metric, which is the square of RMSE). It is worth mentioning that our method does not have any error floor in the high-SNR regime, whereas the scheme proposed in [12] does.…”

Section: A Symbol Misalignment Estimation Performancesmentioning

confidence: 54%

“…The performance gap between 31-bit ZC sequence and 63-bit ZC sequence (also between 63-bit ZC sequence and 127-bit ZC sequence) is 3 dB, agreeing with the effective SNR after cross-correlation in (10), where the signal power is h A L 2 and the noise variance is Lσ 2 . The performance gap is also observed in the simulation of [12].…”

Section: A Symbol Misalignment Estimation Performancesmentioning

confidence: 68%

“…We compute the root mean square error (RMSE) of the estimated symbol misalignment as the performance metric for the proposed method. Previous schemes in [12] served as our benchmarks for comparison purposes.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Furthermore, its estimated symbol misalignment resolution is limited by its oversampling rate (specifically, resolution = symbol duration/oversampling rate), and this leads to an error floor as SNR increases. A recent paper [12] proposed a method that combines oversampling and DFTbased interpolation to estimate the symbol misalignment. However, [12] requires high oversampling rate (e.g., four times oversampling) to achieve good performance and its method also has an error floor in the high-SNR regime.…”

Section: Introductionmentioning

confidence: 99%

“…A recent paper [12] proposed a method that combines oversampling and DFTbased interpolation to estimate the symbol misalignment. However, [12] requires high oversampling rate (e.g., four times oversampling) to achieve good performance and its method also has an error floor in the high-SNR regime. Moreover, the high-rate oversampling results in high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Symbol Misalignment Estimation in Asynchronous Physical-Layer Network Coding

Yang

Liew

et al. 2017

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Symbol misalignment is inevitable in asynchronous physical-layer network coding (PNC) systems. It is paramount that such symbol misalignment is taken into account in PNC decoding for good performance. Thus, accurate estimation of symbol misalignment is crucial. This paper argues that, when Nyquist pulses (i.e., ISI-free pulses) are adopted, signal samples only need to be collected at baud rate for optimal symbol misalignment estimation. Based on this principle, we propose a highly accurate symbol misalignment estimation method with low complexity. Our method makes use of the constant amplitude zero autocorrelation sequence (Zadoff-Chu sequence). We derive a maximum-likelihood (ML) estimator for symbol misalignment based on the cross-correlation result of the Zadoff-Chu sequence. Unlike previous methods that employ oversampling, our estimation method only requires baud-rate sampling, thus has much lower complexity. Extensive simulations show that our method can accurately estimate both integral and fractional symbol misalignments using sinc and raised cosine pulses. The root mean square error of the estimation is below 10 −2 (in unit of symbol duration) when SNR is above 15 dB, 18 dB, and 21 dB for 127-, 63-, and 31-bit length Zadoff-Chu sequences, respectively. Furthermore, our method, being an ML estimation method, has no error floor in the high-SNR regime, whereas the prior methods exhibit an error floor.

show abstract

Section: A Symbol Misalignment Estimation Performancesmentioning

confidence: 54%

Section: A Symbol Misalignment Estimation Performancesmentioning

confidence: 68%

Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations