Symbol Misalignment Estimation in Asynchronous Physical-Layer Network Coding

Yang, Qing; Liew, Soung Chang; Lu, Lu; Shao, Yulin

doi:10.1109/tvt.2016.2578310

Cited by 18 publications

(16 citation statements)

References 14 publications

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“…With the above motivations, this paper puts forth an ML-optimal estimator that estimates symbol misalignment based on double baud-rate samples of the preamble. Simulation results show that this double baud-rate estimator substantially improves the symbol-misalignment estimation accuracy over the prior baud-rate estimator [8]. The mean-square-error (MSE) performance gains of the double baud-rate estimator over the baud-rate estimator are up to 8 dB under both AWGN and Rayleigh fading channels.…”

Section: Introductionmentioning

confidence: 96%

“…Research efforts on RRC-APNC have focused on two issues critical to its inner workings, namely, accurate symbol-misalignment estimation [6]- [8] and optimal PNC decoding under Inter-Symbol Interference (ISI) [9], [10]. This paper makes advances on the solutions to these two problems, as elaborated below.…”

Section: Introductionmentioning

confidence: 99%

“…In [7], simulations showed that as much as 8times oversampling is needed for accurate estimation. Our earlier work [8] presented a maximum likelihood (ML) estimator based on baud-rate sampling (i.e., no oversampling). This baud-rate estimator was shown to be able to achieve estimation accuracy comparable to that of the 8-times oversampling estimators in [6] and [7].…”

Section: Introductionmentioning

confidence: 99%

“…An issue left open by [8] is that baud-rate sampling may not capture all the symbol misalignment information contained in the received signal when the RRC pulse is used. According to the Nyquist-Shannon sampling theorem, the Nyquist rate is the lower bound on the sampling rate that can guarantee no information loss when converting the continuous-time signal into the discrete-time signal (i.e., mathematically, the discrete-time signal can be converted back to the continuous-time signal exactly) [11].…”

Section: Introductionmentioning

confidence: 99%

“…For the signals shaped by the raised-cosine (RC) pulse or the RRC pulse, the Nyquist rate is between the baud-rate and the double baud-rate (i.e., it is defined by a roll-off factor β within the range of 0 ≤ β ≤ 1). This observation suggests that (i) the baud-rate estimator in [8] may still be suboptimal and further improvement is possible with double baud-rate sampling for RRC-APNC; (ii) oversampling beyond the double-baud rate is not necessary because all signal information has already been captured with double baud-rate sampling.…”

Section: Introductionmentioning

confidence: 99%

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Asynchronous Physical-Layer Network Coding: Symbol Misalignment Estimation and Its Effect on Decoding

Shao

Liew

2017

IEEE Trans. Wireless Commun.

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In practical asynchronous physical-layer network coding (APNC) systems, the symbols from multiple transmitters to a common receiver may be misaligned. The knowledge of the amount of symbol misalignment, hence its estimation, is important to PNC decoding. This paper addresses the problem of symbol-misalignment estimation and the problem of optimal PNC decoding given the misalignment estimate, assuming the APNC system uses the root-raised-cosine pulse to carry signals (RRC-APNC).For symbol-misalignment estimation, we put forth an optimal estimator that is substantially more accurate than prior schemes. Our estimator makes use of double baud-rate samples, and hence is information-lossless since the double baud-rate samples capture all the information embedded in the continuous-time signal shaped by the assumed RRC pulse. Extensive simulations show that the meansquare-error (MSE) gains of our double baud-rate estimator over the baud-rate estimator are up to 8 dB. For PNC decoding, we devise optimal APNC decoders under inaccurate symbol-misalignment estimates. In particular, we prove and present a new whitening transformation to whiten the noise of the double baud-rate samples. Given the whitened samples, we construct a factor graph for a sum-product algorithm (SPA) to optimally decode the network-coded message. We further investigate the decoding performance of various estimation-and-decoding solutions for RRC-APNC. Simulation results indicate that: (i) A large roll-off factor of the RRC pulse is preferable in order to reduce decoder complexity while maintaining good performance. (ii) With a large roll-off factor, double baud-rate decoding is indispensable. Compared with a baud-rate sampling system (both estimation and decoding are based on baud-rate samples), a double baud-rate sampling system (both estimation and decoding are based on double baud-rate samples) yields 4.5 dB gains on symbol error rate (SER) performance in an AWGN channel, and 2 dB gains on packet error rate (PER) performance in a flat Rayleigh fading channel.(iii) Under a large roll-off factor, our double baud-rate estimator yields considerable improvements on decoding performance over the baud-rate estimator assuming the double baud-rate decoder is used in both cases. In an AWGN channel, the SER performance gains are up to 2 dB. In a flat Rayleigh fading channel, the PER performance gains are up to 1 dB. Index TermsPhysical-layer network coding, asynchronous PNC, pulse shaping, symbol misalignment estimation, SPA, noise whitening.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Asynchronous Physical-Layer Network Coding: Symbol Misalignment Estimation and Its Effect on Decoding

Shao

Liew

2017

IEEE Trans. Wireless Commun.

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Channel and delay estimation for asynchronous physical layer network coding

Chen

Yin

2018

AEU - International Journal of Electronics and Communications

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Joint timing‐offset and channel estimation for physical layer network coding in frequency selective environments

Abdallah¹,

Saad²,

Alnajjar³

et al. 2021

IET Communications

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This paper considers the problem of joint timing-offset and channel estimation for physical-layer network coding systems operating in frequency-selective environments. Three different algorithms are investigated for the joint estimation of the channel coefficients and the fractional timing offset. The first algorithm is based on the maximumlikelihood (ML) criterion assuming baud-rate (BR) sampling. The second algorithm also assumes BR sampling and is based on the special properties of Zadoff-Chu training sequences. In the third algorithm, oversampling at double the baud-rate (DBR) is used and the least-squares (LS) estimation criterion applied. While the above algorithms assume that the integer timing offset is known, three generalized-likelihood-ratio tests (GLRTs) are also considered for integer offset error correction that integrate very well with the proposed estimation algorithms. Our simulation studies show that the DBR-LS estimator provides the highest estimation accuracy, significantly outperforming both BR estimators and performing very close to the corresponding Cramer-Rao bound. A gain of 4 dB is observed in symbol-error-rate performance using the DBR-LS algorithm. The DBR-GLRT also provides substantially higher probability of error correction. INTRODUCTIONPhysical-layer network coding (PLNC) [1] is an important communication paradigm that continues to attract the interest of many researchers. The appeal of PLNC lies in exploiting the superposition of electromagnetic waves and the broadcast nature of wireless channels during its multiple access phase and broadcast phase, to achieve superior spectral efficiency. PLNC is capable of achieving 100% improvement in throughput over traditional scheduling that is based on point-to-point transmissions, and 33% improvement over conventional network coding schemes [2]. The PLNC framework provides an attractive solution to meet the demands of various applications envisaged for 5th generation (5G) wireless networks. In particular, PLNC can play an important role in facilitating efficient device-to-device (D2D) communication [3,4], where the numerous inactive users can be exploited as relays to extend the range of D2D communication. Other promising applications of PLNC include multiway relaying [2], cognitive relay networks [5] and visible light communication (VLC) [6].This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Symbol Misalignment Estimation in Asynchronous Physical-Layer Network Coding

Cited by 18 publications

References 14 publications

Asynchronous Physical-Layer Network Coding: Symbol Misalignment Estimation and Its Effect on Decoding

Asynchronous Physical-Layer Network Coding: Symbol Misalignment Estimation and Its Effect on Decoding

Channel and delay estimation for asynchronous physical layer network coding

Joint timing‐offset and channel estimation for physical layer network coding in frequency selective environments

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