Channel and delay estimation for asynchronous physical layer network coding

Hu, Xiaoyu; Chen, Zhe; Yin, Fuliang

doi:10.1016/j.aeue.2017.12.010

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…As in [15] and [16], we focus on estimating the relative delay between the two terminals, assuming optimal sampling time for the first received signal. We adopt the symbol‐spaced tap‐delay‐line model to characterize the frequency selective channels

{scriptT}_{1} \to R

and

{scriptT}_{2} \to R

.…”

Section: System Modelmentioning

confidence: 99%

“…An exhaustive search was then employed to obtain the LS integer offset estimate. A discrete Fourier transform (DFT)‐based channel and delay estimation method for asynchronous PLNC was proposed in [16]. The DFT properties were exploited to design pilot sequences that maintain their orthogonality in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

“…Joint channel and timing-offset estimation for PLNC was studied in [15,16]. In [15], only integer offset estimation was considered, while fractional timing offset was ignored.…”

mentioning

confidence: 99%

“…The aforementioned estimation algorithms in [12,13,15,16] all assume flat-fading channels. When the channels are frequency selective, the joint estimation problem becomes significantly more challenging, with intersymbol interference (ISI) due to both the timing misalignment and the time-dispersive nature of the channel.…”

mentioning

confidence: 99%

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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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{scriptT}_{1} \to R

and

{scriptT}_{2} \to R

.…”

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Joint channel and timing-offset estimation for PLNC was studied in [15,16]. In [15], only integer offset estimation was considered, while fractional timing offset was ignored.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…Consequently, the TWR finds applications in a wide range of applications envisaged for 5th generation new radio (5G NR) wireless networks and beyond, including the streaming 4K video, on-line cloud sharing and machine-to-machine communications [11,12]. For the purpose of obtaining a good performance when using PNC for these applications, various studies have been carried out with a specific focus on TWRC issues, including the design of symbol mapping [13,14], channel estimation [15,16] and phase synchronization or effect of time [17][18][19][20]. For satellite communication applications, a satellite can serve as a relay to enable the simultaneous information exchange between two ground stations.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

Chikha¹,

Almadhor²

2022

Computers, Materials &Amp; Continua

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To promote reliable and secure communications in the cognitive radio network, the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation. In this paper, we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output (MIMO) two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution. Our purpose consists of classifying pairs of users modulations from superimposed signals. To achieve this goal, we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier. We use several efficiency metrics including the true positive (TP) rate, false positive (FP) rate, precision, recall, F-Measure and receiver operating characteristic (ROC) area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification. Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio, including the worst case (i.e., m = 0.5), where the fading distribution follows a one-sided Gaussian distribution. We also carry out a comparative study between our proposal using MultiBoostAB classifier with the decision tree (J48) classifier. Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier. In addition, we study the impact of the symbols number, path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.

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Analyzing Impacts of Spatial Correlation for Multi-user Environment with Robust Concatenation of Advanced FEC Schemes

Agarwal

Mehta²

2019

Wireless Pers Commun

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Channel and delay estimation for asynchronous physical layer network coding

Cited by 8 publications

References 17 publications

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

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

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

Analyzing Impacts of Spatial Correlation for Multi-user Environment with Robust Concatenation of Advanced FEC Schemes

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