Adaptive Demodulation for Wireless Systems in the Presence of Frequency-Offset Estimation Errors

Rahbari, Hanif; Siyari, Peyman; Krunz, Marwan; Park, Jung‐Min

doi:10.1109/infocom.2018.8485922

Cited by 1 publication

(5 citation statements)

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“…To estimate the CFO, the Rx then measures the phase of d t and divides it by 2πT . Because the preamble duration is less than the coherence time, the channel coefficient does not impact CFO estimation, and hence it is not shown in (1). If the noise is nonnegligible, the phase of d t will not be 2πT ∆ f .…”

Section: Preliminaries -Cfo Estimation Errorsmentioning

confidence: 99%

“…The CFO-aware boundaries in this case are the same as when δ f = 0. In general, the CFO-aware boundary between any two adjacent reference constellation points S (1) and S (2) of equal amplitude is the same as when δ f = 0 because p(x, y|S (1) ) and p(x, y|S (2) ) remain symmetric w.r.t. the perpendicular bisector of the line segment determined by the two points.…”

Section: B Demodulation Regions Under Imperfect Cfo Estimationmentioning

confidence: 99%

“…In [1], we analyzed and evaluated the achieved coding gain when the sets are optimally paired. The results are summarized in Tables I and II.…”

Section: A Optimizing Tcm Codes Wrt Phase Offsetmentioning

confidence: 99%

“…The main idea behind our approach is to continuously adapt the demodulation regions during frame reception based on the probability distribution of the CFO-induced phase offset. Compared to recent neural networks (e.g., [17], [18]) and decisiontree-based methods (e.g., [19] 1 ), our SVM algorithm is faster and incurs lower storage complexity while maintaining the BER performance of the optimal demodulation boundaries.…”

mentioning

confidence: 99%

“…They often assume uniformly distributed random CFO, which leads to Gaussian distributed residual CFO. In contrast, we focus on the estimated CFO 1 The authors of [19] have already shown in their work that their method outperforms a large number of other machine-learning methods.…”

mentioning

confidence: 99%

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Lightweight Machine Learning for Efficient Frequency-Offset-Aware Demodulation

Siyari

Rahbari

Krunz

2019

IEEE J. Select. Areas Commun.

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Carrier frequency offset (CFO) arises from the intrinsic mismatch between the oscillators of a wireless transmitter and the corresponding receiver, as well as their relative motion (i.e., Doppler effect). Despite advances in CFO estimation and tracking techniques, estimation errors are still present. Residual CFO creates a time-varying phase error, which degrades the decoder's performance by increasing the symbol error rate. The impact is particularly visible in dense constellation maps (e.g., high-order QAM modulation), often used in modern wireless systems such as 5G NR, 802.11ax, and mmWave, as well as in physical security techniques, such as modulation obfuscation (MO). In this paper, we first derive the probability distribution function for the residual CFO under Gaussian noise. Using this distribution, we compute the maximum-likelihood demodulation boundaries for OFDM signals in a non-closed form. For modulation schemes with unequal-amplitude reference constellation points (e.g., 16-QAM and higher, APSK, etc.), the "optimal" boundaries have irregular shapes, and more importantly, they depend on the time since the last CFO correction instance, e.g., reception of frame preamble. To approximate the optimal boundaries and provide a practical (real-time) demodulation scheme, we explore machine learning techniques, specifically, support vector machine (SVM). Our SVM approach exhibits better accuracy and lower complexity in the test phase than other state-of-the-art machine-learning approaches. As a case study, we apply our CFO-aware demodulation to enhance the performance of a MO technique. Our analytical results show a gain of up to 3 dB over conventional demodulation schemes, which exceeds 3 dB in complete system simulations. Finally, we implement our scheme on USRPs and experimentally corroborate our analytic and simulation-based findings.

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Section: Preliminaries -Cfo Estimation Errorsmentioning

confidence: 99%

Section: B Demodulation Regions Under Imperfect Cfo Estimationmentioning

confidence: 99%

“…In [1], we analyzed and evaluated the achieved coding gain when the sets are optimally paired. The results are summarized in Tables I and II.…”

Section: A Optimizing Tcm Codes Wrt Phase Offsetmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%