The convergence analysis of extended Papoulis-Gerchberg Algorithm on AWGN-smeared signals

Cho, Li; Chen, Cheng-Yu; Lin, Chieh-Ke; Hsu, Chau-Yun

doi:10.1109/icasi.2018.8394363

Cited by 2 publications

(8 citation statements)

References 5 publications

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“…= 1 if the j-th frequency component is a pilot or null subcarrier and I ISR T (j,j) = 0 otherwise. In this case, the ratio of the prior knowledge is K ISR T = N p + N n /N = (6 + 2) /32 = 25%, which implies a successful reconstruction of an incomplete signal vector cannot exceed 25% lost samples according to [33]. On the other hand, though the received data subcarriers herein are taken as unknown part and do not contribute to the reconstruction process in each iteration (denote as hollow dots in Fig.…”

Section: B Frequency-domain Prior Knowledgementioning

confidence: 99%

“…PGA has been taken as a well-known tool for restoration of bandlimited signals [37]; however, we introduce GPGA to handle a general signal [33], [34].…”

Section: A Proposed Extended Papoulis-gerchberg Algorithmmentioning

confidence: 99%

“…, M }, and the knowledge ratio of transformed domain is K T = M T /M . According to [33], the lost sample reconstruction with perfect known samples can be achieved if L D ≤ K T is satisfied. The algorithm starts with v(i=0) = K D , where iteration number i is set to zero as an initial guess.…”

Section: A Proposed Extended Papoulis-gerchberg Algorithmmentioning

confidence: 99%

“…To this end, the proposed ISR scheme takes Papoulis-Gerchberg Algorithm (PGA) [31], [32] as basis while further considering specified subcarrier information, so as to enable an effective reconstruction of signal vector at the receiver. Based on the generalization of PGA (GPGA) [33], [34] and decision-aided reconstruction (DAR) [35], the proposed ISR scheme can simply regularize the subcarrier data in the frequency domain, which leads to a very light-weight signal linearization process that can be implemented via system-built-in functions of a typical OFDM receiver, thus yielding high compatibility with the current industry specification/standard. The simulation results demonstrate that the proposed ISR scheme can solidly improve the BER performance of the distorted signal caused by the presence of PA nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mitigation of PA Nonlinearity for IEEE 802.11ah Power-Efficient Uplink via Iterative Subcarrier Regularization

Cho

Yu²,

Hsu³

et al. 2021

IEEE Access

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An orthogonal frequency division multiplexing (OFDM) transmitter for 802.11ah uplink may consume unnecessarily high power due to the malicious effect of a large peak-to-average power ratio (PAPR). This is particularly a problem for client devices (CDs) under the low-power wide-area (LPWA) technology in an Internet of thing (IoT) system, where high PAPR signals may drive the power amplifier (PA) to operate with large input back-off (IBO). This article focuses on receiver-side signal compensation (SC) techniques and introduces a novel scheme called iterative subcarrier regularization (ISR), which is based on the generalization of Papoulis-Gerchberg algorithm (GPGA). We claim that the proposed scheme is completely compatible with 802.11ah as it only exploits the prior information available in the standard operations and popular system-build-in functions in the iterative signal reconstruction process. Extensive numerical evaluations demonstrate that the proposed scheme can improve PA efficiency by 4-9 dB for uplink signaling.INDEX TERMS Orthogonal frequency division multiplexing (OFDM), nonlinear noise, Papoulis-Gerchberg algorithm, peak-to-average power ratio (PAPR), 802.11ah.

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Section: B Frequency-domain Prior Knowledgementioning

confidence: 99%

“…PGA has been taken as a well-known tool for restoration of bandlimited signals [37]; however, we introduce GPGA to handle a general signal [33], [34].…”

Section: A Proposed Extended Papoulis-gerchberg Algorithmmentioning

confidence: 99%

Section: A Proposed Extended Papoulis-gerchberg Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mitigation of PA Nonlinearity for IEEE 802.11ah Power-Efficient Uplink via Iterative Subcarrier Regularization

Cho

Yu²,

Hsu³

et al. 2021

IEEE Access

Self Cite

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“…Indeed, the proposed method and the compared algorithms are all derived from PGIR which in turn usually runs under a noise-free condition. However, some recent studies on PGIR and its generalizations show how the use of some error tolerant techniques leads to reasonable results in the presence of noise too (see [30]).…”

Section: Robustness Against Noisementioning

confidence: 99%

Iterative Reconstruction of Signals on Graph

Brugnoli

Toscano

Vetro

2020

IEEE Signal Process. Lett.

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We propose an iterative algorithm to interpolate graph signals from only a partial set of samples. Our method is derived from the well known Papoulis-Gerchberg algorithm by considering the optimal value of a constant involved in the iteration step. Compared with existing graph signal reconstruction algorithms, the proposed method achieves similar or better performance both in terms of convergence rate and computational efficiency.

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The convergence analysis of extended Papoulis-Gerchberg Algorithm on AWGN-smeared signals

Cited by 2 publications

References 5 publications

Mitigation of PA Nonlinearity for IEEE 802.11ah Power-Efficient Uplink via Iterative Subcarrier Regularization

Mitigation of PA Nonlinearity for IEEE 802.11ah Power-Efficient Uplink via Iterative Subcarrier Regularization

Iterative Reconstruction of Signals on Graph

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