Universal Carrier Recovery Loop for QASK and PSK Signal Sets

Leclert, A.; Vandamme, Peter

doi:10.1109/tcom.1983.1095732

Cited by 49 publications

(16 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Defining θ = φ +ψ , where ψ is the estimation error (or residual phase offset), we obtain z k = y k exp(−iφ k ) = x k exp(iψ k ) + e k , (4) where e k has similar statistical properties as those of additive noise w k ; refer to (1). Let S(ψ) be the sensitivity function (also called phase detector characteristics [4] or loop S-curve [5]), defined as 3…”

Section: Problem Formulationmentioning

confidence: 99%

“…Let S(ψ) be the sensitivity function (also called phase detector characteristics [4] or loop S-curve [5]), defined as 3…”

Section: Problem Formulationmentioning

confidence: 99%

“…A corollary of Property 3 is that S(ψ) should be unimodal in −π /4 < ψ ≤ π/4, which implies that it is only possible to recover θ in the range |θ| ≤ π/4. In the light of these properties, an ideal sensitivity function S(ψ) or the corresponding ideal cost J (ψ) should exhibit following properties: 4. J (ψ) should have the following stationary points and no saddle points:…”

Section: Problem Formulationmentioning

confidence: 99%

“…During 80's, Leclert and Vandamme [4], and Moridi et al [13][14][15] came up with a plethora of decision-directed sensitivity functions for adaptive phase recovery, some of those…”

Section: Existing Adaptive Phase Recovery Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Blind adaptive carrier phase recovery for QAM systems

Abrar

Zerguine

Nandi

2016

Digital Signal Processing

View full text Add to dashboard Cite

This paper provides a study of adaptive phase recovery in quadrature amplitude modulation (QAM) based communication systems. Here, we modify the traditional fourth-power phase recovery algorithm (FP-PRA) to propose three improved algorithms, and analyze their performances in the presence of additive white Gaussian noise, phase noise, frequency-offset, and inter-symbol interference. We demonstrate that it is possible to obtain the optimal values of step-sizes (or loop-gains) in closed-form in the presence of phase noise and/or frequency-offset. In particular, we discuss two methods to improve FP-PRA. The first method involves utilizing the idea of partitioning the QAM constellation into QPSK-like and not-QPSK-like annular regions. The phase synchronizer is allowed to update only when a derotated QAM symbol lies in QPSK-like region; otherwise, the update process is stopped. The second method exploits an evolving idea of QAM-to-QPSK transformation, and uses transformed symbols to estimate phase mismatch. We provide a new interpretation of this transformation method and relate it to the quadrant-wise centroid of the rotated constellation. Furthermore, we discuss the feasibility of this method for both square and cross-QAM, and, identify and verify numerically the existence of false-locks in the case of cross-QAM. To the best of our knowledge, the ideas of constellation partitioning and constellation transformation have not appeared earlier in the context of adaptive phase recovery. We discuss adaptive blind estimation of optimal step-sizes in the presence of phase noise and frequency-offset. Finally, we discuss the modification of the proposed stochastic gradient methods to transform them into batch processing algorithms so as to make them more suitable for higher data rate systems. Numerical experiments indicate that the proposed algorithms can outperform the traditional FP-PRA algorithm for a number of practical QAM sizes under different mismatch conditions, and that our analytical findings are in close agreement with the simulation results.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

“…Let S(ψ) be the sensitivity function (also called phase detector characteristics [4] or loop S-curve [5]), defined as 3…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

“…During 80's, Leclert and Vandamme [4], and Moridi et al [13][14][15] came up with a plethora of decision-directed sensitivity functions for adaptive phase recovery, some of those…”

Section: Existing Adaptive Phase Recovery Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Blind adaptive carrier phase recovery for QAM systems

Abrar

Zerguine

Nandi

2016

Digital Signal Processing

View full text Add to dashboard Cite

show abstract

“…This information is essential for assessing the acquisition performance of the carrier recovery loop. The S-curve S(ϕ) is defined as the average of the phase detector output, conditioned on a fixed phase error [16] S(ϕ) = E d k ,n k e(k)|ϕ , (5) where E d k ,n k [·] denotes the statistical average over data and noise. S(ϕ) is periodic of period π/2 for QAM constellations and π/M for M-PSK constellations, and ϕ = 0 is the only stable solution of S(ϕ) = 0 over [0, π/2] ([0, π/M] for M-PSK).…”

Section: Phase Detector S-curve and Variancementioning

confidence: 99%

A Carrier Recovery Loop for Cognitive Radio Applications

Metref

Guennec

Palicot

2011

Circuits Syst Signal Process

View full text Add to dashboard Cite

From the issue entitled "Special Issue on Embedded Signal Processing Circuits and Systems for Cognitive Radio-Based Wireless Communication Devices"International audienceThe increasing demands for wireless communication especially in consumer electronics applications, result in substantially and continuously increasing reliability and Quality of Service (QoS) requirements. This trend creates a need to introduce intelligent functionalities in the wireless devices, which leads to the concept of Cognitive Radio (CR) technology. In some CR applications such as Dynamic Spectrum Access (DSA), the CR terminal should be able to modify its transmission parameters in order to communicate with an efficient utilization of the spectrum resource. In such a case, the CR terminal would be capable of reconfiguring itself with minimum supplementary information to achieve best performance. In this paper we propose a blind carrier recovery loop with respect to the modulation scheme suited to CR applications. Unlike conventional solutions which use a separate modulation identification device before performing carrier recovery, the introduced loop employs a hierarchical symbol decision to achieve phase lock, and then the S-curve and the variance of a Decision-Directed phase error detector are used to extract an information about the modulation scheme of the incoming signal. After the phase lock with the hierarchical decision the constellation used in the received signal is detected and then the true decisions are used to drive the recovery loop. Results obtained with different digital linear modulations (8-PSK, 16-QAM, 16-APSK, 64-QAM) indicate that the introduced loop is capable of achieving a real-time modulation scheme detection as well as keeping phase lock in case of adaptive modulation scheme transmissions

show abstract

Synchronization, Diversity and Advanced Transmission Techniques

Kalivas

2009

Digital Radio System Design

View full text Add to dashboard Cite

Universal Carrier Recovery Loop for QASK and PSK Signal Sets

Cited by 49 publications

References 9 publications

Blind adaptive carrier phase recovery for QAM systems

Blind adaptive carrier phase recovery for QAM systems

A Carrier Recovery Loop for Cognitive Radio Applications

Synchronization, Diversity and Advanced Transmission Techniques

Contact Info

Product

Resources

About