Blind carrier phase acquisition and tracking for 8-VSB signals

Yuan, Jenq-Tay; Huang, Yongfu

doi:10.1109/tcomm.2010.03.080624

Cited by 7 publications

(8 citation statements)

References 19 publications

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“…During the same period, under the context of carrier phase recovery, the fourth-order statistics appeared as an adaptive solution for phase recovery in [7,[23][24][25]. More recent work (during 2000-2010) can be found in [26][27][28][29][30][31][32] where similar fourth-order statistics are presented for adaptive phase recovery in VSB systems. As far as the analysis is concerned, the link between the steady-state estimates of FP-PRA and decision-directed loop is established in [18].…”

Section: Existing Adaptive Phase Recovery Algorithmmentioning

confidence: 99%

“…It should be noted that we have not made any approximation in (32); it is an exact expression for the evaluation of MSD. There are two random variables (x k and ψ k ) involved in (32), so we require conditional means for the evaluation of moments as given by…”

Section: Performance Analysismentioning

confidence: 99%

“…Owing to variance relation (32), the MSD may be obtained by solving the expression: lim k→∞ ES 2 k = 2 μ Eψ k S k . We start with Eψ k S k = E[ψ k E[S k |ψ k ]] as follows (in the sequel, dropping the subscript k for the sake of brevity):…”

Section: Appendix D Msd Of Sgd-pramentioning

confidence: 99%

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Blind adaptive carrier phase recovery for QAM systems

Abrar

Zerguine

Nandi

2016

Digital Signal Processing

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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.

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Section: Existing Adaptive Phase Recovery Algorithmmentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

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Blind adaptive carrier phase recovery for QAM systems

Abrar

Zerguine

Nandi

2016

Digital Signal Processing

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“…Since the transmitted symbols are independent and identically distributed (i.i.d.) discrete uniform random variables, we can ignore the dependency on all of the previous time states and the sampling density can be rewritten as (16) where N (μ, σ 2 ) denotes the Gaussian distribution with mean μ and variance σ 2 , X m is one of the possible transmitted symbols of the modulation alphabet, X = {X 1 , X 2 , . .…”

Section: B Importance Samplingmentioning

confidence: 99%

“…We consider a communication system with a Binary Phase Shift Keying (BPSK) modulation and an AWGN channel. Note that AWGN channel is considered by many authors in the synchronization context [15], [16] . The random frequency offset is assumed to be uniformly distributed in the range (−0.48, 0.48), i.e., approximately full acquisition range acquisition.…”

Section: F Estimationmentioning

confidence: 99%

Particle filter for joint blind carrier frequency offset estimation and data detection

Nasir

Durrani

Kennedy

2010

2010 4th International Conference on Signal Processing and Communication Systems

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Abstract-This paper proposes a new blind algorithm for joint carrier offset estimation and data detection, which is based on particle filtering and recursively estimates the joint posterior probability density function of the unknown transmitted data and the unknown carrier offset. We develop new guidelines for resampling of the particles to take into account carrier offset estimation ambiguity at the edges of the range, and for fine tuning estimates to achieve fast, accurate convergence. The Mean Square Error (MSE) and Bit Error Rate (BER) performance of the proposed algorithm is studied through computer simulations. The results show that the proposed algorithm achieves fast convergence for the full acquisition range for normalized carrier frequency offsets.

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Particle filter for joint blind carrier frequency offset estimation and data detection

Nasir¹,

Durrani²,

Kennedy³

2010

2010 4th International Conference on Signal Processing and Communication Systems

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This paper proposes a new blind algorithm for joint carrier offset estimation and data detection, which is based on particle filtering and recursively estimates the joint posterior probability density function of the unknown transmitted data and the unknown carrier offset. We develop new guidelines for resampling of the particles to take into account carrier offset estimation ambiguity at the edges of the range, and for fine tuning estimates to achieve fast, accurate convergence. The Mean Square Error (MSE) and Bit Error Rate (BER) performance of the proposed algorithm is studied through computer simulations. The results show that the proposed algorithm achieves fast convergence for the full acquisition range for normalized carrier frequency offsets.

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Blind carrier phase acquisition and tracking for 8-VSB signals

Cited by 7 publications

References 19 publications

Blind adaptive carrier phase recovery for QAM systems

Blind adaptive carrier phase recovery for QAM systems

Particle filter for joint blind carrier frequency offset estimation and data detection

Particle filter for joint blind carrier frequency offset estimation and data detection

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