New Lower Bound below the Information Rate of Phase Noise Channel Based on Kalman Carrier Recovery

Barletta, Luca; Magarini, Maurizio; Spalvieri, A.

doi:10.15676/ijeei.2012.4.4.6

Cited by 7 publications

(2 citation statements)

References 17 publications

(39 reference statements)

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“…Concerning the lower bounds of Fig. 4, we see that the lower bound (52) outperforms the lower bound proposed in [23], [28], [29] which relies upon demodulation performed by a linearized Kalman filter.…”

Section: A Numerical Resultsmentioning

confidence: 84%

Upper and Lower Bounds to the Information Rate Transferred Through First-Order Markov Channels With Free-Running Continuous State

Barletta

Magarini

Pecorino

et al. 2014

IEEE Trans. Inform. Theory

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Abstract-Starting from the definition of mutual information, one promptly realizes that the probabilities inferred by Bayesian tracking can be used to compute the Shannon information between the state and the measurement of a dynamic system. In the Gaussian and linear case, the information rate can be evaluated from the probabilities computed by the Kalman filter. When the probability distributions inferred by Bayesian tracking are non tractable, one is forced to resort to approximated inference, which gives only an approximation to the wanted probabilities. We propose upper and lower bounds to the information rate between the hidden state and the measurement based on approximated inference. Application of these bounds to multiplicative communication channels is discussed, and experimental results for the discrete-time phase noise channel and for the GaussMarkov fading channel are presented.

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Section: A Numerical Resultsmentioning

confidence: 84%

Upper and Lower Bounds to the Information Rate Transferred Through First-Order Markov Channels With Free-Running Continuous State

Barletta

Magarini

Pecorino

et al. 2014

IEEE Trans. Inform. Theory

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“…Model (33) is proposed in [17] as an approximation to the phase noise spectrum of real-world microwave local oscillators and it has been used with α 1 = 0.9999, β 1 = 0.9937, β 2 = 0.7286 to get the simulation results that are hereafter presented. The lower bound is computed by adopting as a Bayesian tracking method the linearized predictive Kalman filter, as in [18] and [19], while for the upper bound we use both the Kalman filter and the particle filter. Figure 1 reports the results for 4-ary quadrature-amplitude modulation (QAM) while Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

Tight upper and lower bounds to the information rate of the phase noise channel

Barletta

Magarini

Spalvieri

2013

2013 IEEE International Symposium on Information Theory

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Abstract-Numerical upper and lower bounds to the information rate transferred through the additive white Gaussian noise channel affected by discrete-time multiplicative autoregressive moving-average (ARMA) phase noise are proposed in the paper. The state space of the ARMA model being multidimensional, the problem cannot be approached by the conventional trellisbased methods that assume a first-order model for phase noise and quantization of the phase space, because the number of state of the trellis would be enormous. The proposed lower and upper bounds are based on particle filtering and Kalman filtering. Simulation results show that the upper and lower bounds are so close to each other that we can claim of having numerically computed the actual information rate of the multiplicative ARMA phase noise channel, at least in the cases studied in the paper. Moreover, the lower bound, which is virtually capacity-achieving, is obtained by demodulation of the incoming signal based on a Kalman filter aided by past data. Thus we can claim of having found the virtually optimal demodulator for the multiplicative phase noise channel, at least for the cases considered in the paper.

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FPGA implementation and performance of joint crest factor reduction and adaptive predistortion

Spalvieri

Pecorino

2017

2017 IEEE International Conference on Communication, Networks and Satellite (Comnetsat)

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New Lower Bound below the Information Rate of Phase Noise Channel Based on Kalman Carrier Recovery

Cited by 7 publications

References 17 publications

Upper and Lower Bounds to the Information Rate Transferred Through First-Order Markov Channels With Free-Running Continuous State

Upper and Lower Bounds to the Information Rate Transferred Through First-Order Markov Channels With Free-Running Continuous State

Tight upper and lower bounds to the information rate of the phase noise channel

FPGA implementation and performance of joint crest factor reduction and adaptive predistortion

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