Bootstrapping Iterative Demodulation and Decoding Without Pilot Symbols

Pecorino, Simone; Mandelli, Silvio; Barletta, Luca; Magarini, Maurizio; Spalvieri, A.

doi:10.1109/jlt.2015.2448109

Cited by 12 publications

(10 citation statements)

References 47 publications

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“…The test scenario that will be reused later considers transmission based on 16-QAM constellation with Gray mapping [9, Sec. 2.5.2]; the proprietary LDPC encoder with the rate R = 7 8 is applied to the block of N b = 4032 bits. The pilot symbols are pseudo-randomly drawn from a 4-QAM constellation; the pilot spacing is set to L = 25 which allows us to place L − 1 = 24 payload symbols between pilots, see (4), so dummy symbols are not needed to fill the frame of transmitted symbols x; we thus have F = 43 pilots and total N = 1051 symbols.…”

Section: E Example: Phase Tracking Reference Resultsmentioning

confidence: 99%

“…2. PER vs. SNR obtained using CBC algorithm with the projection based on the Gaussian approximation (GA) originally proposed in [1] (CBC+GA) or, on the circular moment matching (CMM) explained in Appendix A (CBC), both shown for the scheduling {Idec × 1} with Idec = 10 (filled markers, solid lines) and Idec = 30 (hollow markers, dashed lines); the encoding rate of the LDPC is R = 7 8 . The discretized message passing (DMP) is implemented using N θ = 64 samples, the "AWGN" curve assumes no phase noise, and "All pilots" curve is the performance limits for any joint phase tracking and decoding, see explanation in Sec.…”

Section: E Example: Phase Tracking Reference Resultsmentioning

confidence: 99%

“…Let the scheduling defined by {I loop × I dec } means the following: after each passage of the CBC phase tracking, the decoder executes I dec elementary decoding iterations 7 , then the phase tracking is implemented again and the results are used by the decoder executing I dec elementary decoding iterations. This is repeated I loop times so the total number of decoding iterations is given by I Σ dec = I loop • I dec .…”

Section: Scheduling and Complexitymentioning

confidence: 99%

“…For the most popular constellations such as M -quadrature amplitude modulation (QAM), both conditions hold. 7 Defined as one check-nodes message update and one bit-nodes message update.…”

Section: Scheduling and Complexitymentioning

confidence: 99%

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Parametric Phase Tracking via Expectation Propagation

Szczecinski,

Bouazizi,

Aharony

2020

Preprint

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In this work we propose a simple algorithm for signal detection in a single-carrier transmission corrupted by a strong phase noise. The proposed phase tracking algorithm is formulated within the framework of parametric message passing (MP) which reduces the complexity of the Bayesian inference by using distributions from a predefined family; here, of Tikhonov distributions. This stays in line with previous works mainly inspired by the well-known Colavolpe-Barbieri-Caire (CBC) algorithm which gained popularity due to its simplicity and possibility for decoder-aided operation. In our work we leverage the simplicity of the MP characteristic of the CBC algorithm and combine it with the principles of the expectation propagation (EP). In this way we notably improve the performance of the phase tracking before the decoder's feedback can be even considered. Further, in the spirit of the joint decoding and phase tracking, the EP algorithm can be integrated in the decoding loop; then, not only it outperform the reference scenario of the exact (discretized) message passing -a results that was not yet shown in the literature, but it requires much lower complexity than the state-of-the art algorithms.

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Section: E Example: Phase Tracking Reference Resultsmentioning

confidence: 99%

Section: E Example: Phase Tracking Reference Resultsmentioning

confidence: 99%

Section: Scheduling and Complexitymentioning

confidence: 99%

“…For the most popular constellations such as M -quadrature amplitude modulation (QAM), both conditions hold. 7 Defined as one check-nodes message update and one bit-nodes message update.…”

Section: Scheduling and Complexitymentioning

confidence: 99%

See 2 more Smart Citations

Parametric Phase Tracking via Expectation Propagation

Szczecinski,

Bouazizi,

Aharony

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This is particularly the case for large values of ∆, where the entire range [−π; π) must be covered with very fine precision. Recently, the authors in [24] studied this problem, and proposed a low-complexity solution, which basically reduces the state space and thus the complexity of the algorithm. A comparison in terms of complexity and performance between the TMM and the solution from [24] would be of interest, but out of the scope of this paper.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Low-Complexity Tracking of Laser and Nonlinear Phase Noise in WDM Optical Fiber Systems

Yankov

Fehenberger

Barletta

et al. 2015

J. Lightwave Technol.

Self Cite

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Abstract-In this paper the wavelength division multiplexed (WDM) fiber optic channel is considered. It is shown that for ideal distributed Raman amplification (IDRA), the Wiener process model is suitable for the non-linear phase noise due to cross phase modulation from neighboring channels. Based on this model, a phase noise tracking algorithm is presented. We approximate the distribution of the phase noise at each time instant by a mixture of Tikhonov distributions, and derive a closed form expression for the posterior probabilities of the input symbols. This reduces the complexity dramatically compared to previous trellis based approaches, which require numerical integration. Further, the proposed method performs very well in low-tomoderate signal-to-noise ratio (SNR), where standard decision directed (DD) methods, especially for high order modulation, fail. The proposed algorithm does not rely on averaging, and therefore does not experience high error floors at high SNR in severe phase noise scenarios. The laser linewidth (LLW) tolerance is thereby increased for the entire SNR region compared to previous DD methods. In IDRA WDM links the algorithm is shown to effectively combat the combined effect of both laser phase noise and non-linear phase noise, which cannot be neglected in such scenarios. In a more practical lumped amplification scheme we show near-optimal performance for 16QAM, 64QAM and 256QAM with LLW up to 100kHz, and reasonable performance for LLW of 1MHz for 16QAM and 64QAM, at the moderate received SNR region. The performance in these cases is close to the information rate achieved by the above mentioned trellis processing.

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Joint parameter estimation and decoding in a distributed receiver

Waqas

Nguyen

Lechner

et al. 2022

Satell Commun Network

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Summary This paper presents an algorithm for iterative joint channel parameter (carrier phase, Doppler shift, and Doppler rate) estimation and decoding of transmission over channels affected by Doppler shift and Doppler rate using a distributed receiver. This algorithm is derived by applying the sum‐product algorithm (SPA) to a factor graph representing the joint a posteriori distribution of the information symbols and channel parameters given the channel output. In this paper, we present two methods for dealing with intractable messages of the SPA. In the first approach, we use particle filtering with sequential importance sampling for the estimation of the unknown parameters. We also propose a method for fine‐tuning of particles for improved convergence. In the second approach, we approximate our model with a random walk phase model, followed by a phase tracking algorithm and polynomial regression algorithm to estimate the unknown parameters. We derive the Weighted Bayesian Cramer‐Rao Bounds for joint carrier phase, Doppler shift, and Doppler rate estimation, which take into account the prior distribution of the estimation parameters and are accurate lower bounds for all considered signal‐to‐noise ratio values. Numerical results (of bit error rate and the mean‐square error of parameter estimation) suggest that phase tracking with the random walk model slightly outperforms particle filtering. However, particle filtering has a lower computational cost than the random walk model‐based method.

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Bootstrapping Iterative Demodulation and Decoding Without Pilot Symbols

Cited by 12 publications

References 47 publications

Parametric Phase Tracking via Expectation Propagation

Parametric Phase Tracking via Expectation Propagation

Low-Complexity Tracking of Laser and Nonlinear Phase Noise in WDM Optical Fiber Systems

Joint parameter estimation and decoding in a distributed receiver

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