Joint LDPC decoding and timing recovery using code constraint feedback

Lee, D.-U.; Valles, Esteban L.; Villasenor, John; Jones, Christopher R.

doi:10.1109/lcomm.2006.1603380

Cited by 16 publications

(6 citation statements)

References 7 publications

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“…In addition, incremental operations are limited. Finally, our algorithm is more stable than the algorithm in [4] because our algorithm employs soft decisions, whereas [4] uses hard decisions as code constraint feedback. Hard decisions yield false judgments when even decoded bits are reversed falsely, which involves computing the same parity check equation.…”

Section: Simulation Results and Analysesmentioning

confidence: 99%

“…Constant timing frequency offset and random walk [4] can be combined in our model, because proper timing error models exist as constant timing phase offsets. The combined equation is presented as…”

Section: Timing Error System Modelmentioning

confidence: 99%

“…In addition, a conventional first-order PLL-based structure with a decision-directed Mueller-Müller timing error detector (M&M TED) [6] can also be used as a supplement like [4] to implement the iterative timing tracking. At each iteration, the M&M TED is executed by provided with more accurate symbols decoded by the LDPC decoder and the re-sampled received signals, which are compensated with the optimal estimate of timing phase and frequency offset by our proposed algorithm.…”

Section: Proposed Iterative Timing Recoverymentioning

confidence: 99%

“…Iterative timing recovery uses the metrics from the codewords output of the channel decoding process [3]. A method with LDPC decoding hard decision metrics (i.e., code constraint feedback) to obtain good timing recovery has been proposed in [4]. However, it suffered from insufficient hard metrics, or unreliable satisfaction percentages of decoded codewords for the parity check equation for just few iterations [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…A method with LDPC decoding hard decision metrics (i.e., code constraint feedback) to obtain good timing recovery has been proposed in [4]. However, it suffered from insufficient hard metrics, or unreliable satisfaction percentages of decoded codewords for the parity check equation for just few iterations [4,5]. Studies also suggested the use of timing recovery based on soft LDPC metrics with complex optimization calculations [6].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Iterative Timing Recovery of Low-Density Parity-Check Decoding Metrics Using the Steepest Descent Algorithm for Satellite Communications at Low SNRs

et al. 2021

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An efficient iterative timing recovery via steepest descent of low-density parity-check (LDPC) decoding metrics is presented. In the proposed algorithm, a more accurate symbol timing synchronization is achieved at a low signal-to-noise (SNR) without any pilot symbol by maximizing the sum of the square of all soft metrics in LDPC decoding. The principle of the above-proposed algorithm is analyzed theoretically with the evolution trend of the probability mean of the soft LDPC decoding metrics by the Gaussian approximation. In addition, an efficiently approximate gradient descent algorithm is adopted to obtain excellent timing recovery with rather low complexity and global convergence. Finally, a complete timing recovery is accomplished where the proposed scheme performs fine timing capture, followed by a traditional Mueller–Müller (M&M) timing recovery, which acquires timing track. Using the proposed iterative timing recovery method, the simulation results indicate that the performance of the LDPC coded binary phase shift keying (BPSK) scheme with rather large timing errors is just within 0.1 dB of the ideal code performance at the cost of some rational computation and storage. Therefore, the proposed iterative timing recovery can be efficiently applied on occasions of the weak signal timing synchronization in satellite communications and so on.

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Section: Simulation Results and Analysesmentioning

confidence: 99%

Section: Timing Error System Modelmentioning

confidence: 99%