CCSDS 131.2-B-1 Frequency Estimation Trade-Offs and a Novel Multi-Algorithm FPGA Architecture

Bertolucci, Matteo; Cassettari, Riccardo; Fanucci, Luca

doi:10.1109/iccece49321.2020.9231178

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…With all this information set, we implemented the M&M and Fitz algorithm using N = 64. With respect to [21], where only one symbol rate was part of the analysis (8.5 Mbaud), the improved frequency estimator shows a working range of 5 Mbaud to 500 Mbaud with a higher N value, meaning also improved accuracy. The architecture of the multi-algorithm estimator, depicted in Figure 23, follows similarly its predecessor with a more balanced pipelining.…”

Section: System Architecture-timing Recoverymentioning

confidence: 99%

“…Implementation results for the improved multi-algorithm frequency recovery are then provided in Table 4 for the Zynq Ultrascale+ XCZU28DR RFSoC FPGA. Unlike in [ 21 ] the estimation also takes into account the feedback loop filter, an NCO implemented using a coarse-fine approach [ 22 ] for the frequency generation and the baseband frequency correction module. The functional representation of the NCO is depicted in Figure 22 by the integrator at the input of the FED module.…”

Section: System Architecturementioning

confidence: 99%

See 1 more Smart Citation

On the Frequency Carrier Offset and Symbol Timing Estimation for CCSDS 131.2-B-1 High Data-Rate Telemetry Receivers

Bertolucci

Cassettari²,

Fanucci

2021

Sensors

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In recent years there have been significant developments in satellite transmitter technology to follow the rapid innovation of sensors on-board new satellites. The CCSDS 131.2-B-1 standard for telemetry downlink, released in 2012, is part of the next generation of standards that aims to support the increased data-rate caused by these improvements in resolution. As a result of its relative novelty, this standard currently lacks in-depth analysis by researchers, but it is also strongly supported by the European Space Agency (ESA) for future missions. For these reasons, it seems important to evaluate how major receiver sub-components, such as timing recovery and carrier frequency correction, can be designed and implemented in new receivers that support this standard. The timing error detectors (TED) and frequency error detectors (FED) were therefore studied on the specific peculiarities of CCSDS 131.2-B-1 in its usual environment of Low Earth Orbit (LEO). Estimators have been evaluated highlighting performances, trade-offs and peculiarities of each one with respect to corresponding architectural choices. Finally, a receiver architecture derived from the paper considerations is proposed in the aim of supporting very different mission scenarios. Specifically, the realized architecture employs a parallel feedforward estimator for the timing recovery section and a novel multi-algorithm feedback frequency correction loop to efficiently cover both low symbol rates (5 Mbaud) and high data-rates (up to 500 Mbaud). This solution represents a good trade-off to support these scenarios in a very compact footprint by pushing the clock frequency to the FPGA limit. The FPGA resources occupation on a Zynq Ultrascale+ RFSoC XCZU28DR FPGA is 5202 LUT, 4851 FF, 5 BRAM, and 21 DSP for the timing recovery part, while the frequency recovery section occupies 1723 LUT, 1511 FF, 2.5 BRAM and 32 DSP.

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Section: System Architecture-timing Recoverymentioning

confidence: 99%

Section: System Architecturementioning

confidence: 99%

On the Frequency Carrier Offset and Symbol Timing Estimation for CCSDS 131.2-B-1 High Data-Rate Telemetry Receivers

Bertolucci

Cassettari²,

Fanucci

2021

Sensors

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“…The DA methods leverage known signal sequences to estimate the frequency offset, simplifying the operational and computational aspects while achieving enhanced estimation accuracy. Examples of DA methods include the maximum likelihood (ML) method [4], the autocorrelation method [5], and the Kay [6] method. These approaches prove particularly suitable for carrier burst transmission systems.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based channel estimation and feedbacks

Zheng¹

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In wireless communication systems, the asynchronization of the oscillators in the transmitter and the receiver along with the Doppler shift due to relative movement may lead to the presence of carrier frequency offset (CFO) in the received signals. Estimation of CFO is crucial for subsequent processing such as coherent demodulation. In this brief, we demonstrate the utilization of deep learning for CFO estimation by employing a residual network (ResNet) to learn and extract signal features from the raw in-phase (I) and quadrature (Q) components of the signals. We use multiple modulation schemes in the training set to make the trained model adaptable to multiple modulations or even new signals. In comparison to the commonly used traditional CFO estimation methods, our proposed IQ-ResNet method exhibits superior performance across various scenarios including different oversampling ratios, various signal lengths, and different channels.

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