Skip to main content

Advertisement

Log in

Upgrading an analog recovery loop for optimized decoding jointly to an increased data rate

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

The maximum likelihood detection theory improves the error rate of a sub-optimal but cheaper, coded symbol recovery loop using oversampling proposed as an alternate solution for the decoding problem without the log-likelihood ratio computation. The former implementation delivers the output data in one-symbol delay, and the required transistor count makes this approach attractive for ultra-low-energy wireless applications. The proposed hardware upgrade includes an analog to digital converter and fixed-point accumulation logic to compute the soft values, replacing a trigger used as a hard detector. This work investigates the soft decoding in the presence of binary and non-binary source symbols. Simulation results show that the soft approach improves the signal-to-noise ratio by 3 dB and 2.5 dB when the encoding rates are 1/3 and 2/3.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data Availability

Please contact the author for data request or visit his Homepage.

Code Availability

Custom code, available on GitHub soon.

Abbreviations

ACS:

Add-compare-select

ADC:

Analog to digital converter

ALC:

Automatic level control

AWGN:

Additive white Gaussian noise

DFA:

Deterministic finite automaton

DSP:

Digital signal processor

ISI:

Inter-symbolic interference

LLR:

Log-likelihood ratio

LNA:

Low-noise amplifier

LPF:

Low-pass filter

ML:

Maximum-likelihood

MP-VCO:

Multi-phase voltage-controlled oscillator

MSE:

Mean square error

PAM:

Pulse amplitude modulation

PDF:

Probability density function

PMF:

Probability mass function

PSK:

Phase-shift keying

RAM:

Random access memory

RF:

Radio-frequency

SNR:

Signal-to-noise ratio

TCM:

Trellis-code modulation

VCA:

Voltage-controlled amplifier

References

  1. Peng, H., Liu, R., Hou, Y., & Zhao, L. (2016). A Gb/s parallel block-based Viterbi decoder for convolutional codes on GPU. In 2016 8th International Conference on Wireless Communications Signal Processing (WCSP) (pp. 1–6). China: Yangzhou.

  2. Hagenauer, J., & Hoeher, P. (1989). A Viterbi algorithm with soft-decision outputs and its applications. In 1989 IEEE Global Telecommunications Conference and Exhibition’Communications Technology for the 1990s and Beyond’ (pp. 1680-1686), TX, USA: Dallas.

  3. Hagenauer, J., & Papke, L. (1994). Decoding turbo-codes with the soft output Viterbi algorithm (SOVA). In Proceedings of IEEE International Symposium on Information Theory (p. 164). Norway: Trondheim, Norway.

  4. Ramteke, S., Kakde, S., Suryawanshi, Y., & Meshram, M. (2015). Performance analysis of Turbo decoder using soft output Viterbi algorithm. In 2015 International Conference on Communications and Signal Processing (ICCSP) (pp. 1332–1336). India: Melmaruvathur.

  5. Visalli, G. (2019). Analysis and performance of coded symbol recovery loop using oversampling. EURASIP Journal on Advances in Signal Processing, 2019, 1–16.

    Article  Google Scholar 

  6. Visalli, G., Pappalardo, F., Avellone, G., Rimi, F., & Galluzzo, A. (2008). Method and system for coding decoding signals and computer program product therefor. Google Patents. US Patent 7,424,068

  7. Hagenauer, J., & Winklhofer, M. (1998). The analog decoder. In Proceedings of the IEEE International Symposium on Information Theory (p. 145). USA: Cambridge, MA.

  8. Loeliger, H., Lustenberger, F., Helfenstein, M., & Tarkoy, F. (1998). Probability propagation and decoding in analog VLSI. In Proceedings of the IEEE International Symposium on Information Theory (p. 146). USA: Cambridge, MA.

  9. Tretter, S. A. (1995). Double-sideband suppressed-carrier amplitude modulation and coherent detection (pp. 73–78). Boston, MA: Springer.

    Google Scholar 

  10. Ungerboeck, G. (1987). Trellis-coded modulation with redundant signal sets part I: Introduction. IEEE Communications Magazine, 25(2), 5–11.

    Article  Google Scholar 

  11. Ungerboeck, G. (1987). Trellis-coded modulation with redundant signal sets part II: State of the art. IEEE Communications Magazine, 25(2), 12–21.

    Article  Google Scholar 

  12. Wei, R., Ritcey, J. A., & Lu, B. (2015). TCM with differential encoding: Set partitioning, trellis designs, and distance analysis. IEEE Transactions on Communications, 63(8), 2776–2787.

    Article  Google Scholar 

  13. Napolitano, A. (2016). Cyclostationarity: New trends and applications. Signal Processing, 120, 385–408.

    Article  Google Scholar 

  14. Izzo, L., & Napolitano, A. (2003). Sampling of generalized almost-cyclostationary signals. IEEE Transactions on Signal Processing, 51(6), 1546–1556.

    Article  Google Scholar 

  15. Cohen, D., Rebeiz, E., Eldar, Y.C., Cabric, D. (2013). Cyclic spectrum reconstruction and cyclostationary detection from sub-Nyquist samples. In 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (pp. 425–429).

  16. Landau, L. T. N., Dörpinghaus, M., & Fettweis, G. P. (2018). 1-bit quantization and oversampling at the receiver: Sequence-based communication. EURASIP Journal on Wireless Communications and Networking, 2018, 1–24.

    Article  Google Scholar 

  17. Krone, S., & Fettweis, G. (2012). Capacity of communications channels with 1-bit quantization and oversampling at the receiver. In 2012 35th IEEE Sarnoff Symposium (pp. 1–7).

  18. Fettweis, G., Dörpinghaus, M., Bender, S., Landau, L., Neuhaus, P., & Schlüter, M. (2019). Zero crossing modulation for communication with temporally oversampled 1-bit quantization. In 2019 53rd Asilomar Conference on Signals, Systems, and Computers (pp. 207–214).

  19. Proakis, J. G. (2007). Digital communications (5th ed.). New York: McGraw Hill.

    Google Scholar 

  20. Oppenheim, A. V., Willsky, A. S., & Nawab, S. H. (1996). Signals and systems (2nd ed.). USA: Prentice-Hall Inc.

    Google Scholar 

  21. Liao, Y.-T., & Richard Shi, C.-J. (2008). A 6–11Ghz multi-phase VCO design with active inductors. In 2008 IEEE International Symposium on Circuits and Systems (pp. 988–991).

  22. Papoulis, A. (2007). Probability, random variables and stochastic processes (5th ed.). New York: McGraw-Hill Companies.

    Google Scholar 

  23. Mesgarzadeh., B., & Alvandpour, A. (2006). A wide-tuning range 1.8 Ghz quadrature VCO utilizing coupled ring oscillators. In 2006 IEEE International Symposium on Circuits and Systems (pp. 4–pp).

  24. Katyal, V., Geiger, R.L., & Chen, D.J. (2008). Adjustable hysteresis CMOS Schmitt triggers. In 2008 IEEE International Symposium on Circuits and Systems (pp. 1938–1941).

  25. Trivedi, R. (2006). Low power and high speed Sample-and-Hold Circuit. 2006 49th IEEE International Midwest Symposium on Circuits and Systems, 1, 453–456.

    Article  Google Scholar 

  26. Kakarountas, A. P., Theodoridis, G., Papadomanolakis, K. S., & Goutis, C. (2003). A novel high-speed counter with counting rate independent of the counter’s length. In 10th IEEE International Conference on Electronics, Circuits and Systems, 2003. ICECS 2003. Proceedings of the 2003 (Vol. 3, pp. 1164–1167).

  27. Mutz, D., & George, K. (2016). Costas loop and FFT based BPSK demodulation for pulsed radar receivers. 2016 IEEE Aerospace Conference (pp. 1–12). Big Sky: MT, USA.

    Google Scholar 

  28. Gallager, R. G. (2008). Principles of digital communication. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  29. Benedetto, S., Biglieri, E., & Castellani, V. (1988). Digital transmission theory. USA: Prentice-Hall Inc.

    Google Scholar 

  30. Farsad, N., Rao, M., & Goldsmith, A. (2018). Deep learning for joint source-channel coding of text. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2326–2330).

  31. Jiang, Y., Kim, H., Asnani, H., Kannan, S., Oh, S., & Viswanath, P. (2020). Joint channel coding and modulation via deep learning. In 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (pp. 1–5).

Download references

Acknowledgements

Not available.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

The author read and approved the final manuscript.

Corresponding author

Correspondence to Giuseppe Visalli.

Ethics declarations

Conflict of interest

The author declares that he has no competing interests.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Visalli, G. Upgrading an analog recovery loop for optimized decoding jointly to an increased data rate. Telecommun Syst 78, 239–252 (2021). https://doi.org/10.1007/s11235-021-00807-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-021-00807-9

Keywords

Navigation