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High-Speed VLSI Implementation of an Improved Parallel Delayed LMS Algorithm

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Abstract

Motivated by improvement of convergence rate and throughput performance, this work develops a systematic high-speed VLSI implementation of the adaptive filter based on the improved 2-parallel delayed LMS (PDLMS) algorithm. The proposed design uses a novel hardware-efficient architecture for weight updating based on parallel adaptive 2-by-2 algorithm. Compared with the conventional filter structure, the parallel filter has higher throughput rate and lower power dissipation. To improve the convergent characteristic of the adaptive digital filter, we have selected one branch from two weight update branches which has better system performance. The fine-grained arithmetic operation unit and the retiming technology are employed to reduce the delay of critical path effectively. From the ASIC synthesis results we find that the proposed architecture of an 8-tap filter has nearly 24% less power and nearly 18% less area-delay-product (ADP) than the best existing structure. Thus it can be seen that the proposed design has the important practice instruction significance.

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References

  1. Meher PK, Park SY (2014) Critical-path analysis and low-complexity implementation of the LMS adaptive algorithm. IEEE Trans Circuits Syst I, Reg Papers 61(3):778–788

    Article  Google Scholar 

  2. Liu D, Wang M (2016) Delay-optimized floating point fused add-subtract unit. IEICE Electron Express 12(1):1–12

    MathSciNet  Google Scholar 

  3. Yi Y, Woods R, Ting LK, Cowan CFN (2005) High speed FPGA-based implementations of delayed-LMS filters. Journal of VLSI signal processing 39(2):113–131

    Article  Google Scholar 

  4. Meher PK, Maheshwari M (2011) A high-speed FIR adaptive filter architecture using a modified delayed LMS algorithm. IEEE International Symposium of Circuits and Systems (ISCAS). IEEE, Rio de Janeiro, Brazil, pp 121–124

    Google Scholar 

  5. Shanbhag NR, Parhi KK (1994) Pipelined adaptive Digtal filters. Kluwer Academic Publishers, Dordrecht

    Book  Google Scholar 

  6. Douglas SC, Zhu Q, Smith KF (1998) A pipelined LMS adaptive FIR filter architecture without adaptation delay. IEEE trans. Signal Process 46(3):775–779

    Google Scholar 

  7. Ting LK, Woods R, Cowan CFN (2005) Virtex FPGA implementation of a pipelined adaptive LMS predictor for electronic support measures receivers. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 13(1):86–95

    Article  Google Scholar 

  8. Park Y, Meher PK (2013) Low-power, high-throughput, and low-area adaptive FIR filter based on distributed arithmetic. IEEE Transactions on Circuits and Systems II: Express Briefs 60(6):346–350

    Google Scholar 

  9. Mohanty BK, Meher PK (2009) Delayed block LMS algorithm and concurrent architecture for high-speed implementation of adaptive FIR filters. Proc. IEEE Region 10 TENCON2008 Conference. IEEE, Hyderabad, pp 1–5

    Google Scholar 

  10. Van LD, Feng WS (2001) An efficient systolic architecture for the DLMS adaptive filter and its applications. IEEE trans. Circuits Syst. II, analog and digital. Signal Process 48(4):359–366

    Google Scholar 

  11. Liu X, Zhang X (2020) NOMA-based resource allocation for cluster-based cognitive industrial internet of things. IEEE Trans. Industrial Informatics 16(8):5379–5388

    Article  Google Scholar 

  12. Liu X, Zhang X, Lu W (2021) QoS-guarantee resource allocation for multibeam satellite industrial internet of things with NOMA. IEEE Trans Industrial Informatics 17(3):2052–2061

    Article  Google Scholar 

  13. Feng Li KY, Lam and Xin Liu. (2018) Joint pricing and power allocation for multibeam satellite systems with dynamic game model. IEEE Trans Vehicular Technology 67(3):2398–2408

    Article  Google Scholar 

  14. Liu X, Zhang X, Jia M (2018) 5G-based green broadband communication system design with simultaneous wireless information and power transfer. Physical Communication 28:130–137

    Article  Google Scholar 

  15. Y.-C Tsao and K. Choi.: Area efficient parallel fir digital filter structures for symmetric convolutions based on fast fir algorithm” IEEE Trans VLSI Syst 20(2), 366–371 (2012)

  16. Srinivasan S, Bhudiya K, Ramanarayanan R et al (2013) Split-path fused floating point multiply accumulate (FPMAC). IEEE 21st Symposium on Computer Arithmetic. IEEE, Austin, pp 17–24

    Google Scholar 

  17. Long G, Ling F, Proakis JG (1989) The LMS algorithm with delayed coefficient adaptation. IEEE Trans Accoust, Speech, and Signal Processing 37(9):1397–1405

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the project of shenzhen science and technology innovation committee (JCYJ20180307123857045), the scientific research project in school-level (SZIIT2019KJ026) and the project of guangdong provincial department of education (2019GKQNCX122).

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Correspondence to Zhou Wu.

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Liu, M., Guan, M., Wu, Z. et al. High-Speed VLSI Implementation of an Improved Parallel Delayed LMS Algorithm. Mobile Netw Appl 27, 1593–1603 (2022). https://doi.org/10.1007/s11036-021-01877-4

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  • DOI: https://doi.org/10.1007/s11036-021-01877-4

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