Skip to main content
SpringerLink
Log in

Nonlinear Massive MIMO Signal Detection Algorithm

  • Chapter
  • First Online:
Massive MIMO Detection Algorithm and VLSI Architecture

  • 973 Accesses

Abstract

This chapter first introduces several conventional nonlinear MIMO signal detection algorithms in Sect. 4.1. The optimal nonlinear ML signal detection algorithm is introduced first, and then the SD signal detection algorithm and the K-Best signal detection algorithm evolved from the nonlinear ML signal detection algorithm are introduced. Section 4.2 presents a K-best signal detection and preprocessing algorithm in high-order MIMO systems, combining the Cholesky sorted QR decomposition and partial iterative lattice reduction (CHOSLAR). At the same time, the algorithm uses the partial iterative lattice reduction (PILR) algorithm to acquire more asymptotically orthogonal matrix R. After the preprocessing, the K-Best signal detector combined with ordering reduction and branch expansion can achieve the detection accuracy similar to that of ML signal detection algorithm. Section 4.3 presents another new signal detection algorithm, TASER algorithm. Based on semi-definite relaxation, the TASER algorithm can achieve the signal detection performance of approximate ML within the computational complexity of the polynomial (with the number of transmitting antennas or time slots as independent variables) in the system with low bit rate and fixed modulation scheme.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dai L, Gao X, Su X et al (2015) Low-complexity soft-output signal detection based on Gauss-Seidel method for uplink multiuser large-scale MIMO systems. IEEE Trans Veh Technol 64(10):4839–4845

    Article  Google Scholar 

  2. Studer C, Fateh S, Seethaler D (2011) ASIC implementation of soft-input soft-output MIMO detection using MMSE parallel interference cancellation. IEEE J Solid-State Circuits 46(7):1754–1765

    Article  Google Scholar 

  3. Wu M, Yin B, Wang G et al (2014) Large-scale MIMO detection for 3GPP LTE: algorithms and FPGA implementations. IEEE J Sel Top Sign Proces 8(5):916–929

    Article  Google Scholar 

  4. Peng G, Liu L, Zhou S et al (2017) A 1.58 Gbps/W 0.40 Gbps/mm2 ASIC implementation of MMSE detection for $128x8$ 64-QAM massive MIMO in 65 nm CMOS. IEEE Trans Circuits Syst I Regul Pap PP(99):1–14

    Google Scholar 

  5. Peng G, Liu L, Zhang P et al (2017) Low-computing-load, high-parallelism detection method based on Chebyshev iteration for massive MIMO systems with VLSI architecture. IEEE Trans Signal Process 65(14):3775–3788

    Article  MathSciNet  MATH  Google Scholar 

  6. Gao X, Dai L, Hu Y et al (2015) Low-complexity signal detection for large-scale MIMO in optical wireless communications. IEEE J Sel Areas Commun 33(9):1903–1912

    Article  Google Scholar 

  7. Chu X, Mcallister J (2012) Software-defined sphere decoding for FPGA-based MIMO detection. IEEE Trans Signal Process 60(11):6017–6026

    Article  MathSciNet  MATH  Google Scholar 

  8. Huang ZY, Tsai PY (2011) Efficient implementation of QR decomposition for gigabit MIMO-OFDM systems. IEEE Trans Circuits Syst I Regul Pap 58(10):2531–2542

    Article  MathSciNet  Google Scholar 

  9. Peng G, Liu L, Zhou S et al (2018). Algorithm and architecture of a low-complexity and high-parallelism preprocessing-based K-best detector for large-scale MIMO systems. IEEE Trans Sig Process PP(99):1

    Google Scholar 

  10. Castañeda O, Goldstein T, Studer C (2016) Data detection in large multi-antenna wireless systems via approximate semidefinite relaxation. IEEE Trans Circuits Syst I Reg Pap PP(99):1–13

    Google Scholar 

  11. Soma U, Tipparti AK, Kunupalli SR Improved performance of low complexity K-best sphere decoder algorithm. In: International Conference on Inventive Communication and Computational Technologies, pp 490–495

    Google Scholar 

  12. Fincke U, Pohst M (1985) Improved methods for calculating vectors of short length in a lattice, including a complexity analysis. Math Comput 44(170):463–471

    Article  MathSciNet  MATH  Google Scholar 

  13. Barbero LG, Thompson JS (2006) Performance analysis of a fixed-complexity sphere decoder in high-dimensional mimo systems. In: Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, p IV

    Google Scholar 

  14. Shen CA, Eltawil AM (2010) A radius adaptive K-best decoder with early termination: algorithm and VLSI architecture. IEEE Trans Circuits Syst I Regul Pap 57(9):2476–2486

    Article  MathSciNet  Google Scholar 

  15. Burg A, Borgmann M, Wenk M et al (2005) VLSI implementation of MIMO detection using the sphere decoding algorithm. IEEE J Solid-State Circuits 40(7):1566–1577

    Article  Google Scholar 

  16. Taherzadeh M, Mobasher A, Khandani AK (2006) LLL reduction achieves the receive diversity in MIMO decoding. IEEE Trans Inf Theory 53(12):4801–4805

    Article  MathSciNet  MATH  Google Scholar 

  17. Barbero LG, Thompson JS (2008) Fixing the complexity of the sphere decoder for MIMO detection. IEEE Trans Wireless Commun 7(6):2131–2142

    Article  Google Scholar 

  18. Xiong C, Zhang X, Wu K et al (2009) A simplified fixed-complexity sphere decoder for V-BLAST systems. IEEE Commun Lett 13(8):582–584

    Article  Google Scholar 

  19. Khairy MS, Abdallah MM, Habib ED (2009) Efficient FPGA implementation of MIMO decoder for mobile WiMAX system. In: IEEE International Conference on Communications, pp 2871–2875

    Google Scholar 

  20. Liao CF, Wang JY, Huang YH (2014) A 3.1 Gb/s 8*8 sorting reduced K-best detector with lattice reduction and QR decomposition. IEEE Trans Very Large Scale Integr Syst 22(12):2675–2688

    Google Scholar 

  21. Fujino T, Wakazono S, Sasaki Y (2009) A gram-schmidt based lattice-reduction aided MMSE detection in MIMO Systems. 1–8

    Google Scholar 

  22. Yan Z, He G, Ren Y et al (2015) Design and implementation of flexible dual-mode soft-output MIMO detector with channel preprocessing. IEEE Trans Circuits Syst I Regul Pap 62(11):2706–2717

    Article  MathSciNet  Google Scholar 

  23. Sarieddeen H, Mansour MM, Jalloul L et al (2017) High order multi-user MIMO subspace detection. J Sign Process Syst 1:1–17

    Google Scholar 

  24. Zhang C, Liu L, Marković D et al (2015) A heterogeneous reconfigurable cell array for MIMO signal processing. IEEE Trans Circuits Syst I Regul Pap 62(3):733–742

    Article  MathSciNet  Google Scholar 

  25. Chiu PL, Huang LZ, Chai LW et al (2011) A 684Mbps 57mW joint QR decomposition and MIMO processor for 4×4 MIMO-OFDM systems. In: Solid State Circuits Conference, pp 309–312

    Google Scholar 

  26. Kurniawan IH, Yoon JH, Park J (2013) Multidimensional householder based high-speed QR decomposition architecture for MIMO receivers. In: IEEE International Symposium on Circuits and Systems, pp 2159–2162

    Google Scholar 

  27. Wang JY, Lai RH, Chen CM et al (2010) A 2x2—8x8 sorted QR decomposition processor for MIMO detection. Inst Electr Electron Eng

    Google Scholar 

  28. Sarieddeen H, Mansour MM, Chehab A (2016) Efficient subspace detection for high-order MIMO systems. In: The IEEE International Conference on Acoustics, Speech and Signal Processing

    Google Scholar 

  29. Liu T, Zhang JK, Wong KM (2009) Optimal precoder design for correlated MIMO communication systems using zero-forcing decision feedback equalization. IEEE Trans Signal Process 57(9):3600–3612

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhang C, Prabhu H, Liu Y et al (2015) Energy efficient group-sort QRD processor with on-line update for MIMO channel pre-processing. IEEE Trans Circuits Syst I Regul Pap 62(5):1220–1229

    Article  MathSciNet  Google Scholar 

  31. Yang S, Hanzo L (2013) Exact Bayes’ theorem based probabilistic data association for iterative MIMO detection and decoding. In: Global Communications Conference, pp 1891–1896

    Google Scholar 

  32. Chen Y, Halbauer H, Jeschke M et al (2010) An efficient Cholesky Decomposition based multiuser MIMO detection algorithm. In: IEEE International Symposium on Personal Indoor and Mobile Radio Communications, pp 499–503

    Google Scholar 

  33. Xue Y, Zhang C, Zhang S et al (2016) Steepest descent method based soft-output detection for massive MIMO uplink. In: IEEE International Workshop on Signal Processing Systems, pp 273–278

    Google Scholar 

  34. Jiang W, Asai Y, Kubota S (2015) A novel detection scheme for MIMO spatial multiplexing systems with partial lattice reduction. In: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pp 2524–2528

    Google Scholar 

  35. Mansour MM, Jalloul LMA (2015) Optimized configurable architectures for scalable soft-input soft-output MIMO detectors with 256-QAM. IEEE Trans Signal Process 63(18):4969–4984

    Article  MathSciNet  MATH  Google Scholar 

  36. Luo ZQ, Ma WK, So MC et al (2010) Semidefinite relaxation of quadratic optimization problems. IEEE Signal Process Mag 27(3):20–34

    Article  Google Scholar 

  37. Alshamary HAJ, Anjum MF, Alnaffouri T et al (2015) Optimal non-coherent data detection for massive SIMO wireless systems with general constellations: a polynomial complexity solution. In: Signal Processing and Signal Processing Education Workshop, pp 172–177

    Google Scholar 

  38. Jalden J, Ottersten B (2008) The diversity order of the semidefinite relaxation detector. IEEE Trans Inf Theory 54(4):1406–1422

    Article  MathSciNet  MATH  Google Scholar 

  39. Harbrecht H, Peters M, Schneider R (2012) On the low-rank approximation by the pivoted Cholesky decomposition. Appl Numer Math 62(4):428–440

    Article  MathSciNet  MATH  Google Scholar 

  40. Goldstein T, Studer C, Baraniuk R (2014) A field guide to forward-backward splitting with a FASTA implementation. Computer Science

    Google Scholar 

  41. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. Siam J Imaging Sci 2(1):183–202

    Article  MathSciNet  MATH  Google Scholar 

  42. Benzi M (2002) Preconditioning techniques for large linear systems: a survey. J Comput Phys 182(2):418–477

    Article  MathSciNet  MATH  Google Scholar 

  43. Attouch H, Bolte J, Svaiter BF (2013) Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss-Seidel methods. Math Program 137(1–2):91–129

    Article  MathSciNet  MATH  Google Scholar 

  44. Boumal N (2015) A Riemannian low-rank method for optimization over semidefinite matrices with block-diagonal constraints. Mathematics 1001–1005

    Google Scholar 

  45. Wenk M, Zellweger M, Burg A et al (2006) K-best MIMO detection VLSI architectures achieving up to 424 Mbps. In: Proceedings of the IEEE International Symposium on Circuits and Systems, 2006. ISCAS 2006, pp 4–1154

    Google Scholar 

  46. Rusek F, Persson D, Lau BK et al (2012) Scaling up MIMO: opportunities and challenges with very large arrays. Sig Process Mag IEEE 30(1):40–60

    Article  Google Scholar 

  47. Yin B, Wu M, Cavallaro JR et al (2015) VLSI design of large-scale soft-output MIMO detection using conjugate gradients. In: IEEE International Symposium on Circuits and Systems, pp 1498–1501

    Google Scholar 

  48. Wong KW, Tsui CY, Cheng SK et al (2002) A VLSI architecture of a K-best lattice decoding algorithm for MIMO channels. IEEE Int Symp Circuits Syst 3:273–276

    Google Scholar 

  49. Wu M, Dick C, Cavallaro JR et al (2016) FPGA design of a coordinate descent data detector for large-scale MU-MIMO. In: IEEE International Symposium on Circuits and Systems, pp 1894–1897

    Google Scholar 

  50. Wu Z, Zhang C, Xue Y et al (2016) Efficient architecture for soft-output massive MIMO detection with Gauss-Seidel method. In: IEEE International Symposium on Circuits and Systems, pp 1886–1889

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Microelectronics, Tsinghua University, Beijing, China

    Leibo Liu

  2. Institute of Microelectronics, Tsinghua University, Beijing, China

    Guiqiang Peng

  3. Institute of Microelectronics, Tsinghua University, Beijing, China

    Shaojun Wei

Authors
  1. Leibo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guiqiang Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaojun Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leibo Liu .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd. and Science Press, Beijing, China

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Liu, L., Peng, G., Wei, S. (2019). Nonlinear Massive MIMO Signal Detection Algorithm. In: Massive MIMO Detection Algorithm and VLSI Architecture. Springer, Singapore. https://doi.org/10.1007/978-981-13-6362-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6362-7_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6361-0

  • Online ISBN: 978-981-13-6362-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation