Abstract
In practical massive MIMO detection, besides the influence of the algorithm’s own characteristics on the detection results, the hardware circuits also affect the efficiency of signal detection. In Chap. 2, we have introduced four typical iteration algorithms of massive MIMO linear detection, and illustrate their advantages by comparing them with some existing linear detection algorithms. This chapter describes how to implement the four algorithms in VLSI. First, it describes the implementation of the algorithms in the hardware circuits, and the matters needing attention. Then, it introduces the optimization problems in the chip design, including how to improve the throughput of chip, and how to reduce the power consumption and area of chip. Finally, the parameters of the designed chip are compared with those of the existing linear detection algorithms, and then the comprehensive comparison results are obtained.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gpp TS (2009) 3rd generation partnership project; Technical specification group radio access network; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation (Release 8). 3GPP TS 36.211 V.8.6.0
Bartlett MS, Gower JC, Leslie PH (1960) The characteristic function of Hermitian quadratic forms in complex normal variables. Biometrika 47(1/2):199–201
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
Wu M, Yin B, Vosoughi A et al (2013) Approximate matrix inversion for high-throughput data detection in the large-scale MIMO uplink. In: IEEE international symposium on circuits and systems, pp 2155–2158
Schreiber R, Tang WP (1986) On systolic arrays for updating the Cholesky factorization. BIT 26(4):451–466
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
Yin B, Wu M, Wang G et al (2014) A 3.8 Gb/s large-scale MIMO detector for 3GPP LTE-Advanced. In: IEEE international conference on acoustics, speech and signal processing, pp 3879–3883
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 Process 8(5):916–929
Yin B, Wu M, Cavallaro JR et al (2015) VLSI design of large-scale soft-output MIMO detection using conjugate gradients. IEEE Int Symp Circ Syst 1498–1501
Wu Z, Zhang C, Xue Y et al (2016) Efficient architecture for soft-output massive MIMO detection with Gauss-Seidel method. IEEE Int Symp Circ Syst 1886–1889
Choi JW, Lee B, Shim B et al (2013) Low complexity detection and precoding for massive MIMO systems. In: Wireless communications and NETWORKING conference, pp 2857–2861
Wu M, Dick C, Cavallaro JR et al (2016) FPGA design of a coordinate descent data detector for large-scale MU-MIMO. IEEE Int Symp Circ Syst 1894–1897
Liu L (2014) Energy-efficient soft-input soft-output signal detector for iterative MIMO receivers. IEEE Trans Circ Syst I Regul Pap 61(8):2422–2432
Peng G, Liu L, Zhou S et al (2017) A 1.58 Gbps/W 0.40 Gbps/mm? ASIC implementation of MMSE detection for $128 × 8$ 64-QAM massive MIMO in 65 nm CMOS. IEEE Trans Circ Syst I Regul Pap PP(99):1–14
Wu M, Dick C, Cavallaro JR et al (2016) High-throughput data detection for massive MU-MIMO-OFDM using coordinate descent. IEEE Trans Circuits Syst I Regul Pap 63(12):2357–2367
Chen J, Zhang Z, Lu H et al (2016) An intra-iterative interference cancellation detector for large-scale MIMO communications based on convex optimization. IEEE Trans Circuits Syst I Regul Pap 63(11):2062–2072
Dai L, Gao X, Su X et al (2015) Low-complexity soft-output signal detection based on gauss–CSeidel method for uplink multiuser large-scale MIMO systems. IEEE Trans Veh Technol 64(10):4839–4845
Gao X, Dai L, Ma Y et al (2015) Low-complexity near-optimal signal detection for uplink large-scale MIMO systems. Electron Lett 50(18):1326–1328
Kincaid D, Cheney W (2009) Numerical analysis: mathematics of scientific computing. vol 2. Am Math Soc
Casta eda O, Goldstein T, Studer C (2016) Data detection in large multi-antenna wireless systems via approximate semidefinite relaxation. IEEE Trans Circ Syst I Regul Pap PP(99):1–13
Yin B (2014) Low complexity detection and precoding for massive MIMO systems: algorithm, architecture, and application. Diss. Rice University
Prabhu H, Rodrigues JN, Liu L et al (2017) A 60 pJ/b 300 Mb/s 128 × 8 massive MIMO precoder-detector in 28 nm FD-SOI[C]. Solid-State Circuits Conference (ISSCC), 2017 IEEE International. IEEE, pp 60-–61
Tang W, Chen CH, Zhang Z (2016) A 0.58mm2 2.76 Gb/s 79.8pJ/b 256-QAM massive MIMO message-passing detector. Vlsi Circ 1–5
Chen C, Tang W, Zhang Z (2015) 18.7 A 2.4 mm 2 130mW MMSE-nonbinary-LDPC iterative detector-decoder for 4 × 4 256-QAM MIMO in 65 nm CMOS. In: Solid-state circuits conference, pp 1–3
Noethen B, Arnold O, Perez Adeva E et al (2014) 10.7 A 105GOPS 36 mm 2 heterogeneous SDR MPSoC with energy-aware dynamic scheduling and iterative detection-decoding for 4G in 65 nm CMOS. In: Solid-state circuits conference digest of technical papers, pp 188–189
Chen YT, Cheng CC, Tsai TL et al (2017) A 501 mW 7.6l Gb/s integrated message-passing detector and decoder for polar-coded massive MIMO systems[C]. VLSI Circuits, 2017 Symposium on IEEE, pp C330-C331
Winter M, Kunze S, Adeva EP et al (2012) A 335 Mb/s 3.9 mm2 65 nm CMOS flexible MIMO detection-decoding engine achieving 4G wireless data rates[J]
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd. and Science Press, Beijing, China
About this chapter
Cite this chapter
Liu, L., Peng, G., Wei, S. (2019). Architecture of Linear Massive MIMO Detection. In: Massive MIMO Detection Algorithm and VLSI Architecture. Springer, Singapore. https://doi.org/10.1007/978-981-13-6362-7_3
Download citation
DOI: https://doi.org/10.1007/978-981-13-6362-7_3
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)