Abstract
Driven by the requirement of multi-dimensional computing in contemporary wireless communication technologies, reconfigurable platforms have come to the era of vector-based architectures. In this chapter, the reconfigurable cell array developed in Chaps. 4 and 5 is extended with extensive vector computing capabilities, aiming for high-throughput baseband processing in MIMO-OFDM systems. Besides the heterogeneous and hierarchical resource deployments, a vector-enhanced SIMD structure and various memory access schemes are employed. These architectural enhancements are designed to suffice stringent computational requirements while retaining high flexibility and hardware efficiency. Implemented in a 65 nm CMOS technology, the cell array occupies 8.88 mm2 core area. To illustrate its performance and flexibility, three computationally intensive blocks, namely channel estimation, channel matrix pre-processing, and symbol detection, of a 4 × 4 MIMO processing chain in a 20 MHz 64-QAM Long term evolution-advanced (LTE-A) downlink are mapped and processed in real-time. Operating at 500 MHz and 1.2 V voltage supply, the achieved throughput is 367.88 Mb/s and the average power consumption is 548.78 mW. The corresponding energy consumption for processing one information bit is 1.49 nJ. Comparing to state-of-the-art implementations, the presented solution outperforms related programmable platforms by several orders of magnitude in energy efficiency, and achieves similar level of area and energy efficiency to that of ASICs.
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- 1.
Channel coefficients of each subcarrier are stationary over time within one time slot, i.e., 0.5 ms in LTE-A.
References
3GPP TS 36.101 V11.4.0: user equipment (UE) radio transmission and reception (Release 11), Mar 2013. http://www.3gpp.org/ftp/Specs/archive/36_series/36.101/36101-b40.zip
3GPP TS 36.212 V11.2.0: multiplexing and channel coding (Release 11), Feb 2013. http://www.3gpp.org/ftp/Specs/archive/36_series/36.212/36212-b20.zip
L. Bahl, J. Cocke, F. Jelinek, J. Raviv, Optimal decoding of linear codes for minimizing symbol error rate. IEEE Trans. Inf. Theory 20(2), 284–287 (1974)
L.G. Barbero, J.S. Thompson, Fixing the complexity of the sphere decoder for MIMO detection. IEEE Trans. Wirel. Commun. 7(6), 2131–2142 (2008)
C. Bernard, F. Clermidy, A low-power VLIW processor for 3GPP-LTE complex numbers processing, in Design, Automation Test in Europe Conference Exhibition (DATE), Mar 2011, pp. 1–6
C. Berrou, A. Glavieux, Near optimum error correcting coding and decoding: turbo-codes. IEEE Trans. Commun. 44(10), 1261–1271 (1996)
A. Burg, et al., VLSI implementation of MIMO detection using the sphere decoding algorithm. IEEE J. Solid State Circuits 40(7), 1566–1577 (2005)
J. Byrne, Tensilica DSP targets LTE advanced, Mar 2011. http://www.tensilica.com/uploads/pdf/MPR_BBE64.pdf
R.C.H. Chang, C.H. Lin, K.H. Lin, C.L. Huang, F.C. Chen, Iterative QR decomposition architecture using the modified Gram-Schmidt algorithm for MIMO systems. IEEE Trans. Circuits Syst. Regul. Pap. 57(5), 1095–1102 (2010)
P.L. Chiu, L.Z. Huang, L.W. Chai, C.F. Liao, Y.H. Huang, A 684Mbps 57mW Joint QR decomposition and MIMO processor for 4×4 MIMO-OFDM systems, in IEEE Asian Solid State Circuits Conference (ASSCC), Nov 2011, pp. 309–312
F. Clermidy, et al., A 477mW NoC-based digital baseband for MIMO 4G SDR, in IEEE International Solid-State Circuits Conference (ISSCC), Feb 2010, pp. 278–279
V. Derudder, et al., A 200Mbps+ 2.14nJ/b digital baseband multi processor system-on-chip for SDRs, in IEEE Symposium on VLSI Circuits (VLSIC), 2009, pp. 292–293
I. Diaz, B. Sathyanarayanan, A. Malek, F. Foroughi, J.N. Rodrigues, Highly scalable implementation of a robust MMSE channel estimator for OFDM multi-standard environment, in IEEE Workshop on Signal Processing Systems (SiPS), 2011, pp. 311–315
O. Edfors, M. Sandell, J.J. van de Beek, S.K. Wilson, P.O. Börjesson, OFDM channel estimation by singular value decomposition. IEEE Trans. Commun. 46(7), 931–939 (1998)
F. Edman, V. Öwall, A scalable pipelined complex valued matrix inversion architecture. in IEEE International Symposium on Circuits and Systems (ISCAS), vol. 5, 2005, pp. 4489–4492
M.D. Ercegovac, L. Imbert, D.W. Matula, J.M. Muller, G. Wei, Improving Goldschmidt division, square root, and square root reciprocal. IEEE Trans. Comput. 49(7), 759–763 (2000)
R. Fasthuber, et al., Exploration of soft-output MIMO detector implementations on Massive parallel processors. J. Signal Process. Syst. 64, 75–92 (2011) J. Signal Process. Syst. 64, 75–92 (2011)
S. Gifford, C. Bergstrom, S. Chuprun, Adaptive and linear prediction channel tracking algorithms for mobile OFDM-MIMO applications, in IEEE Military Communications Conference (MILCOM), vol. 2, Oct 2005, pp. 1298–1302
G.H. Golub, C.F. Van Loan, Matrix Computations, 3rd edn. (Johns Hopkins University Press, Baltimore, Maryland, 1996)
L. Gor, M. Faulkner, Power reduction through upper triangular matrix tracking in QR detection MIMO receivers, in IEEE 64th Vehicular Technology Conference (VTC), Sept 2006, pp. 1–5
S. Granlund, L. Liu, C. Zhang, V. Öwall, A low-latency high-throughput soft-output signal detector for spatial multiplexing MIMO systems. Microprocess. Microsyst. 2015. http://www.sciencedirect.com/science/article/pii/S0141933115000034
Z. Guo, P. Nilsson, Algorithm and implementation of the K-best sphere decoding for MIMO detection. IEEE J. Sel. Areas Commun. 24(3), 491–503 (2006)
S. Haene, D. Perels, A. Burg, A real-time 4-Stream MIMO-OFDM transceiver: system design, FPGA implementation, and characterization. IEEE J. Sel. Areas Commun. 26(6), 877–889 (2008)
R.W. Heath, A. Paulraj, Antenna selection for spatial multiplexing systems based on minimum error rate, in IEEE International Conference on Communications (ICC), vol. 7, 2001, pp. 2276–2280
M.H. Hsieh, C.H. Wei, Channel estimation for OFDM systems based on comb-type pilot arrangement in frequency selective fading channels. IEEE Trans. Consum. Electron. 44(1), 217–225 (1998)
X. Huang, C. Liang, J. Ma, System architecture and implementation of MIMO sphere decoders on FPGA. IEEE Trans. Very Large Scale Integr. VLSI Syst. 16(2), 188–197 (2008)
Z.-Y. Huang, P.-Y. Tsai, Efficient implementation of QR decomposition for gigabit MIMO-OFDM systems. IEEE Trans. Circuits Syst. Regul. Pap. 58(10), 2531–2542 (2011)
J. Janhunen, O. Silven, M. Juntti, M. Myllyla, Software defined radio implementation of K-best list sphere detector algorithm, in International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS), July 2008, pp. 100–107
J. Janhunen, T. Pitkanen, O. Silven, M. Juntti, Fixed- and floating-point processor comparison for MIMO-OFDM detector. IEEE J. Sel. Top. Sign. Proces. 5(8), 1588–1598 (2011)
Y. Kim, R.N. Mahapatra, I. Park, K. Choi, Low power reconfiguration technique for coarse-grained reconfigurable architecture. IEEE Trans. Very Large Scale Integr. VLSI Syst. 17(5), 593–603 (2009)
C. Kozyrakis, D. Patterson, Vector vs. superscalar and VLIW architectures for embedded multimedia benchmarks, in 35th Annual IEEE/ACM International Symposium on Microarchitecture, 2002, pp. 283–293
H. Lee, C. Chakrabarti, T. Mudge, A low-power DSP for wireless communications. IEEE Trans. Very Large Scale Integr. VLSI Syst. 18(9), 1310–1322 (2010)
L. Liu, F. Ye, X. Ma, T. Zhang, J. Ren, A 1.1-Gb/s 115-pJ/bit configurable MIMO detector using 0.13-μCMOS technology. IEEE Trans. Circuits Syst. Express Briefs 57(9), 701–705 (2010)
L. Liu, J. Löfgren, P. Nilsson, Area-efficient configurable high-throughput signal detector supporting multiple MIMO modes. IEEE Trans. Circuits Syst. Regul. Pap. 59(9), 2085–2096 (2012)
J. Löfgren, L. Liu, O. Edfors, P. Nilsson, Improved matching-pursuit implementation for LTE channel estimation. IEEE Trans. Circuits Syst. Regul. Pap. 61(1), 226–237 (2014)
P. Luethi, A. Burg, S. Haene, D. Perels, N. Felber, W. Fichtner, VLSI implementation of a high-speed iterative sorted MMSE QR decomposition, in IEEE International Symposium on Circuits and Systems (ISCAS), 2007, pp. 1421–1424
M. Li, et al., Optimizing near-ML MIMO detector for SDR baseband on parallel programmable architectures, in Design, Automation and Test in Europe (DATE), Mar 2008, pp. 444–449
M. Mahdavi, M. Shabany, Novel MIMO detection algorithm for high-order constellations in the complex domain. IEEE Trans. Very Large Scale Integr. VLSI Syst. 21(5), 834–847 (2013)
K. Mohammed, B. Daneshrad, A MIMO decoder accelerator for next generation wireless communications. IEEE Trans. Very Large Scale Integr. VLSI Syst. 18(11), 1544–1555 (2010)
A. Nilsson, E. Tell, D. Liu, An 11 mm2, 70 mW fully programmable baseband processor for mobile WiMAX and DVB-T/H in 0.12μm CMOS. IEEE J. Solid-State Circuits 44(1), 90–97 (2009)
T. Nylanden, J. Janhunen, O. Silven, M. Juntti, A GPU implementation for two MIMO-OFDM detectors, in International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS), July 2010, pp. 293–300
J.M. Rabaey, A. Chandrakasan, B. Nikolic, Digital Integrated Circuits - A Design Perspective, 2nd edn. (Prentice Hall, Englewood Cliffs, 2002)
S. Roger, C. Ramiro, A. Gonzalez, V. Almenar, A.M. Vidal, Fully parallel GPU implementation of a fixed-complexity soft-output MIMO detector. IEEE Trans. Veh. Technol. 61(8), 3796–3800 (2012)
M. Shabany, D. Patel, P.G. Gulak, A low-latency low-power QR-decomposition ASIC implementation in 0.13 μm CMOS. IEEE Trans. Circuits Syst. Regul. Pap. 60(2), 327–340 (2013)
M. Šimko, D. Wu, C. Mehlfüehrer, J. Eilert, D. Liu, Implementation aspects of channel estimation for 3GPP LTE terminals, in 11th European Wireless Conference, Apr 2011, pp. 1–5
D. Sui, Y. Li, J. Wang, P. Wang, B. Zhou, High throughput MIMO-OFDM detection with graphics processing units, in IEEE International Conference on Computer Science and Automation Engineering (CSAE), vol. 2, May 2012, pp. 176–179
M. Thuresson, et al., FlexCore: utilizing exposed datapath control for efficient computing. J. Signal Process. Syst. 57(1), 5–19 (2009)
M. Wu, S. Gupta, Y. Sun, J.R. Cavallaro, A GPU implementation of a real-time MIMO detector, in IEEE Workshop on Signal Processing Systems (SiPS), Oct 2009, pp. 303–308
D. Wübben, J. Rinas, R. Böhnke, V. Kühn, K.D. Kammeyer, Efficient algorithm for detecting layered space-time codes, in 4th International ITG Conference on Source and Channel Coding (SCC), Jan 2002, pp. 399–405
D. Wübben, R. Böhnke, V. Kühn, K.D. Kammeyer, MMSE extension of V-BLAST based on sorted QR decomposition, in IEEE 58th Vehicular Technology Conference (VTC), vol. 1, 2003, pp. 508–512
Y. Xie, W. Wolf, H. Lekatsas, Code compression for embedded VLIW processors using variable-to-fixed coding. IEEE Trans. Very Large Scale Integr. VLSI Syst. 14(5), 525–536 (2006)
C. Yang, D. Marković, A flexible DSP architecture for MIMO sphere decoding. IEEE Trans. Circuits Syst. Regul. Pap. 56(10), 2301–2314 (2009)
S. Ye, S. H. Wong, C. Worrall, Enhanced physical downlink control channel in LTE advanced release 11. IEEE Commun. Mag. 51(2), 82–89 (2013)
C. Zhang, T. Lenart, H. Svensson, V. Öwall, Design of coarse-grained dynamically reconfigurable architecture for DSP applications, in International Conference on Reconfigurable Computing and FPGAs (ReConFig), Dec 2009, pp. 338–343
C. Zhang, L. Liu, D. Marković, V. Öwall, A heterogeneous reconfigurable cell array for MIMO signal processing. IEEE Transactions on Circuits and Systems I: Regular Papers, 62(3), 733–742 (2015)
C. Zhang, H. Prabhu, Y. Liu, L. Liu, O. Edfors, V. Öwall, Energy efficient group-sort QRD processor with on-line update for MIMO channel pre-processing. IEEE Trans. Circuits Syst. Regul. Pap. 62(5), 1220–1229 (2015)
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Zhang, C., Liu, L., Öwall, V. (2016). Multi-Task MIMO Signal Processing. In: Heterogeneous Reconfigurable Processors for Real-Time Baseband Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-24004-6_6
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