Application-Specific Instruction Set Processor Implementation of List Sphere Detector

  • Juho Antikainen1Email author,

    Affiliated with

    • Perttu Salmela2,

      Affiliated with

      • Olli Silvén1,

        Affiliated with

        • Markku Juntti1,

          Affiliated with

          • Jarmo Takala2 and

            Affiliated with

            • Markus Myllylä1

              Affiliated with

              EURASIP Journal on Embedded Systems20082007:054173

              DOI: 10.1155/2007/54173

              Received: 8 June 2007

              Accepted: 12 November 2007

              Published: 8 January 2008

              Abstract

              Multiple-input multiple-output (MIMO) technology enables higher transmission capacity without additional frequency spectrum and is becoming a part of many wireless system standards. Sphere detection has been introduced in MIMO systems to achieve maximum likelihood (ML) or near-ML estimation with reduced complexity. This paper reviews related work on sphere detector implementations and presents an application-specific instruction set processor (ASIP) implementation of K-best list sphere detector (LSD) using transport triggered architecture (TTA). The implementation is based on using memory and heap data structure for symbol vector sorting. The design space is explored by presenting several variations of the implementation and comparing them with each other in terms of their latencies and hardware complexities. An early proposal for a parallelized architecture with a decoding throughput of approximately 5.3 Mbps is presented

              [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46]

              Authors’ Affiliations

              (1)
              Information Processing Laboratory and Centre for Wireless Communications, University of Oulu
              (2)
              Institute of Digital and Computer Systems, Tampere University of Technology

              References

              1. Telatar IE: Capacity of multi-antenna gaussian channels. In Internal Technical Memorandum. Bell Laboratories, Suffolk, UK; 1995:1-28.
              2. Telatar E: Capacity of multi-antenna gaussian channels. European Transactions Telecommunication 1999, 10: 585-595. 10.1002/ett.4460100604View Article
              3. Foschini GJ, Gans MJ: On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Communications 1998,6(3):311-335. 10.1023/A:1008889222784View Article
              4. Wolniansky PW, Foschini GJ, Golden GD, Valenzuela RA: V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel. Proceedings of the International Symposium on Signals, Systems and Electronics, (ISSSE '98), 1998, Pisa, Italy 295-300.
              5. Gesbert D, Shafi M, Shiu D, Smith PJ, Naguib A: From theory to practice: an overview of MIMO space-time coded wireless systems. IEEE Journal on Selected Areas in Communications 2003,21(3):281-302. 10.1109/JSAC.2003.809458View Article
              6. Paulraj AJ, Gore DA, Nabar RU, Bölcskei H: An overview of MIMO communications—a key to gigabit wireless. Proceedings of the IEEE 2004,92(2):198-217. 10.1109/JPROC.2003.821915View Article
              7. Boelcskei H, Gesbert D, Papadias CB, van der Veen AJ: Space-Time Wireless Systems: From Array Processing to MIMO Communications. Cambridge University Press, Cambridge, UK; 2006.
              8. Myllylä M, Hintikka JM, Cavallaro J, Juntti M, Limingoja M, Byman A: Complexity analysis of MMSE detector architectures for MIMO OFDM systems. Conference Record—Asilomar Conference on Signals, Systems and Computers, 2005, Pacific Grove, Calif, USA 2005: 75-81.
              9. Artés H, Seethaler D, Hlawatsch F: Efficient detection algorithms for MIMO channels: a geometrical approach to approximate ML detection. IEEE Transactions on Signal Processing 2003,51(11):2808-2820. 10.1109/TSP.2003.818210MathSciNetView Article
              10. Golden GD, Foschini CJ, Valenzuela RA, Wolniansky PW: Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture. Electronics Letters 1999,35(1):14-16. 10.1049/el:19990058View Article
              11. Fincke U, Pohst M: Improved methods for calculating vectors of short length in a lattice, including a complexity analysis. Mathematics of Computation 1985, 44: 463–-471. 10.1090/S0025-5718-1985-0777278-8MathSciNetView Article
              12. Damen O, Chkeif A, Belfiore JC: Lattice code decoder for space-time codes. IEEE Communications Letters 2000,4(5):161-163. 10.1109/4234.846498View Article
              13. Yao H, Wornell GW: Lattice-reduction-aided detectors for MIMO communication systems. Proceedings of the IEEE Global Telecommunications Conference, 2002, Taipei, Taiwan 1: 424-428.
              14. Wübben D, Böhnke R, Kühn V, Kammeyer K: Near-maximum-likelihood detection of MIMO systems using MMSE-based lattice-reduction. Proceedings of the IEEE International Conference on Communications, 2004, Paris, France 2: 798-802.
              15. Damen MO, El Gamal H, Caire G: On maximum-likelihood detection and the search for the closest lattice point. IEEE Transactions on Information Theory 2003,49(10):2389-2402. 10.1109/TIT.2003.817444MATHMathSciNetView Article
              16. Silvola P, Hooli K, Juntti M: Sub-optimal soft-output MAP detector with lattice reduction. IEEE Signal Processing Letter 2006, 13: 321–-324. 10.1109/LSP.2006.871726View Article
              17. Hochwald BM, Ten Brink S: Achieving near-capacity on a multiple-antenna channel. IEEE Transactions on Communications 2003,51(3):389-399. 10.1109/TCOMM.2003.809789View Article
              18. de Jong YLC, Willink TJ: Iterative tree search detection for MIMO wireless systems. IEEE Transactions on Communications 2005,53(6):930-935. 10.1109/TCOMM.2005.849638View Article
              19. Kang JW, Lee KB: Simplified ML detection scheme for MIMO systems. Proceedings of the IEEE Vehicular Technology Conference, 2004, Milan, Italy 2: 824-827.
              20. Hassibi B, Vikalo H: On the sphere-decoding algorithm I. expected complexity. IEEE Transactions Signal Processing 2005, 53: 2806-2818. 10.1109/TSP.2005.850352MathSciNetView Article
              21. Vikalo H, Hassibi B: On the sphere-decoding algorithm II. generalizations, second-order statistics, and applications to communications. IEEE Transactions Signal Processing 2005, 53: 2819–-2834. 10.1109/TSP.2005.850350MathSciNetView Article
              22. Garrett DC, Davis LM, Woodward GK: 19.2 Mbit/s 4×4 BLAST/MIMO detector with soft ML outputs. Electronics Letters 2003,39(2):233-235. 10.1049/el:20030125View Article
              23. Burg A, Borgmann M, Wenk M, Zellweger M, Fichtner W, Bölcskei H: VLSI Implementation of MIMO detection using the sphere decoding algorithm. IEEE Journal of Solid-State Circuits 2005,40(7):1566-1576. 10.1109/JSSC.2005.847505View Article
              24. Garrett D, Davis L, Ten Brink S, Hochwald B, Knagge G: Silicon complexity for maximum likelihood MIMO detection using spherical decoding. IEEE Journal of Solid-State Circuits 2004,39(9):1544-1552. 10.1109/JSSC.2004.831454View Article
              25. Garrett D, Woodward GK, Davis L, Nicol C: A 28.8 Mbit/s 4×4 MIMO 3G CDMA receiver for frequency selective channels. IEEE Journal of Solid-State Circuits 2005, 40: 320-3302. 10.1109/JSSC.2004.837931View Article
              26. Guo Z, Nilsson P: Algorithm and implementation of the K -best sphere decoding for MIMO detection. IEEE Journal on Selected Areas in Communications 2006,24(3):491-503. 10.1109/JSAC.2005.862402View Article
              27. Wong K, Tsui C, Cheng RK, Mow W: A VLSI architecture of a K -best lattice decoding algorithm for MIMO channels. Proceedings of the IEEE International Symposium on Circuits and Systems, 2002, Scottsdale, Ariz, USA 3: 273-276.
              28. Schlegel C, Prez L: Trellis and Turbo Coding. Wiley IEEE Press, Piscataway, NJ, USA; 2004.View Article
              29. Jeon WG, Chang KH, Cho YS: Instrumentable tree encoding of information sources. IEEE Transactions on Information Theory 1971,17(1):118-119. 10.1109/TIT.1971.1054572View Article
              30. Anderson J, Mohan S: Source and channel coding: an algorithmic approach. IEEE Transactions on Communications 1984, 32: 169-176. 10.1109/TCOM.1984.1096023View Article
              31. Corporaal H: Microprocessor Architectures: From VLIW to TTA. John Wiley & Sons, New York, NY, USA; 1998.
              32. Corporaal H: Design of transport triggered architectures. Proceedings of the 4th Great Lakes Symposium on (VLSI '94), 1994, Notre Dame, Ind, USA 130-135.
              33. Corporaal H: A different approach to high performance computing. Proceedings of the 4th International Conference on High Performance Computing, 1997, Bangalore, India 22–-27.
              34. Antikainen J, Salmela P, Silvén O, Juntti M, Takala J, Myllylä M: Transport triggered architecture implementation of list sphere detector. Proceedings of the Finnish Signal Processing Symposium, August 2007, Oulu, Finland
              35. Antikainen J, Salmela P, Silvén O, Juntti M, Takala J, Myllylä M: Application-specific instruction set processor implementation of list sphere detector. Proceedings of the 39th Annual Asilomar Conference on Signals, Systems Composition, 2007, Pacific Grove, Calif, USA
              36. 3rd Generation Partnership Project : Group radio access network requirements for evolved UTRA (E-UTRA) and evolved UTRAN (E-UTRAN). In Technical Specification TR 25.913 version 7.3.0 (release 7). 3rd Generation Partnership Project, Valbonne, France; 2006.
              37. Fujita T, Onizawa T, Jiang W, Uchida D, Sugiyama T, Ohta A: A new signal detection scheme combining ZF and K -best algorithms for OFDM/SDM. Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, (PIMRC '04), 2004 4: 2387-2391.
              38. Myllylä M, Silvola P, Juntti M, Cavallaro JR: Comparison of two novel list sphere detector algorithms for MIMOOFDM systems. Proceedings of the IEEE International Symposium Personal, Indoor, Mobile Radio Communications, September 2006, Helsinki, Finland 12-16.
              39. Wenk M, Zellweger M, Burg A, Felber N, Fichtner W: K -best MIMO detection VLSI architectures achieving up to 424 Mbps. Proceedings of the IEEE International Symposium on Circuits and Systems, 2006, Kos, Greece 1151-1154.
              40. Kerttula J: Implementation of a K-best based multiple antenna detector, M.S. thesis. Department of Electrical and Information Engineering, University of Oulu, Oulu, Finland; 2007.
              41. Kerttula J, Myllylä M, Juntti M: Implementation of a K -best based MIMO-OFDM detector algorithm. Proceedings of the European Signal Proccessing Conference, 2007, Poznán, Poland
              42. Janhunen J: Signal processor implementation of list sphere detection, M.S. thesis. Department of Electrical and Information Engineering, University of Oulu, Oulu, Finland; 2007.
              43. Janhunen J, Silvèn O, Myllylä M, Juntti M: A DSP implementation of a K -best list sphere detector algorithm. Proceedings of the Finnish Signal Processing Symposium, 2007, Oulu, Finland 6.
              44. Wiesel A, Mestre X, Pags A, Fonollosa JR: Efficient implementation of sphere demodulation. Proceedings of the IEEE Workshop on Signal Processing Advances in Wireless Communications, June 2003, Rome, Italy 36-40.
              45. Widdup B, Woodward G, Knagge G: A highlyparallel VLSI architecture for a list sphere detector. Proceedings of the IEEE International Conference on Communications, June 2004, Paris, France 2720–-2725.
              46. Salmela P, Antikainen J, Silvén O, Takala J: Memory-based list updating for list sphere decoders. Proceedings of the IEEE Workshop on Signal Processing Systems (SiPS '07), 2007, Shanghai, China 633–-638.

              Copyright

              © Juho Antikainen et al. 2007

              This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.