Design and Implementation of a Low Complexity VLSITurbo-Code Decoder Architecture for LowEnergy Mobile Wireless Communications

  • Authors:
  • Sangjin Hong;Wayne E. Stark

  • Affiliations:
  • Wireless Communications Research Laboratory, Department of Electrical Engineering and Computer Science, 1301 Beal Avenue, University of Michigan, Ann Arbor MI 48109-2122;Wireless Communications Research Laboratory, Department of Electrical Engineering and Computer Science, 1301 Beal Avenue, University of Michigan, Ann Arbor MI 48109-2122

  • Venue:
  • Journal of VLSI Signal Processing Systems - Special issue on recent advances in the design and implementation of signal processing systems
  • Year:
  • 2000

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Abstract

Channel coding is commonly incorporated to obtainsufficient reception quality in wireless mobile communicationstransceiver to counter channel degradation due to intersymbolinterference, multipath dispersion, and thermal noise induced byelectronic circuit devices. For low energy mobile wirelesscommunications, it is highly desirable to incorporate a decoder whichhas a very low power consumption while achieving a high coding gain.In this paper, a sub-optimal low-complexity multi-stage pipelinedecoder architecture for a powerful channel coding technique known as“turbo-code” is presented. The presentedarchitecture avoids complexoperations such as exponent and logarithmic computations. Theturbo-code decoding algorithm is reformulated for an efficient VLSIimplementation. Furthermore, the communication channel statisticestimation process has been completely eliminated. The architecturehas been designed and implemented with the 0.6 μm CMOS standardcell technology using Epoch computer aided design tool. Theperformance and the circuit complexity of the turbo-code decoder areevaluated and compared with the other types of well-knowndecoders. The power consumption of the low-complexity turbo-codedecoder is comparable to that of the conventional convolutional-codedecoder. However, the low-complexity turbo-code decoder has asignificant coding gain over the conventional convolutional-codedecoders and it is well suited for very low power applications.