A High Throughput Low Power Soft-Output Viterbi Decoder

University essay from KTH/Skolan för informations- och kommunikationsteknik (ICT)

Abstract: A high-throughput low-power Soft-Output Viterbi decoder designed for the convolutional codes used in the ECMA-368 UWB standard is presented in this thesis. The ultra wide band (UWB) wireless communication technology is supposed to be used in physical layer of the wireless personal area network (WPAN) and next generation Blue Tooth. MB-OFDM is a very popular scheme to implement the UWB system and is adopted as the ECMA-368 standard. To make the high speed data transferred over the channel reappear reliably at the receiver, the error correcting codes (ECC) are wildly utilized in modern communication systems. The ECMA-368 standard uses concatenated convolutional codes and Reed-Solomon (RS) codes to encode the PLCP header and only convolutional codes to encode the PPDU Payload. The Viterbi algorithm (VA) is a popular method of decoding convolutional codes for its fairly low hardware implementation complexity and relatively good performance. Soft-Output Viterbi Algorithm (SOVA) proposed by J. Hagenauer in 1989 is a modified Viterbi Algorithm. A SOVA decoder can not only take in soft quantized samples but also provide soft outputs by estimating the reliability of the individual symbol decisions. These reliabilities can be provided to the subsequent decoder to improve the decoding performance of the concatenated decoder. The SOVA decoder is designed to decode the convolutional codes defined in the ECMA-368 standard. Its code rate and constraint length is R=1/3 and K=7 respectively. Additional code rates derived from the "mother" rate R=1/3 codes by employing "puncturing", including 1/2, 3/4, 5/8, can also be decoded. To speed up the add-compare-select unit (ACSU), which is always the speed bottleneck of the decoder, the modified CSA structure proposed by E.Yeo is adopted to replace the conventional ACS structure. Besides, the seven-level quantization instead of the traditional eight-level quantization is proposed to be used is in this decoder to speed up the ACSU in further and reduce its hardware implementation overhead. In the SOVA decoder, the delay line storing the path metric difference of every state contains the major portion of the overall required memory. A novel hybrid survivor path management architecture using the modified trace-forward method is proposed. It can reduce the overall required memory and achieve high throughput without consuming much power. In this thesis, we also give the way to optimize the other modules of the SOVA decoder. For example, the first K-1 necessary stages in the Path Comparison Unit (PCU) and Reliability Measurement Unit (RMU) are IX removed without affecting the decoding results. The attractiveness of SOVA decoder enables us to find a way to deliver its soft output to the RS decoder. We have to convert bit reliability into symbol reliability because the soft output of SOVA decoder is the bit-oriented while the reliability per byte is required by the RS decoder. But no optimum transformation strategy exists because the SOVA output is correlated. This thesis compare two kinds of the sub-optimum transformation strategy and proposes an easy to implement scheme to concatenate the SOVA decoder and RS decoder under various kinds of convolutional code rates. Simulation results show that, using this scheme, the concatenated SOVA-RS decoder can achieve about 0.35dB decoding performance gain compared to the conventional Viterbi-RS decoder.

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