Abstract
Sequential decoding can achieve a very low computational complexity and short decoding delay when the signal-to-noise ratio (SNR) is relatively high. In this article, a low-complexity high-throughput decoding architecture based on a sequential decoding algorithm is proposed for convolutional codes. Parallel Fano decoders are scheduled to the codewords in parallel input buffers according to buffer occupancy, so that the processing capabilities of the Fano decoders can be fully utilized, resulting in high decoding throughput. A discrete time Markov chain (DTMC) model is proposed to analyze the decoding architecture. The relationship between the input data rate, the clock speed of the decoder and the input buffer size can be easily established via the DTMC model. Different scheduling schemes and decoding modes are proposed and compared. The novel high-throughput decoding architecture is shown to incur 3-10% of the computational complexity of Viterbi decoding at a relatively high SNR.
Highlights
The 57-64 GHz unlicensed bandwidth around 60 GHz can accommodate multi-gigabits per second wireless transmission in a short range
A novel architecture based on parallel Fano algorithm decoding with scheduling was proposed
Due to the scheduling of the Fano decoders according to the input buffer occupancy, a high decoding throughput can be achieved by the proposed architecture
Summary
The 57-64 GHz unlicensed bandwidth around 60 GHz can accommodate multi-gigabits per second (multiGbps) wireless transmission in a short range. A novel low-complexity high-throughput decoding architecture based on parallel Fano algorithm decoding with scheduling is proposed. It will be shown that the high-throughput decoding architecture can achieve a much lower computational complexity compared to the Viterbi decoding with a similar error rate performance. By increasing the number of merged states (NMS), the probability that the FD and the BD to decode on the same path can be increased, resulting in an improved error rate performance. This is at the cost of higher computational effort. The BFA can achieve a lower computational complexity and variability compared to the UFA, which is more pronounced at a lower SNR
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