Area-Efficient Scalable MAP Processor Design for High-Throughput Multistandard Convolutional Turbo Decoding

Most of advanced wireless standards, such as WiMAX and LTE, have adopted different convolutional turbo code (CTC) schemes with various block sizes and throughput rates. Thus, a reconfigurable and scalable hardware accelerator for multistandard CTC decoding is necessary. In this paper, we propose scalable maximum a posteriori algorithm (MAP) processor designs which can support both single-binary (SB) and double-binary (DB) CTC decoding, and handle arbitrary block sizes for high throughput CTC decoding. We first propose three combinations of parallel-window (PW) and hybrid-window (HW) MAP decoding. Moreover, the computational modules and storages of the dual-mode (SB/DB) MAP decoding are designed to achieve a high area utilization. To verify the proposed approaches, a 1.28 mm
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dual-mode 2PW-1HW MAP processor is implemented in 0.13 μ m CMOS process. The prototyping chip achieves a maximum throughput rate of 500 Mb/s at 125 MHz with an energy efficiency of 0.19 nJ/bit and an area efficiency of 3.13 bits/mm
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. For the multistandard systems, the expected throughput rates of the WiMAX and LTE CTC schemes is achieved by using five dual-mode 2PW-1HW MAP processors.