For the scenarios with high throughput requirements, the belief propagation (BP) decoding is one of the most promising decoding strategies for polar codes. By pruning the redundant variable nodes (VNs) and check nodes (CNs) in the original factor graph, the graph is condensed to a sparse bipartite graph that is similar to the graph for low-density parity-check (LDPC) codes. In this paper, we introduce the bit flipping scheme into the LDPC-like BP (L-BP) decoding and propose two methods to identify the error-prone VNs. By additional decoding attempts, the L-BP flip (L-BPF) decoding improves the error-correction performance with a similar average complexity for high Eb/N0 values. The simulation results show that the L-BPF decoding achieves 0.25 dB gain compared with the L-BP decoding. Finally, a parallel decoder with the proposed L-BPF algorithm for an (256,128) polar code is implemented using 65nm CMOS technology, and it delivers a throughput of 1877.3 Mbps.
为了满足供应链物流的不同需求,考虑多种车型、车辆容量、车辆油耗、车辆最大配送距离等约束条件,以最小油耗、最短配送距离为目标,建立多车型供应链物流运输调度模型(Multi-Type Vehicle Routing Problem in Supply Chain,MTVRPSC),并提出一种混沌烟花算法求解该模型。该算法以烟花算法为核心,提出一种编解码策略实现连续空间到MTVRPSC离散空间的映射,重新定义算法的适应度函数、适应度值和适应度的比较方法,并采用混沌初始化策略和混沌搜索策略来增强算法收敛效果。实验结果表明,所提出的算法在求解MTVRPSC时具有较强的寻优能力和稳定性。 相似文献