In 5G new radio(NR), polar codes are adopted for e MBB downlink control channels where the blind detection is employed in user equipment(UE) to identify the correct downlink control information(DCI). However, differen...In 5G new radio(NR), polar codes are adopted for e MBB downlink control channels where the blind detection is employed in user equipment(UE) to identify the correct downlink control information(DCI). However, different from that in the 4G LTE system, the cyclic redundancy check(CRC) in polar decoding plays both error correction and error detection roles. Consequently, the false alarm rates(FAR) may not meet the system requirements(FAR<1.52 × 10^(−5)). In this paper, to mitigate the FAR in polar code blind detection, we attach a binary classifier after the polar decoder to further remove the false alarm results and meanwhile retain the correct DCI. This classifier works by tracking the squared Euclidean distance ratio(SEDR) between the received signal and hypothesis. We derive an analytical method to fast compute proper classification threshold that is implementation-friendly in practical use. Combining the well-designed classifier, we show that some very short CRC sequences can even be used to meet the FAR requirements. This consequently reduces the CRC overhead and contributes to the system error performance improvements.展开更多
In recent years,various types of surrogate optimization models have been proposed to reduce the computational time and to improve the emulation accuracy.In this study,by leveraging an ANN surrogate model developed ear...In recent years,various types of surrogate optimization models have been proposed to reduce the computational time and to improve the emulation accuracy.In this study,by leveraging an ANN surrogate model developed earlier,a comprehensive and efficient optimization algorithm is conceived for the global optimal design of an integrated regenerative methanol transcritical cycle.It combines a unique converging/diverging classifier model into the surrogate model to form a surrogate-based model,which significantly improves the prediction accuracy of the objective function.Six binary classifiers are explored and the multi-layer feed-forward(MLF)neural network classifier is selected.In addition,within the five global optimizers being explored,the basinhopping(BH)and dual-annealing(DA)are selected.The optimal surrogate-based model and global optimizers are then combined to form a unique surrogate-optimizer model.The surrogate-optimizer model is slightly outperformed by the physics-based model in terms of the optimization results,the time consumption of the surrogate-optimizer model during the optimization searching process is 99%less than that of the physicsbased model.As the results,the surrogate-optimizer model is slightly outperformed by the physics-based model in terms of the optimization results,where the Levelized Cost of Energy(LCOE)of the Surrogate-DA and Surrogate-BH models are 77.912 and 78.876$/MWh,respectively,compared to the 77.190$/MWh of the Baseline model with fairly close penalties between them.In the meantime,the time consumption of the surrogate-optimizer model during the optimization searching process is 99%less than that of the physics-based model.展开更多
基金supported in part by National Natural Science Foundation of China(No.62471054)in part by National Natural Science Foundation of China(No.92467301)+3 种基金in part by the National Natural Science Foundation of China(No.62201562)in part by the National Natural Science Foundation of China(No.62371063)in part by the National Natural Science Foundation of China(No.62321001)in part by Liaoning Provincial Natural Science Foundation of China(No.2024–BSBA–51).
文摘In 5G new radio(NR), polar codes are adopted for e MBB downlink control channels where the blind detection is employed in user equipment(UE) to identify the correct downlink control information(DCI). However, different from that in the 4G LTE system, the cyclic redundancy check(CRC) in polar decoding plays both error correction and error detection roles. Consequently, the false alarm rates(FAR) may not meet the system requirements(FAR<1.52 × 10^(−5)). In this paper, to mitigate the FAR in polar code blind detection, we attach a binary classifier after the polar decoder to further remove the false alarm results and meanwhile retain the correct DCI. This classifier works by tracking the squared Euclidean distance ratio(SEDR) between the received signal and hypothesis. We derive an analytical method to fast compute proper classification threshold that is implementation-friendly in practical use. Combining the well-designed classifier, we show that some very short CRC sequences can even be used to meet the FAR requirements. This consequently reduces the CRC overhead and contributes to the system error performance improvements.
基金financial support provided for the study,and Nuclear Regulatory Commission(NRC)for its financial support through the Award No.31310019M0014.
文摘In recent years,various types of surrogate optimization models have been proposed to reduce the computational time and to improve the emulation accuracy.In this study,by leveraging an ANN surrogate model developed earlier,a comprehensive and efficient optimization algorithm is conceived for the global optimal design of an integrated regenerative methanol transcritical cycle.It combines a unique converging/diverging classifier model into the surrogate model to form a surrogate-based model,which significantly improves the prediction accuracy of the objective function.Six binary classifiers are explored and the multi-layer feed-forward(MLF)neural network classifier is selected.In addition,within the five global optimizers being explored,the basinhopping(BH)and dual-annealing(DA)are selected.The optimal surrogate-based model and global optimizers are then combined to form a unique surrogate-optimizer model.The surrogate-optimizer model is slightly outperformed by the physics-based model in terms of the optimization results,the time consumption of the surrogate-optimizer model during the optimization searching process is 99%less than that of the physicsbased model.As the results,the surrogate-optimizer model is slightly outperformed by the physics-based model in terms of the optimization results,where the Levelized Cost of Energy(LCOE)of the Surrogate-DA and Surrogate-BH models are 77.912 and 78.876$/MWh,respectively,compared to the 77.190$/MWh of the Baseline model with fairly close penalties between them.In the meantime,the time consumption of the surrogate-optimizer model during the optimization searching process is 99%less than that of the physics-based model.
