| 摘要: |
| 在第五代移动通信系统中，采用极化码编码下行控制信息，并通过物理下行控制信道（Physical Download Control Channel，PDCCH）发送给用户设备，用户设备通过极化码盲检获得属于自己的控制信息。在通常情况下，用户设备接收到的帧包括极化码字帧和噪声帧，若将其全部进行译码，则存在不必要的开销。针对此问题，提出了一种基于阈值检验的区分极化码字帧和噪声帧的方法。该方法根据收端接收到的极化码字各个节点呈现出不同的硬判可靠度，为各个节点设置不同的硬判错误率阈值，对所有帧按照未通过阈值检验的节点个数进行由小到大进行排序，选取较小的帧进入后续的译码盲检，从而在盲检前剔除一定数量的噪声帧，显著降低用户设备端的盲检译码能耗。仿真验证表明，在复杂度相同的情况下，与已有的区分算法相比，该方案能将漏检率降低90%以上。 |
| 关键词: 5G 极化码 物理下行控制信道(PDCCH) 盲检 |
| DOI：10.20079/j.issn.1001-893x.230425001 |
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| 基金项目:国家重点研发计划（2018YFB1801403） |
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| A Method for Distinguishing Polarized PDCCH Frames from Noise Frames |
| WANG Yiru,GUAN Pengxin,WANG Xiayi,ZHAO Yuping |
| (School of Electronics,Peking University,Beijing 100871,China) |
| Abstract: |
| In 5G systems,the polar code is used to encode the downlink control information,which is sent to the user equipment through the physical download control channel(PDCCH).The user equipment obtains its own control information through polar code blind detection.In general,the frames received by the user equipment include polarized codeword frames and noise frames,and if all of them are decoded,there will be unnecessary overhead.To solve this problem,the authors propose a method based on threshold inspection to distinguish polarized codeword frames from noise frames.The method sets different hard decision reliability thresholds for each node according to the polar code words received at the receiving end，then sorts the number of nodes that fail the threshold test in ascending order for all frames.Frames with lower failure counts are selected for subsequent blind decoding,thereby eliminating a certain number of noisy frames before blind decoding,significantly reducing the blind decoding energy consumption at the user equipment end.The proposed scheme is simulated and verified by building a simulation platform.The results show that,compared with the existing discrimination algorithms,this scheme can reduce the missed detection rate by more than 90% under the same complexity. |
| Key words: 5G polar code physical download control channel(PDCCH) blind detection |
