计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 632-638.doi: 10.11896/jsjkx.210800036
卿朝进, 杜艳红, 叶青, 杨娜, 张岷涛
QING Chao-jin, DU Yan-hong, YE Qing, YANG Na, ZHANG Min-tao
摘要： 在大规模多输入多输出(Massive-Multiple Input and Multiple-Output,mMIMO)系统中,叠加信道状态信息(Channel State Information,CSI)反馈可避免上行带宽资源占用,但叠加干扰会造成接收机计算复杂度高、反馈精度低等问题,且均未考虑存在CSI估计错误的实际应用场景。为此,针对存在CSI估计错误场景下的叠加CSI反馈,在改进极限学习机(Extreme Learning Machine,ELM)的基础上,提出基于增强型ELM的叠加CSI反馈方法。首先,基站对接收信号进行预均衡处理,初步消除上行信道干扰;然后对传统叠加CSI反馈进行迭代展开,构建增强型ELM网络,通过规范化各个ELM网络的隐藏层输出来增强网络学习数据分布的能力,从而改善恢复下行CSI和上行用户数据序列(Uplink User Data Sequence,UL-US)的精确性。仿真实验表明,与经典和时新的叠加CSI反馈方法相比,所提方法能够获得相似或更好的下行CSI和上行用户数据的恢复精确性;同时,针对不同的参数影响,性能改善具有鲁棒性。
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