基于辅助信息与人工智能的6G系统自适应码本选择
Adaptive Selection of Codebook Using Assistance Information and Artificial Intelligence for 6G Systems
AI总结：
本文提出基于辅助信息与人工智能的6G系统自适应码本选择方法，通过神经网络预测不同码本类型的数据量化精度，以降低CSI开销并保持系统性能。
摘要：
本文针对下行链路（DL）预编码量化中的自适应码本（CB）选择问题，该问题涉及信道状态信息（CSI）报告。预编码量化精度取决于传播条件，需要为每个用户设备（UE）独立参数适应。为实现最优CB选择，本文在基站（BS）端提出利用UE报告的跨时域、频域和空域的统计信道特性进行CB选择。报告的辅助信息作为神经网络（NN）的输入，预测各种CB类型对每个服务用户的数据量化精度。预测的精度随后用于选择最优CB，同时考虑相关的CSI报告开销和预编码性能。系统级仿真显示，所提出的方法在保持目标系统吞吐量性能的同时，减少了总CSI开销。
英文摘要：
This paper addresses the problem of adaptive codebook (CB) selection for downlink (DL) precoder quantization in channel state information (CSI) reporting. The accuracy of precoder quantization depends on propagation conditions, requiring independent parameter adaptation for each user equipment (UE). To enable optimal CB selection, this paper proposes UE-assisted CB selection at the base station (BS) using reported by the UE statistical channel properties across time, frequency, and spatial domains. The reported assistance information serves as input to a neural network (NN), which predicts the quantization accuracy of various CB types for each served user. The predicted accuracy is then used to select the optimal CB while considering the associated CSI reporting overhead and precoding performance. System-level simulations demonstrate that the proposed approach reduces total CSI overhead while maintaining the target system throughput performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
