先验辅助掩码向量量化用于FDD大规模MIMO系统的CSI反馈

中文摘要 AI 辅助

下行信道状态信息（CSI）反馈是频分双工（FDD）大规模MIMO系统中的关键瓶颈，因为用户设备（UE）必须通过有限的上行链路（UL）预算将其估计的信道传达给基站（BS）。为了在严格的反馈约束下提高CSI重建精度，我们提出了先验辅助掩码向量量化（PM-VQ），一种基于学习的分离式信源-信道编码（SSCC）反馈方案，其条件为平均角度-时延功率图——一种在UE和BS处均可获得的信道二阶统计量的紧凑表示。在PM-VQ中，先验辅助编码器将CSI映射为潜在令牌，空间自适应掩码模块（SAMM）在反馈预算内对信息量最大的令牌进行评分和选择。选中的令牌经过向量量化后连同其位置一起反馈，而自适应去掩码模块（ADM）在BS处完成潜在表示，然后进行先验条件解码。为支持可变速率压缩，单个模型在选中的令牌数量范围内进行训练，从而无需重新训练即可在多个反馈维度上运行。我们在Sionna生成的3GPP TR 38.901 UMa数据集上，将PM-VQ与三个代表性基线进行评估，重点关注最具挑战性的漫射区域，其中信道能量分布在许多角度-时延系数上。仿真结果表明，在该区域内，PM-VQ在所有测试的SNR水平和反馈维度上均实现了最低的NMSE。此外，即使仅从少量信道实现中估计，角度-时延功率图先验仍然是有益的。

英文摘要

Downlink channel state information (CSI) feedback is a key bottleneck in frequency-division duplex (FDD) massive MIMO systems, as the user equipment (UE) must convey its estimated channel to the base station (BS) over a limited uplink (UL) budget. To improve CSI reconstruction accuracy under tight feedback constraints, we propose prior-aided masked vector quantization (PM-VQ), a learning-based separate source--channel coding (SSCC) feedback scheme conditioned on the average angle--delay power map---a compact representation of the channel second-order statistics available at both the UE and the BS. In PM-VQ, a prior-aided encoder maps the CSI to latent tokens, and a spatially-adaptive masking module (SAMM) scores and selects the most informative tokens within the feedback budget. The selected tokens are vector-quantized and fed back together with their positions, while an adaptive de-masking module (ADM) completes the latent representation at the BS before prior-conditioned decoding. To support variable-rate compression, a single model is trained over a range of selected-token counts, enabling operation across multiple feedback dimensions without retraining. We evaluate PM-VQ against three representative baselines on a Sionna-generated 3GPP TR~38.901 UMa dataset, focusing on the most challenging diffuse regime where channel energy is spread across many angle--delay coefficients. Simulation results show that PM-VQ achieves the lowest NMSE across all tested SNR levels and feedback dimensions in this regime. Moreover, the angle--delay power-map prior remains beneficial even when estimated from only a few channel realizations.

发表机构

• Technische Universität Berlin（柏林工业大学）
• Huawei Technologies Co., Ltd.（华为技术有限公司）

机构由 AI 辅助整理，请以论文原文为准。