[论文解读] Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams that Adapt to Deployment and Hardware

Muhammad Alrabeiah, Yu Zhang+1 more|arXiv (Cornell University)|Jun 25, 2020

Millimeter-Wave Propagation and Modeling被引用 7

一句话总结

本文提出了一种新颖的在线机器学习框架，通过使用复值神经网络设计环境与硬件感知的毫米波大规模MIMO波束码本。该模型在硬件约束（恒包络、量化相位偏移）下，通过自监督训练直接学习模拟波束成形权重，无需显式信道状态信息，显著降低了波束训练开销，提升了对硬件损伤的鲁棒性，同时增强了频谱效率。

ABSTRACT

Millimeter wave (mmWave) and massive MIMO systems are intrinsic components of 5G and beyond. These systems rely on using beamforming codebooks for both initial access and data transmission. Current beam codebooks, however, generally consist of a large number of narrow beams that scan all possible directions, even if these directions are never used. This leads to very large training overhead. Further, these codebooks do not normally account for the hardware impairments or the possible non-uniform array geometries, and their calibration is an expensive process. To overcome these limitations, this paper develops an efficient online machine learning framework that learns how to adapt the codebook beam patterns to the specific deployment, surrounding environment, user distribution, and hardware characteristics. This is done by designing a novel complex-valued neural network architecture in which the neuron weights directly model the beamforming weights of the analog phase shifters, accounting for the key hardware constraints such as the constant-modulus and quantized-angles. This model learns the codebook beams through online and self-supervised training avoiding the need for explicit channel state information. This respects the practical situations where the channel is either unavailable, imperfect, or hard to obtain, especially in the presence of hardware impairments. Simulation results highlight the capability of the proposed solution in learning environment and hardware aware beam codebooks, which can significantly reduce the training overhead, enhance the achievable data rates, and improve the robustness against possible hardware impairments.

研究动机与目标

• 解决传统毫米波波束码本因扫描所有可能方向而导致的高波束训练开销问题。
• 克服固定、单瓣码本无法适应用户分布或非均匀阵列几何结构的局限性。
• 在码本设计中考虑相位失配和量化相位移器等硬件损伤的影响。
• 通过实现自监督、在线学习波束波形，消除对显式信道状态信息的依赖。
• 开发一种可训练、受硬件约束的波束码本，能够动态适应特定部署条件。

提出的方法

• 设计了一种复值神经网络架构，其中神经元权重直接表示满足恒包络和量化相位约束的模拟波束成形权重。
• 采用自监督目标进行网络训练，利用接收信号强度和波束波形一致性，避免对真实信道状态信息的依赖。
• 通过波束成形过程进行反向传播，梯度通过考虑模拟相位移器物理约束的结构化雅可比矩阵计算得出。
• 参数更新规则（公式24）支持基于环境实时反馈的波束码本波束在线自适应。
• 该框架集成了一种可微分波束成形模型，将可训练相位移器映射为波束波形，实现最优码本波束的端到端学习。
• 该架构支持在线与自监督学习，使其在信道估计不完整或不可用的实际部署中更具实用性。

实验结果

研究问题

• RQ1 神经网络能否在无需显式信道状态信息的情况下，学习生成适应特定部署环境与用户分布的波束码本波形？
• RQ2 如何在毫米波大规模MIMO系统中设计波束码本，以考虑恒包络与量化相位偏移等硬件约束？
• RQ3 与传统全扫描码本相比，自监督学习在多大程度上可减少波束训练开销？
• RQ4 所提出方法在应对相位失配与阵列非均匀性等硬件损伤方面，如何提升鲁棒性？
• RQ5 所学习的码本能否在频谱效率与训练开销方面优于传统DFT或分层码本？

主要发现

• 所提方法通过仅学习当前部署下相关的波束，避免全角度扫描，显著降低了波束训练开销。
• 与传统DFT码本相比，该模型通过根据用户分布与环境自适应波束波形，实现了更高的频谱效率。
• 即使信道状态信息不完整或不可用，自监督训练框架仍能实现有效的码本学习。
• 通过端到端训练，该架构在相位失配与非均匀阵列几何结构方面表现出更强的鲁棒性。
• 仿真结果证实，所学习的码本在可实现数据速率与训练效率方面均优于标准码本。
• 该框架成功学习了环境感知的波束波形，不再局限于单瓣波束，从而在非视 Line-of-Sight 场景中实现更优性能。