移动性与反馈感知的多级冲突触发混合波束成形用于多用户毫米波无人机系统
Mobility- and Feedback-Aware Multi-Level Conflict-Triggered Hybrid Beamforming for Multi-User mmWave UAV Systems
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中文总结 AI 辅助
针对毫米波无人机下行链路中信道老化与反馈延迟问题，提出移动性与反馈感知的多级冲突触发混合波束成形策略MLR-TG，通过预测净效用排序和自适应细化级别，显著降低中断概率并提升用户速率，同时减少候选评估与反馈开销。
中文摘要 AI 辅助
本文研究了在多用户大规模多输入多输出毫米波无人机（UAV）下行链路系统中，在移动性引起的信道老化和延迟波束训练反馈条件下的混合波束成形问题。从紧凑的延迟报告中进行模拟波束选择是一个部分观测决策，而额外的候选评估会消耗处理时间并减少有效载荷间隔。我们提出了一种移动性与反馈感知的多级细化策略，称为MLR-TG，以提高鲁棒性而无需始终开启的候选搜索。候选子集根据由报告码本的量化复系数和无人机移动状态构建的预测净效用进行排序，而传输正则化迫零预编码器在模拟选择后根据导频估计的有效信道状态信息（CSI）计算一次。细化级别根据冲突严重性和老化敏感性自适应选择。选择规则是预测效用最大化的双统计量近似，采用系统规模不变的冲突评分，并在与评估不相交的训练数据上进行离线校准。在具有无人机姿态动力学和常见信道轨迹的三维空对地模型上的仿真表明，与贪婪扇区波束成形相比，MLR-TG将系统中断概率降低了26.7%，并将第5百分位用户速率提高了53.9%，而净频谱效率保持在0.96%以内。与始终开启的全局前3细化相比，MLR-TG在评估候选数减少77.9%的情况下将净频谱效率提高了5.5%，并且在净频谱效率上保持在非因果CSI水平理想值的3.4%以内，同时比全CSI报告所需的反馈比特数减少86.9%。
英文摘要
This paper investigates hybrid beamforming for multi-user large multiple-input multiple-output millimeter-wave unmanned aerial vehicle (UAV) downlink systems under mobility-induced channel aging and delayed beam-training feedback. Analog beam selection from compact delayed reports is a partial-observation decision, while additional candidate evaluations consume processing time and reduce the useful payload interval. We propose a mobility- and feedback-aware multi-level refinement strategy, termed MLR-TG, to improve robustness without always-on candidate search. Candidate subsets are ranked by a predicted net utility constructed from quantized complex coefficients of the reported codewords and the UAV mobility state, while the transmission regularized zero-forcing precoder is computed once from pilot-estimated effective channel state information (CSI) after analog selection. The refinement level is adaptively selected according to conflict severity and aging sensitivity. The selection rule is a two-statistic approximation of predicted-utility maximization, employs a system-size-invariant conflict score, and is calibrated offline on training data disjoint from evaluation. Simulations on a three-dimensional air-to-ground model with UAV attitude dynamics and common channel trajectories show that MLR-TG reduces system outage probability by 26.7% and improves the 5th-percentile user rate by 53.9% relative to greedy sector beamforming, while net spectral efficiency remains within 0.96%. Compared with always-on global top-3 refinement, MLR-TG improves net spectral efficiency by 5.5% while evaluating 77.9% fewer candidates, and remains within 3.4% of a noncausal-CSI level oracle in net spectral efficiency while requiring 86.9% fewer feedback bits than full-CSI reporting.
发表机构
- Phenikaa University（Phenikaa大学）
- Henan Polytechnic University（河南理工大学）
- Van Lang University（文朗大学）
机构由 AI 辅助整理，请以论文原文为准。
