可移动天线系统在延迟中断约束下的跨层分析与优化
Cross-Layer Analysis and Optimization for Movable Antenna Systems with Delay-Outage Constraints
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中文总结 AI 辅助
针对MA增强MU-MIMO下行链路中的延迟中断问题，提出跨层框架与双时间尺度协议，利用鞅转化约束，通过AO算法联合优化天线位置与波束成形，平衡空间重构与时间服务损失。
中文摘要 AI 辅助
可移动天线（MA）已成为增强无线通信性能的一项有前景的技术。然而，在延迟敏感系统中，MA的移动会带来不可忽略的延迟，这可能降低突发流量下的服务过程。本文研究了MA增强的多用户多输入多输出（MU-MIMO）下行链路系统中的延迟中断问题。与现有主要关注物理层性能优化的研究不同，本文开发了一个跨层框架，该框架考虑了天线移动带来的总体延迟，以及媒体访问控制（MAC）层的服务中断和队列积累。考虑到天线移动和数据包级队列演化的不同时间尺度，我们建立了一个双时间尺度传输协议和一个具有马尔可夫调制突发到达的跨层排队模型。随后，我们制定了一个长期发射功率最小化问题，以在延迟中断和实际MA移动约束下联合优化目标天线位置和发射波束成形。为了处理概率性延迟中断约束，我们构建了一个积压鞅，以推导出关于服务率的确定性充分条件，从而将概率约束转化为确定性服务率要求。进一步开发了一种基于短视界近似的交替优化（AO）算法，以获得高质量的次优解。数值结果表明，所提出的算法通过有效平衡MA的空间重构能力和时间服务损失，优于基线方案。
英文摘要
Movable antennas (MAs) have emerged as a promising technology for enhancing wireless communication performance. However, in delay-sensitive systems, MA movement incurs non-negligible delay, which may degrade the service process for bursty traffic. This paper investigates a delay-outage problem in an MA-enhanced multiuser multiple-input multiple-output (MU-MIMO) downlink system. Unlike existing studies that mainly focus on physical-layer performance optimization, a cross-layer framework is developed that accounts for the overall delay due to antenna movement, along with service interruptions and queue accumulation at the medium access control (MAC) layer. Considering the different timescales of antenna movement and packet-level queue evolution, we establish a two-timescale transmission protocol and a cross-layer queueing model with Markov-modulated bursty arrivals. A long-term transmit-power minimization problem is then formulated to jointly optimize the destination antenna positions and transmit beamforming under delay-outage and practical MA movement constraints. To tackle the probabilistic delay-outage constraints, a backlog martingale is constructed to derive a deterministic sufficient condition in terms of service rate, thereby transforming the probabilistic constraints into deterministic service rate requirements. An alternating optimization (AO) algorithm based on a short-horizon approximation is further developed to obtain a high-quality suboptimal solution. Numerical results demonstrate that the proposed algorithm outperforms baseline schemes by effectively balancing the MAs' spatial reconfiguration capability and the temporal service loss.
发表机构
- University of Electronic Science and Technology of China（电子科技大学）
- Tiangong University（天津工业大学）
- National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。
