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ERGODIC SPECTRUM MANAGEMENT SYSTEMS AND METHODS

机译:人体光谱管理系统和方法

摘要

Presented are Ergodic Spectrum Management (ESM) systems and methods that take advantage of the presence of statistical consistencies (“ergodicity”) and correlations, such as a wireless network's average dimensional consistencies of probability distributions (in time, space, and frequency) of channel gains, to adaptively learn qualitative and quantitative network/user behavior; estimate or predict network performance; and guide locally implemented radio resource management (RRM) decisions of wireless multi-user transmissions in a manner such as to reduce interference and improve latency; connection stability; efficiency; and overall wireless performance. ESM also enhances end-users' Quality of Experience (QoE) by allowing movement across bands and regions as users/devices roam. A remote-cloud-based resource management implementation of ESM's Learn-ed Resource Managers (LRMs) removes the need for heavy edge-computing close to radio cells.
机译:提出了利用统计一致性(“遍历性”)和相关性(例如无线网络的信道概率分布(在时间,空间和频率上)的平均维数一致性)的遍历频谱管理(ESM)系统和方法。收获,以适应性地学习定性和定量的网络/用户行为;估计或预测网络性能;以减少干扰和改善等待时间的方式指导无线多用户传输的本地实施的无线电资源管理(RRM)决策;连接稳定性;效率;和整体无线性能。 ESM还允许用户/设备漫游时跨频段和区域移动,从而提高了最终用户的体验质量(QoE)。 ESM的学习型资源管理器(LRM)的基于远程云的资源管理实现消除了对无线电小区附近进行繁琐的边缘计算的需求。

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