基于FFR的ICIC技术中应用Q-Learning解决热点场景干扰问题
Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
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中文总结 AI 辅助
针对热点场景中的干扰问题，提出基于Q-Learning动态调整FFR-ICIC参数的方法，以提升用户SINR，实验表明热点用户SINR最高提升180%。
中文摘要 AI 辅助
这项工作探索了基于分数频率复用（FFR）的干扰协调技术（小区间干扰协调，ICIC），作为用户集中度随时间变化的多小区场景的解决方案。首先，我们提出了高用户集中度的问题及其后果。接下来，讨论了多输入多输出（MIMO）和小小区作为该问题的经典解决方案，进而引入了分数频率复用以及使用FFR的现有ICIC技术。为了展示ICIC技术在减少同信道干扰方面的有效性，并比较不同技术，我们进行了探索性分析。利用第一次分析中的一种技术进行了统计研究，以确定其哪些参数与系统性能相关。此外，另一项研究强调了高用户集中度在所提出场景中的影响。由于系统的动态特性，这项工作提出了一种基于机器学习的解决方案。该方案包括自动更改ICIC参数，以在随时间出现热点的场景中维持最佳的信干噪比（SINR）。所有研究均基于ns-3模拟器原型。结果表明，与没有Q-Learning的场景相比，所提出的Q-Learning算法提高了所有用户和热点用户的平均SINR。在最佳情况下，热点用户的SINR提高了180%，在最差情况下提高了11.2%。
英文摘要
This work explores interference coordination techniques (inter-cell interference coordination, ICIC) based on fractional frequency reuse (FFR) as a solution for a multi-cellular scenario with user concentration varying over time. Initially, we present the problem of high user concentration along with their consequences. Next, the use of multiple-input multiple-output (MIMO) and small cells are discussed as classic solutions to the problem, leading to the introduction of fractional frequency reuse and existing ICIC techniques that use FFR. An exploratory analysis is presented in order to demonstrate the effectiveness of ICIC techniques in reducing co-channel interference, as well as to compare different techniques. A statistical study was conducted using one of the techniques from the first analysis in order to identify which of its parameters are relevant to the system performance. Additionally, another study is presented to highlight the impact of high user concentration in the proposed scenario. Because of the dynamic aspect of the system, this work proposes a solution based on machine learning. It consists of changing the ICIC parameters automatically to maintain the best possible signal-to-interference-plus-noise ratio (SINR) in a scenario with hotspots appearing over time. All investigations are based on ns-3 simulator prototyping. The results show that the proposed Q-Learning algorithm increases the average SINR from all users and hotspot users when compared with a scenario without Q-Learning. The SINR from hotspot users is increased by 11.2% in the worst case scenario and by 180% in the best case
发表机构
- Federal University of Rio Grande do Norte（北里奥格兰德联邦大学）
机构由 AI 辅助整理，请以论文原文为准。
