基于RL的部分重叠音调非协调网络干扰抑制
RL-Based Interference Mitigation in Uncoordinated Networks with Partially Overlapping Tones
摘要：
已知部分重叠音调（POT）通过向所发送的信号引入有意的频率偏移（FO）来帮助减轻非协调多载波网络中的同信道干扰。在本文中，我们探讨了在密集网络中使用（POT）与强化学习（RL），其中多个链路同时访问时频资源。我们提出了一种基于Q学习的新框架，以获得用于每个链路的多载波波形的（FO）。特别是，我们考虑滤波多音（FMT）系统，利用高斯，根升余弦（RRC），和各向同性正交变换算法（IOTA）的原型滤波器。仿真结果表明，在高信噪比的加性高斯白噪声（AWGN）信道中，该方案可使链路容量提高至少30%，在严重多径衰落的情况下，该方案的性能更胜一筹.对于广泛的干扰链路密度，我们证明了中断概率和多用户效率的大幅改善促进（POT），高斯滤波器优于其他两个过滤器。
英文摘要：
Partially-overlapping tones (POT) are known to help mitigate co-channel interference in uncoordinated multi-carrier networks by introducing intentional frequency offsets (FOs) to the transmitted signals. In this paper, we explore the use of (POT) with reinforcement learning (RL) in dense networks where multiple links access time-frequency resources simultaneously. We propose a novel framework based on Q-learning, to obtain the (FO) for the multi-carrier waveform used for each link. In particular, we consider filtered multi-tone (FMT) systems that utilize Gaussian, root-raised-cosine (RRC), and isotropic orthogonal transform algorithm (IOTA) based prototype filters. Our simulation results show that the proposed scheme enhances the capacity of the links by at least 30\% in additive white Gaussian noise (AWGN) channel at high signal-to-noise ratio (SNR), and even more so in the presence of severe multi-path fading. For a wide range of interfering link densities, we demonstrate substantial improvements in the outage probability and multi-user efficiency facilitated by (POT), with the Gaussian filter outperforming the other two filters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
