NR-U系统中基于学习的WiFi业务负载估计
Learning-based WiFi Traffic Load Estimation in NR-U Systems
摘要：
在新无线电（NR）系统中，未授权频谱已经被用来弥补频谱上的不足。为了充分利用免许可频段带来的优势，关键问题之一是保证与WiFi系统的公平共存。要实现这一目标，及时准确地估计WiFi流量负载是一个重要的前提。在本文中，机器学习（ML）的基础上，提出了一种方法来检测未授权频段上的WiFi用户的数量。采用无监督的神经网络（NN）结构对未授权频谱上检测到的传输冲突概率进行滤波，使NR用户能够精确校正测量误差并估计活动WiFi用户的数量。通过对神经网络进行在线训练，并对神经网络的相关参数和学习率进行联合优化，实现了对WiFi用户数的自适应高精度估计。仿真结果表明，与传统的基于卡尔曼滤波器的检测机制相比，该方法具有更低的复杂度，能够获得更稳定、更准确的估计。
英文摘要：
The unlicensed spectrum has been utilized to make up the shortage on frequency spectrum in new radio (NR) systems. To fully exploit the advantages brought by the unlicensed bands, one of the key issues is to guarantee the fair coexistence with WiFi systems. To reach this goal, timely and accurate estimation on the WiFi traffic loads is an important prerequisite. In this paper, a machine learning (ML) based method is proposed to detect the number of WiFi users on the unlicensed bands. An unsupervised Neural Network (NN) structure is applied to filter the detected transmission collision probability on the unlicensed spectrum, which enables the NR users to precisely rectify the measurement error and estimate the number of active WiFi users. Moreover, NN is trained online and the related parameters and learning rate of NN are jointly optimized to estimate the number of WiFi users adaptively with high accuracy. Simulation results demonstrate that compared with the conventional Kalman Filter based detection mechanism, the proposed approach has lower complexity and can achieve a more stable and accurate estimation.
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。
