Detection of Co- Existing RF Signals in CBRS Using ML: Dataset and API-Based Collection Testbed
Detection of Co- Existing RF Signals in CBRS Using ML: Dataset and API-Based Collection Testbed
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DOI：
10.1109/mcom.002.2200682
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发表时间：
2023-09
影响因子：
11.2
通讯作者：
Chinenye Tassie;Abdo Gaber;Vini Chaudhary;Nasim Soltani;M. Belgiovine;Michael Loehning;Vincent Kotzsch;Charles Schroeder;K. Chowdhury
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文献类型：
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作者：
Chinenye Tassie;Abdo Gaber;Vini Chaudhary;Nasim Soltani;M. Belgiovine;Michael Loehning;Vincent Kotzsch;Charles Schroeder;K. Chowdhury
开放频谱供共享使用，例如公民无线电宽带服务（CBRS）频段，为允许商业运营商在原本仅供联邦使用的频率上运营提供了前所未有的机会。特别是在CBRS频段，检测最高优先级在位雷达的挑战对部署LTE网络的运营商的发射功率施加了严格的限制。虽然基于机器学习（ML）的解决方案已经证明了在完全重叠的二次信号中检测微弱雷达信号的潜力，但由于一个关键原因，在将这些方法移植到实际的现实世界条件下存在根本差距：目前没有可访问的数据集，甚至没有受控的方法来通过空中（OTA）生成这样的数据集，其中雷达和LTE在许多具有挑战性的SINR条件下“重叠”。本文有三个贡献：它使用由软件定义无线电组成的实验测试平台，描述了3.5 GHz频段上第一个公开可用的CBRS重叠和非重叠LTE和雷达OTA数据集；它描述了首个同类开源应用程序编程接口（API），可以自动配置多个发射器和接收器无线电，同步它们，去除Tx本地振荡器引起的伪像，并仔细设置采样率，中心频率和采样持续时间等参数，最终产生信号元数据格式（SigMF）的高保真数据；它通过采用名为“You Only Look Once”（YOLO）的著名ML模型，以近乎完美的精度检测和定位雷达和LTE信号，展示了CBRS数据集的实用性，指出了降低CBRS频段蜂窝运营商当前fcc规定的功率阈值的可能性。
Opening up of spectrum for shared use, such as the Citizen Radio Broadband Service (CBRS) band, offers unprecedented opportunities for allowing commercial operators to operate in frequencies otherwise reserved for federal use only. Specifically in the CBRS band, the challenge of detecting the highest priority incumbent radar reliably forces severe restrictions on the transmit power for operators deploying LTE networks. While Machine Learning (ML)-based solutions have demonstrated the potential for detecting weak radar signals in fully overlapping secondary signals, there exists a fundamental gap in porting these methods for practical, real-world conditions due to a key reason: There are no accessible data-sets or even controlled methods to generate such datasets today over-the-air (OTA), where radar and LTE ‘overlap’ in a number of challenging SINR conditions. This article makes three contributions: It describes the first publicly available CBRS over-lapping and non-overlapping LTE and radar OTA dataset in the 3.5 GHz band using an experimental testbed composed of software defined radios; It describes the first-of-its-kind open source Application Programming Interface (API) that can configure automatically multiple transmitters and receiver radios, synchronize them, remove the Tx local oscillators-induced artifacts, and carefully set their parameters such as sampling rates, center frequencies, and time duration for sample collection, ultimately resulting in high-fidelity data in the Signal Metadata Format (SigMF); It demonstrates the utility of the CBRS dataset by adapting the well-known ML model called “You Only Look Once” (YOLO) for detecting and localizing the radar and LTE signals with near-perfect accuracy, pointing to the possibility that current FCC-mandated power thresholds can be lowered for cellular operators in the CBRS band.