학술논문

SenseORAN: O-RAN-Based Radar Detection in the CBRS Band
Document Type
Periodical
Source
IEEE Journal on Selected Areas in Communications IEEE J. Select. Areas Commun. Selected Areas in Communications, IEEE Journal on. 42(2):326-338 Feb, 2024
Subject
Communication, Networking and Broadcast Technologies
Radar detection
Radar
Sensors
Interference
Spectrogram
Synthetic aperture sonar
Signal to noise ratio
CBRS
radar detection
O-RAN
AI
xApp
Language
ISSN
0733-8716
1558-0008
Abstract
Open RAN (O-RAN) has the potential for revolutionizing not only cellular communication but also spectrum sensing by carefully controlling uplink/downlink traffic in shared spectrum bands. In this paper, we present the design of SenseORAN, which detects the presence of radar pulses within the Citizens Broadband Radio Service (CBRS) band. SenseORAN is especially useful for scenarios where these pulses (highest priority) are fully overlapping with interfering LTE signals (secondary priority licensee), requiring immediate detection of such an occurrence. This design paradigm of re-using existing cellular infrastructure with ORAN-compliant sensing and communication slices can potentially eliminate the need for dedicated spectrum sensors along the coastline as well as severe restrictions on the transmit power for the LTE operators that are enforced today. Our approach involves a machine learning module deployed as a Radar Detection xApp at the near-Real-Time (near-RT) Radio Access Network (RAN) Intelligent Controller, i.e., near-RT RIC. The base station or gNB: 1) uses the you-only-look-once (YOLO)-based machine learning framework that is modified to detect radar signals present within spectrograms generated from I/Q samples collected during the regular uplink cellular operation; and 2) maintains a list of ‘occupied’ channels in the 3.5-GHz CBRS band that indicate radar presence. Our design is validated with: 1) an over the air collected dataset composed of Type 1 radar and standard-compliant LTE waveforms; and 2) an experimental testbed of SDRs running a complete Open RAN stack with a near-RT RIC implementation integrated with our YOLO-based xApp. We show radar detection accuracy of 100% under SINR conditions 12-dB after combining 7 spectrograms into a single decision. Furthermore, using testbed results, we demonstrate that the gNB can be reconfigured to avoid radar interference within 866-ms, which represents a reduction of 85.5% over the 60-s response time mandated for pausing cellular operation in detecting radar presence in the CBRS band today.