학술논문

Cognitive Resource Analyzer for Cellular Network Ecosystems
Document Type
Periodical
Source
IEEE Transactions on Cognitive Communications and Networking IEEE Trans. Cogn. Commun. Netw. Cognitive Communications and Networking, IEEE Transactions on. 8(2):733-749 Jun, 2022
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Long Term Evolution
Monitoring
Ecosystems
Synchronization
Detectors
Cellular networks
Downlink
Cognitive radio
interweave
network monitoring
4G LTE
5G NR
opportunistic communications
energy detection
Language
ISSN
2332-7731
2372-2045
Abstract
Modern cellular infrastructure has grown in complexity and become an ecosystem that includes the host technologies, such as 4G Long Term Evolution (LTE) and 5G New Radio (NR), along with subsystems such as narrowband Internet of Things (NB-IoT), category M1 (Cat-M1/LTE-M), observed time difference of arrival (OTDOA) and support for 4G/5G coexistence using dynamic spectrum sharing (DSS). Such an increased complexity has driven the need for network monitoring to enable load tracking, congestion control, spectral efficiency analysis, intrusion detection, and cognitive radio communications. However, no approach can passively monitor the entire cellular ecosystem because some reservations are configured with high-layer messages that are only accessible to in-network, active, and sometimes user-specific equipment. In this work, we design the Resource Analyzer for Variable Ecosystem Networks (RAVEN) to monitor the individual technologies within the cellular ecosystem. RAVEN reduces the synchronization search space for multiple subsystems and enables a fine-grained subsystem-level tracking of spectrum usage. RAVEN also provides a method to detect anomalies and find subsystems that are not yet supported. RAVEN is validated through simulation and live experiments and is compared to other cellular network monitors.