In this work, we propose a novel framework to identify and mitigate a recently disclosed covert channel scheme exploiting unprotected broadcast messages in cellular MAC layer protocols. Examples of covert channel are used in data exfiltration, remote command-and-control (CnC) and espionage. Responsibly disclosed to GSMA (CVD-2021-0045), the SPARROW covert channel scheme exploits the downlink power of LTE/5G base-stations that broadcast contention resolution identity (CRI) from any anonymous device according to the 3GPP standards. Thus, the SPARROW devices can covertly relay short messages across long-distance which can be potentially harmful to critical infrastructure. The SPARROW schemes can also complement the solutions for long-range M2M applications. This work investigates the security vs. performance trade-off in CRI-based contention resolution mechanisms. Then it offers a rigorously designed method to randomly obfuscate CRI broadcast in future 5G/6G standards. Compared to CRI length reduction, the proposed method achieves considerable protection against SPARROW exploitation with less impact on the random-access performance as shown in the numerical results.

Starting with 5G-NR standard and moving towards 6G, the mmW communication technology (in 6−300 GHz frequency range) fulfills the demand for high throughput in various parts of a radio access network. The weak propagation characteristics of mmW signals compel a more meticulous radio network planning, even in the initial phases. Previously used in backhaul networks, the quality of mmW radio links significantly relies on the Line- of-Sight (LOS) signal paths between the communicating radios. LiDAR point cloud is a popular source of high-resolution aerial survey data suitable for accurate LOS assessments. In this work we consider a comprehensive LOS assessment problem incorporating the radio mounting height, a key practical consideration in planning cellular networks. Then we proposed a method empowered by a novel topologically-sorted polar digital elevation model (DEM) and a new query-based algorithm to execute large-scale LOS assessments more efficiently and accurately than the existing methods. The post deployment measurement reports indicate that using the proposed DEM leads to 50% reduction in the prediction errors compared to the other common methods. The proposed method is designed to process LOS assessment queries in constant-time, O(1), and achieves O(log N) speed-up over a widely known visibility algorithm when performing LOS assessments for the entire planning area (viewshed). The numerical results from our software implementation also confirm significant reduction in the viewshed computation time.