LarS-Net: A Large-Scale Framework for Network-Level Spectrum Sensing

TL;DR

LarS-Net addresses the challenge of large-scale spectrum sensing by introducing a deployment-aware simulator that couples realistic cellular topology, directional incumbent emissions, and physics-based propagation to quantify network-level detection performance. It defines three metrics—$EDP$, $TDP$, and $TMP$—to capture spatial coverage, temporal reliability, and mis-detection across inter-site distances and duty cycles. Through Monte Carlo simulations of a fixed-service link at $f_c=7.25$ GHz, the study shows that achieving $EDP$ above 90% requires modest densification (e.g., $D_{ ext{ISD}}$ on the order of $1$–$2.5$ km) and that sectorized sensing can reduce infrastructure needs to as little as 4% of BS sites, depending on propagation and antenna models. The results emphasize the importance of spatial diversity for wide-area sensing and provide a practical framework for deployment planning and ISAC-style optimization in future 6G shared-spectrum systems, with potential extensions to heterogeneous networks and adaptive sensing profiles.

Abstract

As the demand of wireless communication continues to rise, the radio spectrum (a finite resource) requires increasingly efficient utilization. This trend is driving the evolution from static, stand-alone spectrum allocation toward spectrum sharing and dynamic spectrum sharing. A critical element of this transition is spectrum sensing, which facilitates informed decision-making in shared environments. Previous studies on spectrum sensing and cognitive radio have been largely limited to individual sensors or small sensor groups. In this work, a large-scale spectrum sensing network (LarS-Net) is designed in a cost-effective manner. Spectrum sensors are either co-located with base stations (BSs) to share the tower, backhaul, and power infrastructure, or integrated directly into BSs as a new feature leveraging active BS antenna systems. As an example incumbent system, fixed service microwave link operating in the lower-7 GHz band is investigated. This band is a primary candidate for 6G, being considered by the WRC-23, ITU, and FCC. Based on Monte Carlo simulations, we determine the minimum subset of BSs equipped with sensing capability to guarantee a target incumbent detection probability. The simulations account for various sensor antenna configurations, propagation channel models, and duty cycles for both incumbent transmissions and sensing operations. Building on this framework, we introduce three network-level sensing performance metrics: Emission Detection Probability (EDP), Temporal Detection Probability (TDP), and Temporal Mis-detection Probability (TMP), which jointly capture spatial coverage, temporal detectability, and multi-node diversity effects. Using these metrics, we analyze the impact of LarS-Net inter-site distance, noise uncertainty, and sensing duty-cycle on large-scale sensing performance.

Figures (5)

• Figure 1: System model with $D_{\text{ISD}}$ = 500 m and free-space propagation. Black dots are sensors with 3-sector antenna and the red star represents the incumbent with an arrow pointing at its transmit direction. Sensors exceeding the detection threshold are shown in green.
• Figure 2: Spatial received-power maps under (a) FSPL, (b) LRM at Boulder, CO, and (c) terrain map of Boulder, CO from Google Earth. Black markers show BS positions and red stars denote the incumbent transmitter.
• Figure 3: Spatial received-power maps under FSPL and different ISDs (From left to right: ISD = 500m, 1000m, and 1500m). The sensors use 3-sector antennas.
• Figure 4: EDP, TDP, and TMP versus ISD under the FSPL and 3-sector antenna ($d_s = 0.2$, $p_{\text{on}} = 0.3$, noise = 3 dB).
• Figure 5: EDP versus ISD for three city footprints (areas indicated in the legend). "Network" denotes $K$-out-of-$N$ detection across all deployed BSs with $K=1$, while "Single (center)" denotes detection using the single sensor at the region center. Results are averaged over 2000 random incumbent drops per (ISD, city) under FSPL.