Cognitive Network Interference

TLDR

Opportunistic spectrum access allows secondary users to reuse under‑utilized licensed spectrum, but only if they accurately detect and quickly react to avoid harmful interference to primary users. This study aims to characterize the impact of cognitive network interference arising from such secondary spectrum reuse. We develop a statistical model for aggregate interference that incorporates sensing, spatial reuse, path loss, shadowing, fading, and power control, deriving its characteristic function, cumulants, and truncated‑stable distribution representation. Numerical results confirm that the model accurately captures the statistical behavior of cognitive network interference, offering essential insight for future deployments.

Abstract

Opportunistic spectrum access creates the opening of under-utilized portions of the licensed spectrum for reuse, provided that the transmissions of secondary radios do not cause harmful interference to primary users. Such a system would require secondary users to be cognitive-they must accurately detect and rapidly react to varying spectrum usage. Therefore, it is important to characterize the effect of cognitive network interference due to such secondary spectrum reuse. In this paper, we propose a new statistical model for aggregate interference of a cognitive network, which accounts for the sensing procedure, secondary spatial reuse protocol, and environment-dependent conditions such as path loss, shadowing, and channel fading. We first derive the characteristic function and cumulants of the cognitive network interference at a primary user. Using the theory of truncated-stable distributions, we then develop the statistical model for the cognitive network interference. We further extend this model to include the effect of power control and demonstrate the use of our model in evaluating the system performance of cognitive networks. Numerical results show the effectiveness of our model for capturing the statistical behavior of the cognitive network interference. This work provides essential understanding of interference for successful deployment of future cognitive networks.