Interference Modeling in Wireless Networks

Abstract

Cognitive radio (CR) networks and heterogeneous cellular networks are promising approaches to satisfy the demand for higher data rates and better connectivity. A CR network increases the utilization of the radio spectrum by opportunistically using it. Heterogeneous networks provide high data rates and improved connectivity by spatially reusing the spectrum and by bringing the network closer to the user. Interference presents a critical challenge for reliable communication in these networks. Accurately modeling it is essential in ensuring a successful design and deployment of these networks. We first propose modeling the aggregate interference power at a primary receiver (PU-Rx) caused from transmissions by randomly located cognitive users (CUs) in a CR network as a shifted lognormal random process. Its parameters are determined using a moment matching method. Extensive benchmarking shows that the proposed model is more accurate than the lognormal and Gaussian process models considered in the literature, even for a relatively dense deployment of CUs. It also compares favorably with the asymptotically exact stable and symmetric truncated stable distribution models, except at high CU densities. Our model accounts for the effect of imperfect spectrum sensing, interweave and underlay modes of CR operation, and path-loss, time-correlated shad-owing and fading of the various links in the network. It leads to new expressions for the probability distribution function, level crossing rate (LCR), and average exceedance duration (AED). The impact of cooperative spectrum sensing is also characterized. We also apply and validate the proposed model by using it to redesign the primary exclusive zone to account for the time-varying nature of interference. Next we model the uplink inter-cell aggregate interference power in homogeneous and heterogeneous cellular systems as a simpler lognormal random variable. We develop a new moment generating function (MGF) matching method to determine the lognormal’s parameters. Our model accounts for the transmit power control, peak transmit power constraint, small scale fading and large scale shadowing, and randomness in the number of interfering mobile stations and their locations. In heterogeneous net-works, the random nature of the number and locations of low power base stations is also accounted for. The accuracy of the proposed model is verified for both small and large values of interference. While not perfect, it is more accurate than the conventional Gaussian and moment-matching-based lognormal and Gamma distribution models. It is also performs better than the symmetric-truncated stable and stable distribution models, except at higher user density.