Abstract-Typically, wireless network performance decreases in high traf c regimes, e. g., at traf c hot spots and peak hours, since a large number of active users have to share limited radio resources. From a network perspective, even congestion may occur in some cells, which may lead to drastic deterioration of user throughputs. As a consequence, network operators employ admission control in their base stations in order to prevent congestion and enhance quality of experience. In this paper, we develop an effective network planning and optimization tool set, which considers the dynamic behavior of mobile traf c. The tool set allows for a more accurate prediction of data request blocking probabilities and data throughputs under admission control, since it explicitly considers the inter-cell interference coupled nature of frequency reuse one networks. This enables more reliable planning and (self-) optimization of wireless networks. We prove utility by applying the tool set to a traf c-adaptive admission control scheme and compare the resulting network performance with that of static admission control schemes under demands of high mobile data traf c. We nd that the adaptive algorithm is able to exploit the trade-offs between blocking of requests, reduced interference, and guaranteed resources for individual data transmissions in each of the cells. Under high traf c conditions, it yields better performance compared to any other static scheme investigated
