In this paper we propose a new algorithm for computing the optimal configuration of the Multiple Fractional Guard Channel (MFGC) admission policy in multiservice mobile wireless networks. The optimal configuration maximizes the offered traffic that the system can handle while meeting certain QoS requirements. The proposed algorithm is shown to be more efficient than previous algorithms appeared in the literature.

We study the impact of incorporating handoff prediction information in the session admission control process in mobile cellular networks. We evaluate the performance of optimal policies obtained with and without the predictive information, while taking into account possible prediction errors. Two different approaches to compute the optimal admission policy were studied: dynamic programming and reinforcement learning. Numerical results show significant performance gains when the predictive information is used in the admission process.

In mobile communication systems, mobility tracking operations are executed to maintain known the whereabouts of each mobile terminal (MT). In this article, we propose and analyse two new versions of the original time-based registration method, namely the Stop and Wait (S W) and the Reset and Wait (R W) time-based strategies. We also propose and study the modified S&W and R&W schemes as a result of combining the S&W and R&W policies with the enhanced time-based method in [3]. When the MT uses one of the four schemes proposed, it sends less location update (LU) messages. As a result of fewer LUs, which results in less contacts with the network, the uncertainty of the MT position increases in the proposed time-based schemes. Although the MT paging (PG) cost is lightly increased, a significant reduction in the LU cost is achieved. As it is shown, the net effect is a saving in the total location management cost per call arrival. In order to analyse the four strategies and compare them with the original and enhanced time-based schemes, a performance evaluation method based on Markovian standard tools is proposed here. The location management cost for all these time-based policies are evaluated analytically here by using this evaluation method.