An efficient predictive admission control policy for heterogenous wireless bandwidth allocation in next generation mobile networks

Abstract

Next generation mobile networks (NGMN) are expected to integrate several heterogenous wireless technologies in order to provide high system capacity and cost effective global service coverage. In this paper we propose an efficient predictive admission control policy for heterogenous wireless bandwidth allocation. We predict well chosen traffic parameters using neural networks and we estimate blocking probabilities using generally distributed traffic models. Furthermore, we use a Tabu search algorithm to find the optimal guard band for a multi-layer heterogenous NGMN. The objective of our multi-layer predictive admission control policy (MLPAC) is to minimize global blocking probability while guaranteeing a hard constraint on handoff dropping probability. It extends the overflow scheme used in two-layer hierarchical cellular systems (HCS) to multiple heterogenous access technologies in NGMN. Presented results show that our MLPAC approach is more efficient in allocating the scarce heterogenous wireless bandwidth to a higher number of accepted connections while maintaining minimal guard bands for horizontal and vertical handoff protection.

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“…3. Variable capacity links: The availability of bandwidth in MANETs is scarce [1] and the main problem is the estimation of bandwidth because it varies with the mobility of nodes. 4.…”

Challenges and Evaluation of the State of the Art Routing Protocols for Wireless Mesh Networks

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