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The efficient allocation and use of radio resources is crucial for achieving the maximum possible throughput and capacity in wireless networks. The conventional strongest signal-based user association in cellular networks generally considers only the strength of the signal while selecting a BS, and ignores the level of congestion or load at it. As a consequence, some BSs tend to suffer from heavy load, while their adjacent BSs may carry only light load. This load imbalance severely hampers the network from fully utilizing the network capacity and providing fair services to users. In this thesis, we investigate the applicability of the preamble code sequence, which is mainly used for cell identification, as an implicit information indicator for load balancing in cellular networks. By exploiting the high auto-correlation and low cross-correlation property among preamble sequences, we propose distributed load balancing schemes that implicitly obtain information about the load status of BSs, for intelligent association control. This enables the new users to be attached to BSs with relatively low load in the long term, alleviating the problem of non-uniform user distribution and load imbalance across the network. Extensive simulations are performed with various user densities considering throughput fair and resource fair, as the resource allocation policies in each cell. It is observed that significant improvement in minimum throughput and fair user distribution is achieved by employing our proposed schemes, and preamble sequences can be effectively used as a leverage for better cell-site selection from the viewpoint of fairness provisioning. The load of the entire system is also observed to be balanced, which consequently enhances the capacity of the network, as evidenced by the simulation results.

Association Control Based Load Balancing in Wireless Cellular Networks Using Preamble Sequences