Abstract:
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.
