Next Generation Networks (NGN) such as LTE and WiMAX offer higher
spectral efficiency and data rates using new technologies such as
femto cells, relay nodes etc. These networks are normally deployed
for use in parallel with existing networks. This approach to
network deployment complicates network operation and management,
thus translating to higher capital and operational costs. In a bid
to minimize these costs, self-organising operations were
envisioned. Load balancing is a self-organising operation. It aims
at ensuring an equitable distribution of users in the network.
Several methods based on iterative techniques have been proposed
and some adopted for load balancing. However, these iterative
techniques are computationally intensive and use a limited number
of parameters for load balancing. This work proposes two models
developed from network simulations for load balancing. The two
models are based on Adaptive Neuro-Fuzzy Inference System (ANFIS)
designed using load indicators and key performance indicators
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