Cellular networks are projected to deal with an immense rise in data
traffic, as well as an enormous and diverse device, plus advanced use
cases, in the nearest future; hence, future 5G networks are being
developed to consist of not only 5G but also different RATs integrated.
In addition to 5G, the user’s device (UD) will be able to connect to the
network via LTE, WiMAX, WiFi, Satellite, and other technologies. On the
other hand, Satellite has been suggested as a preferred network to
support 5G use cases. However, achieving load balancing is essential to
guarantee an equal amount of traffic distributed between different RATs
in a heterogeneous wireless network; this would enable optimal
utilisation of the radio resources and lower the likelihood of call
blocking/dropping. This study presented an artificial intelligent-based
application in heterogeneous wireless networks and proposed an Enhanced
Particles Optimization (EPSO) algorithm to solve the load balancing
problem in 5G-Satellite networks. The algorithm uses a call admission
control strategy to admit users into the network to ensure that users
are evenly distributed on the network. The proposed algorithm was
compared with Artificial Bee and Simulated Annealing algorithm using
three performance metrics: throughput, call blocking and fairness.
Finally, based on the experimental findings, results outcomes were
analysed and discussed.