Abstract:
5G micro infrastructure comprising micro and picocells would play a pivotal role in densifying the network to
provide ample coverage. However, a disproportional association
of mobile devices with these small cells would cause hotspots and
load imbalance. In such a network, a few micro or picocells suffer
from network congestion. While many others are underutilized,
experience lower throughput, and operate below the potential
network capacity. To mitigate this drawback, some means of Load
Balancing (LB) would be essential in heterogeneous and homogenous networks. To achieve this, we propose an extreme Swapbased Load Balancing (SLB) algorithm between APs, which
minimizes the load imbalance at cell edges. The experimental
setup uses a dataset contributed by Irish mobile operators. Our
results reveal SLB with biasing reduces the load imbalance by a
factor of 7.14% compared to the optimal uni-transfer algorithm.
Against other state-of-the-art algorithms, it betters by 22.24%.
SLB with biasing delivers both lesser load imbalance in APs and
signal quality amongst users.
