Swap-based load balancing for fairness in radio access networks

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.