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 Swap-based 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.

中文翻译：

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基于交换的负载平衡以实现无线接入网络的公平性


由微蜂窝和微微蜂窝组成的 5G 微基础设施将在致密网络以提供充足的覆盖范围方面发挥关键作用。然而，移动设备与这些小蜂窝的不成比例的关联会导致热点和负载不平衡。在这样的网络中，一些微蜂窝或微微蜂窝遭受网络拥塞。而许多其他技术则未得到充分利用，吞吐量较低，并且在低于潜在网络容量的情况下运行。为了减轻这个缺点，在异构和同质网络中，一些负载平衡（LB）方法是必不可少的。为了实现这一目标，我们提出了 AP 之间基于交换的极端负载均衡 (SLB) 算法，该算法最大限度地减少了小区边缘的负载不平衡。实验设置使用爱尔兰移动运营商提供的数据集。我们的结果表明，与最佳单传输算法相比，带有偏置的 SLB 将负载不平衡减少了 7.14%。与其他最先进的算法相比，它提高了 22.24%。带有偏置的 SLB 可减少 AP 中的负载不平衡和用户之间的信号质量。