The ever-increasing demand for data services and the proliferation of user equipment (UE) have resulted in a significant rise in the volume of mobile traffic. Moreover, in multi-band networks, non-uniform traffic distribution among different operational bands can lead to congestion, which can adversely impact the user's quality of experience. Load balancing is a critical aspect of network optimization, where it ensures that the traffic is evenly distributed among different bands, avoiding congestion and ensuring better user experience. Traditional load balancing approaches rely only on the band channel quality as a load indicator and to move UEs between bands, which disregards the UE's demands and the band resource, and hence, leading to a suboptimal balancing and utilization of resources. To address this challenge, we propose an event-based algorithm, in which we model the load balancing problem as a multi-objective stochastic optimization, and assign UEs to bands in a probabilistic manner. The goal is to evenly distribute traffic across available bands according to their resources, while maintaining minimal number of inter-frequency handovers to avoid the signaling overhead and the interruption time. Simulation results show that the proposed algorithm enhances the network's performance and outperforms traditional load balancing approaches in terms of throughput and interruption time.

翻译：随着数据服务需求的持续增长以及用户设备（UE）的广泛普及，移动流量规模显著攀升。在多频段网络中，不同工作频段间的非均匀流量分布可能导致网络拥塞，进而对用户体验质量造成负面影响。负载均衡作为网络优化的关键环节，旨在确保流量在不同频段间均匀分布，从而避免拥塞并提升用户体验。传统负载均衡方法仅将频段信道质量作为负载指标，并以此为依据在频段间迁移UE，这种做法忽视了UE的实际需求与频段资源状况，导致负载均衡效果欠佳且资源利用率低下。针对这一挑战，本文提出一种基于事件的算法，将负载均衡问题建模为多目标随机优化，并以概率方式为UE分配频段。其核心目标是根据各频段资源能力实现流量均匀分布，同时最小化频间切换次数以降低信令开销与中断时延。仿真结果表明，所提算法在吞吐量与中断时延等性能指标上优于传统负载均衡方法，显著提升了网络整体性能。