面向多级公平性负载均衡的用户关联与资源分配联合优化
Joint Optimization of User Association and Resource Allocation for Load Balancing With Multi-Level Fairness
AI总结：
针对差异化用户优先级需求，论文提出异构 alpha 公平性目标及基于定价和辅助变量的分布式 UARA 方法，并证明其收敛到随迭代改善的 $ε$-最优解。
摘要：
用户关联是将每个用户设备分配给合适基站的问题；随着无线网络日益密集，并为更多具有多样化业务需求的用户提供服务，该问题正变得愈发关键。用户关联与资源分配（UARA）联合优化是未来无线网络的基本问题，因为它在提升整体网络性能、用户公平性和资源效率方面发挥关键作用。鉴于新兴网络应用对时延敏感，网络管理更偏好简单且计算高效的算法，而非复杂的集中式方法。因此，基于定价的分布式策略在 UARA 文献中受到重视，并在多种目标函数下展现出实用性和有效性，例如和速率、比例公平、最大-最小公平以及 alpha 公平。尽管 alpha 公平框架能够通过单一参数 $α$ 在效率与公平性之间灵活调节，但现有工作主要假设同构公平性情境，即为所有用户分配相同的 $α$ 值。然而，由于应用需求和时延各不相同，现实网络经常需要差异化的用户优先级。为弥补这一差距，我们提出一种新的异构 alpha 公平性（HAF）目标函数，为不同用户分配不同的 α 值，从而对全网吞吐量、公平性和时延之间的平衡提供更强的控制。我们提出一种利用辅助变量框架的、基于定价的分布式优化方法，并给出解析证明，表明其收敛到一个 $ε$-最优解，其中最优性差距 $ε$ 随迭代次数增加而减小。
英文摘要：
User association, the problem of assigning each user device to a suitable base station, is increasingly crucial as wireless networks become denser and serve more users with diverse service demands. The joint optimization of user association and resource allocation (UARA) is a fundamental issue for future wireless networks, as it plays a pivotal role in enhancing overall network performance, user fairness, and resource efficiency. Given the latency-sensitive nature of emerging network applications, network management favors algorithms that are simple and computationally efficient rather than complex centralized approaches. Thus, distributed pricing-based strategies have gained prominence in the UARA literature, demonstrating practicality and effectiveness across various objective functions, e.g., sum-rate, proportional fairness, max-min fairness, and alpha-fairness. While the alpha-fairness frameworks allow for flexible adjustments between efficiency and fairness via a single parameter $α$, existing works predominantly assume a homogeneous fairness context, assigning an identical $α$ value to all users. Real-world networks, however, frequently require differentiated user prioritization due to varying application requirements and latency. To bridge this gap, we propose a novel heterogeneous alpha-fairness (HAF) objective function, assigning distinct α values to different users, thereby providing enhanced control over the balance between throughput, fairness, and latency across the network. We present a distributed, pricing-based optimization approach utilizing an auxiliary variable framework and provide analytical proof of its convergence to an $ε$-optimal solution, where the optimality gap $ε$ decreases with the number of iterations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
