The Root Sequence Index (RSI) is a parameter used in mobile wireless networks to allocate uplink channels between user equipment and base stations. Inadequate RSI assignment to neighbor radios may lead to failure in service establishment and performance degradation. Wireless networks are also dynamic, with uncertain modifications in time due to, for instance, seasonal foliage. In this paper, we model RSI allocation with seasonal changes in the network as a multistage robust problem, being the first proactive, look-ahead method to consider uncertainty in RSI assignments. We develop methods to solve this stochastic problem, aiming to minimize the possible interference and network changes in time. A mixed-integer programming model, a classic Biased Random-Key Genetic Algorithm (BRKGA), and a novel BRKGA hybridized with Dijkstra’s algorithm are explored and compared. We also introduce a Monte Carlo-based simulation methodology to obtain scenarios. The hybrid BRKGA-based approach is shown to obtain more robust solutions in shorter computational times.

中文翻译：

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根序列索引分配的多阶段方法

根序列索引 （RSI） 是移动无线网络中使用的一个参数，用于在用户设备和基站之间分配上行链路信道。对邻居无线电的 RSI 分配不足可能会导致服务建立失败和性能下降。无线网络也是动态的，例如，由于季节性树叶，时间变化不确定。在本文中，我们将网络中季节性变化的 RSI 分配建模为一个多阶段稳健问题，这是第一个考虑 RSI 分配不确定性的主动、前瞻性方法。我们开发了解决这个随机问题的方法，旨在最大限度地减少可能的干扰和网络时间变化。探索和比较了混合整数规划模型、经典的偏置随机键遗传算法 （BRKGA） 以及与 Dijkstra 算法混合的新型 BRKGA。我们还引入了一种基于 Monte Carlo 的仿真方法来获取场景。结果表明，基于 BRKGA 的混合方法可以在更短的时间内获得更稳健的解决方案。