考虑信道噪声影响，提升频谱接入的快速分配效率
A Context Augmented Multi-Play Multi-Armed Bandit Algorithm for Fast Channel Allocation in Opportunistic Spectrum Access
- 用上下文建模信道噪声对收益的影响，改进多臂赌博机算法
- 两种策略分别基于线性和非线性相关，降低后悔值并减少选劣解
- 适合需要快速、稳定频谱分配的无线通信系统研究者
我们研究了机会频谱接入（OSA）场景下的非平稳上下文多选多臂赌博机（MP-MAB）问题。现有大多数MP-MAB方法因假设理想条件、计算开销大，且忽略与服务质量直接相关的信道噪声，难以应用于实际系统。本文将信道噪声建模为臂收益函数的扰动，并利用信道状态信息作为上下文来表征该扰动。我们探讨了上下文与扰动之间的线性和非线性关联，分别推导出两种索引策略：一种通过线性模型学习，另一种使用神经网络。两者均利用估计的噪声值调整上置信界，从而更合理地选择信道。数值实验表明，所提策略能显著降低后悔值，并更优地避免选择次优信道。
原文摘要 · Abstract (English)
We study the restless contextual multi-play multi-armed bandit (MP-MAB) problem for channel allocation in the opportunity spectrum access (OSA) scenario. Most existing MP-MAB methods are impractical for real-world OSA systems as they assume many ideal conditions, incur a heavy computational cost, and most importantly, ignore the impact of channel noise which is directly related to the quality of service. In this study, we embody this impact by modeling channel noise as a perturbation of the arm's reward function in MP-MAB. As there is an implicit correlation between channel state information and channel noise, we take the former as a context for MP-MAB to present the perturbation caused by the latter. We investigate two types of correlation between the context and the perturbation -- linear and nonlinear, and derive two index policies, respectively. These policies learn the correlations through a linear model and a neural network, and use estimated noise value to adjust the upper confidence bound. Numerical experiments demonstrate that the proposed policies can achieve lower regret and select sub-optimal arms in a more reasonable way.
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