论文标题
一种基于多臂强盗的移动网络提供商选择的方法
A Multi-Armed Bandit-based Approach to Mobile Network Provider Selection
论文作者
论文摘要
我们主张使用户能够从任何移动网络运营商暂时租赁带宽。我们建议，原型和评估移动网络访问的频谱市场，多个网络运营商以特定的价格以短期租赁的特定价格向用户提供带宽的块，并在用户设备上自主代理商通过交易价格，绩效和预算约束来进行购买决策。 我们表明，提供者选择的问题可以作为所谓的强盗问题提出。对于提供者同步更改价格的情况，我们通过上下文的多军匪徒和强化学习方法来解决问题，例如直接应用于Bandit最大化问题，或间接地用于近似已知的Gittins指数，这些指数已知，以产生最佳的提供者选择策略。对于与实际用例相对应的模拟场景，我们的代理商在各种需求，价格和移动性条件下显示了$ 20-41 \％$ QOE的改进。 我们使用LTE网络和ESIM Techology实施了原型频谱市场，并将其部署在测试台上，并使用区块链实现了记录带宽购买交易的分类帐。实验表明，我们可以有效地学习用户行为和网络性能，并在各种竞争代理方案下记录$ 25-74 \％$改进。
We argue for giving users the ability to lease bandwidth temporarily from any mobile network operator. We propose, prototype, and evaluate a spectrum market for mobile network access, where multiple network operators offer blocks of bandwidth at specified prices for short-term leases to users, with autonomous agents on user devices making purchase decisions by trading off price, performance, and budget constraints. We show that the problem of provider selection can be formulated as a so-called Bandit problem. For the case where providers change prices synchronously, we approach the problem through contextual multi-armed bandits and Reinforcement Learning methods like Q-learning either applied directly to the bandit maximization problem or indirectly to approximate the Gittins indices that are known to yield the optimal provider selection policy. For a simulated scenario corresponding to a practical use case, our agent shows a $20-41\%$ QoE improvement over random provider selection under various demand, price and mobility conditions. We implemented a prototype spectrum market using LTE networks and eSIM techology and deployed it on a testbed, using a blockchain to implement the ledger where bandwidth purchase transactions are recorded. Experiments showed that we can learn both user behavior and network performance efficiently, and recorded $25-74\%$ improvements in QoE under various competing agent scenarios.
