AI 中文总结
针对无线电能传输网络中现有协议不足，提出基于深度循环Q学习的波束控制策略，联合解决能量波束控制与时隙ALOHA随机接入问题，通过特定动作深度循环Q网络学习策略，无需信道估计等，相比非学习方案吞吐量提升68%，接近预言策略性能。
AI 中文摘要
在无线电能传输网络中，使用先收集再传输策略的设备的介质访问控制协议必须是分布式、低开销的，并且能够处理不规则和不频繁的数据传输，以确保高效的能源利用。然而，大多数现有协议无法满足这些要求中的一项或多项，导致稀缺的收集能量被浪费。我们通过将波束控制识别为调节能量收集设备充电速率并从而控制其对共享无线介质访问的潜在机制来解决这个问题。在制定了能量波束控制和基于时隙ALOHA的随机接入的联合问题后，我们利用基于特定动作的深度循环Q网络（ADRQN）的深度学习框架，仅从宏观层面的三元时隙结果（即空闲、成功和冲突）中学习波束控制策略。此外，我们设计了一种具有网络全局知识的预言策略，以对我们提出的盲自适应波束控制方法进行基准测试。数值结果表明，我们的方法与非学习方案相比，吞吐量提高了68%，同时也达到了预言策略性能的75-80%，且无需信道估计、电荷水平报告或设备状态跟踪。
英文摘要
In wireless powered communication networks, medium access control protocols for devices using the harvest-then-transmit strategy must be distributed, low-overhead, and capable of handling irregular and infrequent data transmissions to ensure efficient energy utilisation. However, most existing protocols fail to meet one or more of those requirements, leading to wasted scarce harvested energy. We address this by identifying beam steering as a potential mechanism to regulate the charging rate of energy harvesting devices and thus control their access to the shared wireless medium. After formulating a joint problem of energy beam steering and slotted ALOHA-based random access, we leverage a deep learning framework based on an action-specific deep recurrent Q-Network (ADRQN) to learn a beam-steering policy only from the macro-level ternary slot outcomes, namely, idle, success and collision. Additionally, we design an oracle policy with global knowledge of the network to benchmark our proposed blind adaptive beam-steering approach. The numerical results demonstrate that our approach achieves up to 68\% increase in throughput compared to non-learning schemes, while also reaching 75-80\% of the oracle policy's performance, all without requiring channel estimation, charge-level reporting, or device-state tracking.
Comments23 pages, 12 figures
