As several new spectrum bands are opening up for shared use, a new paradigm of \textit{Diverse Band-aware Dynamic Spectrum Access} (d-DSA) has emerged. d-DSA equips a secondary device with software defined radios (SDRs) and utilize whitespaces (or idle channels) in \textit{multiple bands}, including but not limited to TV, LTE, Citizen Broadband Radio Service (CBRS), unlicensed ISM. In this paper, we propose a decentralized, online multi-agent reinforcement learning based cross-layer BAnd selection and Routing Design (BARD) for such d-DSA networks. BARD not only harnesses whitespaces in multiple spectrum bands, but also accounts for unique electro-magnetic characteristics of those bands to maximize the desired quality of service (QoS) requirements of heterogeneous message packets; while also ensuring no harmful interference to the primary users in the utilized band. Our extensive experiments demonstrate that BARD outperforms the baseline dDSAaR algorithm in terms of message delivery ratio, however, at a relatively higher network latency, for varying number of primary and secondary users. Furthermore, BARD greatly outperforms its single-band DSA variants in terms of both the metrics in all considered scenarios.

中文翻译：

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不同频段感知DSA网络的跨层频段选择和路由设计

随着几个新的频段开放供共享使用，出现了\textit{Diverse Band-aware Dynamic Spectrum Access} (d-DSA) 的新范式。d-DSA 为辅助设备配备软件定义无线电 (SDR) 并利用 \textit{multiple band} 中的空白（或空闲信道），包括但不限于 TV、LTE、公民宽带无线电服务 (CBRS)、未经许可的 ISM。在本文中，我们为这种 d-DSA 网络提出了一种基于分散式、在线多智能体强化学习的跨层 BAnd 选择和路由设计（BARD）。BARD 不仅利用多个频段中的空白，而且考虑到这些频段的独特电磁特性，以最大限度地提高异构消息包的服务质量 (QoS) 要求；同时还确保对所用频段的主要用户没有有害干扰。我们的大量实验表明，BARD 在消息传递率方面优于基线 dDSAaR 算法，但是，对于不同数量的主要和次要用户，网络延迟相对较高。此外，在所有考虑的场景中，BARD 在这两个指标方面都大大优于其单频段 DSA 变体。