Due to the ever-expanding applications of the Internet-of-Things (IoT), designing energy- and spectrally-efficient transmission schemes to support massive connections and devices is inevitable and still challenging. Thus, energy-harvesting (EH) and cognitive-radio (CR) systems are becoming more inseparable for future IoT networks. This paper analyzes the performance of EH-CR-IoT networks, where closed-form expressions for network metrics, such as GoodPut, collision probability and average packet delay are derived. In addition to the interference caused by spectrum sensing errors, our analysis also incorporates the primary user (PU) return interference into the different network metrics. Furthermore, the effect of primary network traffic behavior and IoT network parameters are investigated. To account for delay-sensitive packets, the average end-to-end delay of packets as well as delay violation probability in the IoT network are mathematically formulated and analyzed as quality-of-service (QoS) measures for network stability. Moreover, the derived metrics can be utilized to optimize the Goodput, subject to various practical constraints. Simulations are also performed to verify the theoretical results. Above all, the effect of energy-harvesting rate on GoodPut and IoT network stability is explored, which provides insights into determining the physical structure of the energy-harvesting system.

中文翻译：

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能量收集认知无线电物联网网络的 GoodPut、碰撞概率和网络稳定性

由于物联网 (IoT) 的应用不断扩大，设计节能和频谱高效的传输方案以支持海量连接和设备是不可避免的，并且仍然具有挑战性。因此，能量收集 (EH) 和认知无线电 (CR) 系统对于未来的物联网网络变得越来越密不可分。本文分析了 EH-CR-IoT 网络的性能，其中推导出了网络度量的闭式表达式，例如 GoodPut、冲突概率和平均数据包延迟。除了频谱感知错误引起的干扰外，我们的分析还将主用户 (PU) 返回干扰纳入不同的网络指标。此外，还研究了主要网络流量行为和物联网网络参数的影响。为了考虑延迟敏感的数据包，数据包的平均端到端延迟以及物联网网络中的延迟违规概率被数学公式化并分析为网络稳定性的服务质量 (QoS) 措施。此外，衍生的指标可用于优化 Goodput，但受各种实际约束。还进行了模拟以验证理论结果。最重要的是，探索了能量收集率对 GoodPut 和 IoT 网络稳定性的影响，这为确定能量收集系统的物理结构提供了见解。受到各种实际限制。还进行了模拟以验证理论结果。最重要的是，探索了能量收集率对 GoodPut 和 IoT 网络稳定性的影响，这为确定能量收集系统的物理结构提供了见解。受到各种实际限制。还进行了模拟以验证理论结果。最重要的是，探索了能量收集率对 GoodPut 和 IoT 网络稳定性的影响，这为确定能量收集系统的物理结构提供了见解。