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The goal of energy harvesting (EH) and cognitive radio networks (CRNs) is to increase spectrum usage while preserving user mobility. By allowing secondary users (SUs) to access the channel while the primary users (PUs are not present, cognitive radio technology improves spectrum utilization. Examine an EH secondary transmitter (ST)-based CRN using Jarratt–Butterfly optimization algorithm (JBOA) in cases where delays are present in this research. Initially, the EH-enabled ST dynamically adjusts transmission power to attain spectrum efficiency for data transmission depending on the PU's sensing findings. Furthermore, the approximate compound Poisson distribution describes the ST renewable energy collection method. The primary objective is to maximize energy efficiency (EE) of EH-ST-CRN while keeping in mind the average energy, average interference power, average transmission power, and multiple minimum rate outage probability (MMROP). Given that maximizing EE is an unsolvable optimal issue with numerous intricate restrictions, a solution known as JBOA is suggested. To be more precise, the suggested JBOA first converts a primitive optimal issue into a tractable counterpart. The proposed approach is compared with current methods and shows encouraging results in terms of achieving a throughput rate in threshold set 0.45 bps/Hz for 25 (mW) and 0.58 bps/Hz for 125 mW, respectively.

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Hybrid Jarratt and Butterfly Optimization-Based Approach for Maximizing Throughput in Energy-Harvesting Cognitive Radio Networks

  • 会议论文

  • 发表日期：

    2026年

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  • N. Kumaran,
  • Gujjula Parameshwar,
  • Siva Surya Narayana Chintapalli,
  • G. P. Ramesh,
  • V. Sushma

摘要

The goal of energy harvesting (EH) and cognitive radio networks (CRNs) is to increase spectrum usage while preserving user mobility. By allowing secondary users (SUs) to access the channel while the primary users (PUs are not present, cognitive radio technology improves spectrum utilization. Examine an EH secondary transmitter (ST)-based CRN using Jarratt–Butterfly optimization algorithm (JBOA) in cases where delays are present in this research. Initially, the EH-enabled ST dynamically adjusts transmission power to attain spectrum efficiency for data transmission depending on the PU's sensing findings. Furthermore, the approximate compound Poisson distribution describes the ST renewable energy collection method. The primary objective is to maximize energy efficiency (EE) of EH-ST-CRN while keeping in mind the average energy, average interference power, average transmission power, and multiple minimum rate outage probability (MMROP). Given that maximizing EE is an unsolvable optimal issue with numerous intricate restrictions, a solution known as JBOA is suggested. To be more precise, the suggested JBOA first converts a primitive optimal issue into a tractable counterpart. The proposed approach is compared with current methods and shows encouraging results in terms of achieving a throughput rate in threshold set 0.45 bps/Hz for 25 (mW) and 0.58 bps/Hz for 125 mW, respectively.