The intervention of evolutionary computation methods in studying energy harvesting (EH) techniques in multiple-input-single-output cognitive radio networks (MISO-CRNs) is carefully investigated. In this work, a secondary user (SU) with multiple antennas harvests energy from a hybrid base station (HBS) in addition to harvesting from the primary user (PU) transmission during the downlink time-slot. Hence, the SU transmits its data on the uplink over Nakagami-m fading channels. For the underlay paradigm, a specific tolerable PU interference limits the SU total transmission power. The HBS does not have previous information about the SU battery level. A closed-form expression for the SU exact outage probability is derived and simplified to its asymptotic formula for the high signal-to-noise-ratio (SNR) region. Closed-form expressions for the system average symbol error probability (ASEP) and ergodic capacity are obtained. The particle swarm optimization (PSO) algorithm is used to find the optimal SU transmission power to minimize the sophisticated formula of the exact outage probability. Simulations and numerical results reveal the effect of the critical system parameters on the overall system performance and validate the derived mathematical analysis. Moreover, the optimum solution of the power optimization problem shows noteworthy enhancement of the system performance compared to the traditional equally distributed model.

仔细研究了进化计算方法对研究多输入单输出认知无线电网络（MISO-CRN）中的能量收集（EH）技术的干预。在这项工作中，具有多个天线的辅助用户（SU）除了在下行链路时隙中从主要用户（PU）传输中收集能量之外，还从混合基站（HBS）收集能量。因此，SU在上行链路上超过Nakagami-发送其数据米衰落的频道。对于底层范例，特定的可忍受的PU干扰会限制SU的总传输功率。HBS没有有关SU电池电量的先前信息。对于高信噪比（SNR）区域，得出SU确切中断概率的封闭形式表达式，并将其简化为渐近公式。获得系统平均符号错误概率（ASEP）和遍历容量的闭式表达式。粒子群优化（PSO）算法用于找到最佳的SU传输功率，以最大程度地减少精确停机概率的复杂公式。仿真和数值结果揭示了关键系统参数对整体系统性能的影响，并验证了得出的数学分析。此外，