The problem of joint power and admission control (JPAC) is a critical issue encountered in underlay cognitive radio networks (CRNs). Moving forward towards the realization of Fifth Generation (5G) and beyond, where optimization is envisioned to take place in multiple performance dimensions, it is crucially desirable to achieve high sum throughput with low power consumption. In this work, a multi-objective JPAC optimization problem that jointly maximizes the sum throughput and minimizes power consumption in underlay CRNs is formulated. An enhanced swarm intelligence algorithm has been developed by hybridizing two new enhanced Particle Swarm Optimization (PSO) variants, namely two-phase PSO (TPPSO) and diversity global position binary PSO (DGP-BPSO) variants employed to optimize the multi-objective JPAC problem. The performance of the enhanced swarm intelligence algorithm in terms of convergence speed and stability, while optimizing both the sum throughput and power consumption, is investigated under three different operational scenarios defined by their single objective priorities, which translate to sum throughput and power consumption preferences. Simulation results have proven the effectiveness of the enhanced swarm intelligence algorithm in achieving high sum throughput and low power consumption under the three operational scenarios when the network includes an arbitrary number of primary and secondary users. Comparing the hybrid SPSO approach and the proposed approach, the proposed scheme has shown its effectiveness in increasing the sum throughput to 7%, 16%, and 31% under the multimedia, balanced and power saving operational scenarios, respectively. In addition, the proposed approach is more power efficient as it can provide additional power savings of 3.58 W, 2.48 W, and 1.6741 W under the aforementioned operational scenarios, respectively.

中文翻译：

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基于增强群智能的认知无线网络联合功率和接入控制多目标优化

联合权力和准入控制（JPAC）问题是底层认知无线电网络（CRN）遇到的关键问题。朝着实现第五代（5G）以及以后的发展迈进，在第五代中可以在多个性能维度上进行优化，因此，以低功耗实现高总吞吐量至关重要。在这项工作中，提出了一个多目标JPAC优化问题，该问题共同提高了底层CRN的总吞吐量和最小化功耗。通过混合两个新的增强型粒子群优化（PSO）变体，即两相PSO（TPPSO）和多样性全局位置二进制PSO（DGP-BPSO）变体，来优化多目标JPAC问题，从而开发了一种增强的群智能算法。 。在优化总吞吐率和功耗的同时，优化了群体智能算法在收敛速度和稳定性方面的性能，并在三种不同的操作场景下对其进行了研究，这些场景由它们的单个目标优先级定义，这些场景转化为总吞吐率和功耗偏好。仿真结果证明，当网络包括任意数量的主要和次要用户时，增强的群智能算法在三种操作方案下实现高总吞吐量和低功耗的有效性。将混合SPSO方法与所提出的方法进行比较，所提出的方案已显示出其在多媒体，平衡和节能操作方案下将总吞吐量提高到7％，16％和31％的有效性，分别。另外，所提出的方法具有更高的功率效率，因为在上述操作方案下，它可以分别提供3.58 W，2.48 W和1.6741 W的额外功率节省。