Cognitve Radio (CR) technology has recently become popular for future communication networks since it allows co-existence of Secondary User (SU) and Primary User (PU) networks. Compared to interweave and overlay, underlay mode of operation is best suited for practical scenarios since it facilitates PU-SU co-existence with minimum overhead. Aiming to enhance Spectral Efficiency (SE) alone using CR may lead to huge power consumption and thus increase the overall cost of network operation. Hence, effort must be taken to maintain a tradeoff between SE and Energy Efficiency (EE). In this work, a novel technique, called Superior Population Generation Algorithm (SPGA), is developed to solve the crucial problem of SE-EE tradeoff for a Fifth generation (5 G) heterogeneous network which uses key future technologies such as CR and Carrier Aggregation (CA). The proposed technique performs optimum resource allocation, by modifying regular Genetic Algorithm (GA) to incorporate the capabilities of conventional techniques. SPGA follows a multi objective optimization approach towards obtaining SE-EE tradeoff, rather than focusing on SE alone. Simulation results show that SPGA outperforms conventional methods and regular GA in terms of SE-EE tradeoff with about 5% and 10% better EE for the same SE respectively. It is also observed that SPGA converges to the Pareto optimal solutions, much faster using only 25% of iterations required for regular GA. All simulations in this work are performed according to 3GPP specifications for 5 G communication networks. Also, power consumption of various base station components is also considered in addition to the transmit power to make the EE calculations further close to reality.

由于认知无线电（CR）技术允许次要用户（SU）和主要用户（PU）网络共存，因此最近在未来的通信网络中变得越来越流行。与交织和覆盖相比，底层操作模式最适合实际情况，因为它有助于以最小的开销实现PU-SU共存。仅使用CR来提高频谱效率（SE）的目的可能会导致巨大的功耗，从而增加网络运营的总体成本。因此，必须努力保持SE与能源效率（EE）之间的平衡。在这项工作中，开发了一种称为高级人口生成算法（SPGA）的新技术，以解决第五代（5 G）异构网络的SE-EE折衷的关键问题，该异构网络使用了未来的关键技术，例如CR和载波聚合（CA）。所提出的技术通过修改常规遗传算法（GA）以合并常规技术的功能来执行最佳的资源分配。SPGA遵循多目标优化方法来获得SE-EE权衡，而不是仅关注SE。仿真结果表明，在SE-EE权衡方面，SPGA优于常规方法和常规GA，相同SE的EE分别提高了5％和10％。还可以观察到，SPGA仅使用常规GA所需的25％迭代即可收敛到Pareto最优解。这项工作中的所有仿真都是根据5GPP通信网络的3GPP规范执行的。也，