In densely populated urban centers, planning optimized capacity for the fifth-generation (5G) and beyond wireless networks is a challenging task. In this paper, we propose a mathematical framework for the planning capacity of a 5G and beyond wireless networks. We considered a single-hop wireless network consists of base stations (BSs), relay stations (RSs), and user equipment (UEs). Wireless network planning (WNP) should decide the placement of BSs and RSs to the candidate sites and decide the possible connections among them and their further connections to UEs. The objective of the planning is to minimize the hardware and operational cost while planning capacity of a 5G and beyond wireless networks. The formulated WNP is an integer programming problem. Finding an optimal solution by using exhaustive search is not practical due to the demand for high computing resources. As a practical approach, a new population-based meta-heuristic algorithm is proposed to find a high-quality solution. The proposed discrete fireworks algorithm (DFWA) uses an ensemble of local search methods: insert, swap, and interchange. The performance of the proposed DFWA is compared against the low-complexity biogeography-based optimization (LC-BBO), the discrete artificial bee colony (DABC), and the genetic algorithm (GA). Simulation results and statistical tests demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources.

在人口稠密的城市中心，为第五代（5G）和无线网络规划最佳容量是一项艰巨的任务。在本文中，我们为5G和更多无线网络的规划能力提出了一个数学框架。我们认为单跳无线网络由基站（BS），中继站（RS）和用户设备（UE）组成。无线网络规划（WNP）应该确定BS和RS到候选站点的位置，并确定它们之间可能的连接以及它们与UE的进一步连接。规划的目的是在规划5G及更高无线网络容量的同时，最大程度地减少硬件和运营成本。制定的WNP是整数编程问题。由于需要大量的计算资源，因此使用穷举搜索来找到最佳解决方案是不切实际的。作为一种实用的方法，提出了一种新的基于种群的元启发式算法来寻找高质量的解决方案。提出的离散烟花算法（DFWA）使用了一系列本地搜索方法：插入，交换和交换。将拟议的DFWA的性能与基于低复杂度生物地理的优化（LC-BBO），离散人工蜂群（DABC）和遗传算法（GA）进行了比较。仿真结果和统计测试表明，该算法可以较适度地找到计算量适中的优质解决方案。提出的离散烟花算法（DFWA）使用了一系列本地搜索方法：插入，交换和交换。将拟议的DFWA的性能与基于低复杂度生物地理的优化（LC-BBO），离散人工蜂群（DABC）和遗传算法（GA）进行了比较。仿真结果和统计测试表明，该算法可以较适度地找到计算量适中的优质解决方案。提出的离散烟花算法（DFWA）使用了一系列本地搜索方法：插入，交换和交换。将拟议的DFWA的性能与基于低复杂度生物地理的优化（LC-BBO），离散人工蜂群（DABC）和遗传算法（GA）进行了比较。仿真结果和统计测试表明，该算法可以较适度地找到计算量适中的优质解决方案。