Evolutionary Algorithms for 5G Multi-Tier Radio Access Network Planning

With the ever-increasing traffic demand of wireless users, resulting from huge deployment Internet-of-Things (IoT) devices and emergence smart city applications requiring ultra-low latency networks, Fifth Generation (5G) cellular networks have been introduced as a revolutionary broadband technology to boost quality service mobile users. In this paper, we investigate planning process for 5G radio access network having mmWave Micro Remote Radio Units (mRRUs) on top sub-6 GHz Macro (MRRUs). We rely proper channel models link budgets well Urban Macro-cells (UMa) Micro-cells (UMi) characteristics carefully formulate optimization problem. aim jointly determine minimum number MRRUs mRRUs install find their locations in given geographical area while fulfilling coverage user constraints. order solve process, propose two-step where first employ low complexity meta-heuristic algorithm optimize RRUs followed by an iterative elimination method remove redundant cells. To evaluate performances proposed approach, conduct comparative study using Accelerated Particle Swarm Optimization Simulated Annealing. Simulations results UMa 28 UMi demonstrate ability approach achieve more than 98% with cell capacity outage rate, not exceeding 2%, different scenarios illustrate efficiency evolutionary algorithms solving NP-hard problem reasonable running time.