Designing Passive Indoor Distributed Antenna System with Practical Constraints Using Binary Encoding With the continuous increase in data traffic in buildings, there is an increasing demand for good cellular network coverage in indoor environments. Passive InBuilding Distributed Antenna System (IBDAS) has been widely used as a dedicated indoor solution for the required coverage. In IBDAS, spatially separated antennas are connected to a base station through coaxial cables and splitters. Due to the consideration of many factors such as types of splitters and the solution structure of IBDAS, many different approaches to produce optimal or near optimal design have been reported in the literature. However, some of them encounter the scalability issue, while others require a dedicated representation of the problem with highly customized operations. In this paper, a general binary tree representation based on Genetic Algorithm (GA) for designing IBDAS has been devised. The experimental results illustrate that this representation enhances the computational performance and yields lower costs for buildings with higher floors. In addition, it produces neater cabling solutions due to the intrinsic grouping mechanism, which is highly required in practical scenarios.

Efficient Optimal Backhaulaware Placement of Multiple DroneCells Based on Genetic Algorithm * Dronecell is a promising solution for providing GSM3GLTE5G mobile networks to victims and rescue teams in disasteraffected areas. One of the challenges that dronecells are facing is the limitation of reliable backhaul links. In this paper, we study the optimal placement problem of a group of dronecells deployed in a disaster area with limited backhaul communication ranges, aims to maximize the number of served users. Two approaches, exhaustive search algorithm and a computationally efficient genetic algorithm are proposed, and the optimal 2D backhaulaware placements of multiple dronecells are found for each approach. We also introduce a restartstrategy to enhance searching efficiency and avoid local optimums for the proposed genetic algorithm. Simulations show that the proposed genetic algorithm can save the computing time up to 99.927% compared with the exhaustive search algorithm, and the restartstrategy helps the probability of finding the global optimum by the proposed genetic algorithm increased from 12% to 92%.