Application of Exact and Metaheuristic Algorithms to Optimize Indoor Distributed Antenna System

Abstract

The continuous growth in data traffic inside buildings requires maintaining good cellular network coverage for indoor mobile users. Passive In-building Distributed Antenna System (IB-DAS) is proved to be one of the most efficient networks to provide an indoor solution that meets the signal strength requirements. IB-DAS is a network of spatially separated antennas connected to telephone rooms, which are then connected to a Base Transmission Station (BTS). These connections are established through passive splitters, tappers and coaxial cables. The design of IB-DAS is challenging due to the consideration of many factors and the power-sharing property resulting in two contradicting objectives: minimizing the design components cost and minimizing the power related to the system. Different attempts have been made in the literature in order to solve this problem. Some of them are lacking the consideration of all the necessary practical aspects, and some others are facing scalability issues. In addition, most of these attempts translate the IB-DAS design into a single-objective problem, which leads to a critical task of determining a correct combined objective function with justified weighting factors associated with each objective. Therefore, these approaches do not produce multiple design choices which may not be satisfactory in practical scenarios. This research addresses the problem of IB-DAS design. It proposes a multi-objective algorithm-based approach for designing inter-floor IB-DAS. It also proposes a feasible MILP model that is used to assess the multi-objective algorithm performance. Experimental results from the multi-objective based approach demonstrate its success in providing multiple design options among the different tradeoff solutions to the decision-maker in a relatively short time. They also show that it is scalable and able to outperform the method applied to the same problem instance in the literature. Additionally, the comparison with the MILP model results illustrates the multi-objective approach's capability of finding the optimal solution when applied on a small scale.

Date of Award	Mar 2020
Original language	American English