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A Multi-objective Design of In-Building Distributed Antenna System Using Evolutionary Algorithms

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Artificial Intelligence XXXVI (SGAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11927))

Abstract

The increasing data traffic inside buildings requires maintaining good cellular network coverage for indoor mobile users. Passive In-building Distributed Antenna System (IB-DAS) is one of the most efficient methods to provide an indoor solution that meets the signal strength requirements. It is a network of spatially distributed antennas in a building connected to telephone rooms which are then connected to a Base Transmission Station (BTS). These connections are established through passive coaxial cables and splitters. The design of IB-DAS is considered to be challenging due to the power-sharing property resulting in two contradicting objectives: minimizing the power usage at the BTS (long-term cost) and minimizing the design components cost (short-term cost). Different attempts have been made in the literature to solve this problem. Some of them are either lacking the consideration of all necessary aspects or facing scalability issues. Additionally, most of these attempts translate the IB-DAS design into a mono-objective problem, which leads to a challenging task of determining a correct combined objective function with justified weighting factors associated with each objective. Moreover, these approaches do not produce multiple design choices which may not be satisfactory in practical scenarios. In this paper, we propose a multi-objective algorithm for designing IB-DAS. The experimental results show the success of this algorithm to achieve our industrial partner’s requirement of providing different design options that cannot be achieved using mono-objective approaches.

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Correspondence to Khawla AlShanqiti .

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AlShanqiti, K., Poon, K., Shakya, S., Sleptchenko, A., Ouali, A. (2019). A Multi-objective Design of In-Building Distributed Antenna System Using Evolutionary Algorithms. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXVI. SGAI 2019. Lecture Notes in Computer Science(), vol 11927. Springer, Cham. https://doi.org/10.1007/978-3-030-34885-4_20

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  • DOI: https://doi.org/10.1007/978-3-030-34885-4_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34884-7

  • Online ISBN: 978-3-030-34885-4

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