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Exact and Approximation Algorithms for Optimal Equipment Selection in Deploying In-Building Distributed Antenna Systems
ETH, Switzerland.
ETH, Switzerland.
Lund University, Sweden.
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
2015 (English)In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, Vol. 14, no 4, 702-713 p.Article in journal (Refereed) Published
Abstract [en]

We consider a combinatorial optimization problemin passive In-Building Distributed Antenna Systems (IB-DAS) deployment for indoor mobile broadband service. These systems have a tree topology, in which a central base station is connected to a number of antennas located at tree leaves via cables represented by the tree edges. Each inner node corresponds to a power equipment, of which the available types differ in the number of output ports and/or by power gain at the ports. This paper focuses on the equipment selection problemthat amounts to, for a given passive DAS tree topology, selecting a power equipment type for each inner node and assigning the outgoing edges of the node to the equipment ports. The performance metric is the power deviation at the antennas from the target values. We consider as objective function the minimization of either the total or the largest power deviation over all antennas. Our contributions are the development of exact pseudo-polynomial time algorithms and (additive) fully-polynomial time approximation schemes for both objectives. Numerical results are provided to illustrate the algorithms. We also extend some results to account for equipment cost.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2015. Vol. 14, no 4, 702-713 p.
Keyword [en]
Wireless communication; combinatorial algorithms; trees
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:liu:diva-117649DOI: 10.1109/TMC.2014.2331976ISI: 000352569000003OAI: oai:DiVA.org:liu-117649DiVA: diva2:811557
Note

Funding Agencies|Linkoping-Lund Excellence Center in Information Technology (ELLIIT); European Marie Curie IRSES Project WiNDOW

Available from: 2015-05-12 Created: 2015-05-06 Last updated: 2017-12-04

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