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Network Deployment for Maximal Energy Efficiency in Uplink with Multislope Path Loss
University of Pisa, Italy.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
University of Pisa, Italy.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5954-434x
2018 (English)In: IEEE Transactions on Green Communications and Networking, E-ISSN 2473-2400, Vol. 2, no 3, p. 735-750Article in journal (Refereed) Published
Abstract [en]

This work aims to design the uplink (UL) of a cellular network for maximal energy efficiency (EE). Each base station (BS) is randomly deployed within a given area and is equipped with M antennas to serve K user equipments (UEs). A multislope (distance-dependent) path loss model is considered and linear processing is used, under the assumption that channel state information is acquired by using pilot sequences (reused across the network). Within this setting, a lower bound on the UL spectral efficiency and a realistic circuit power consumption model are used to evaluate the network EE. Numerical results are first used to compute the optimal BS density and pilot reuse factor for a Massive MIMO network with three different detection schemes, namely, maximum ratio combining, zero-forcing (ZF) and multicell minimum mean-squared error. The numerical analysis shows that the EE is a unimodal function of BS density and achieves its maximum for a relatively small density of BS, irrespective of the employed detection scheme. This is in contrast to the single-slope (distance-independent) path loss model, for which the EE is a monotonic non-decreasing function of BS density. Then, we concentrate on ZF and use stochastic geometry to compute a new lower bound on the spectral efficiency, which is then used to optimize, for a given BS density, the pilot reuse factor, number of BS antennas and UEs. Closed-form expressions are computed from which valuable insights into the interplay between optimization variables, hardware characteristics, and propagation environment are obtained.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 2, no 3, p. 735-750
National Category
Telecommunications Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-161661DOI: 10.1109/TGCN.2018.2839346Scopus ID: 2-s2.0-85067528162OAI: oai:DiVA.org:liu-161661DiVA, id: diva2:1367932
Available from: 2019-11-05 Created: 2019-11-05 Last updated: 2019-11-12Bibliographically approved

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Verenzuela, DanielBjörnson, Emil

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