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On RRH Placement for Multi-User Distributed Massive MIMO Systems
Author(s) -
Arin Minasian,
Raviraj S. Adve,
Shahram Shahbazpanahi,
Gary Boudreau
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2880149
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In a distributed massive multiple-input multiple-output (DM-MIMO) system, a large number of antennas are distributed across multiple remote radio heads (RRHs) within the coverage area. Distributing antennas is an appealing approach to provide fairly uniform coverage and enable macro-diversity. However, the careful design of the locations of the RRHs is crucial to achieve the potential gains of DM-MIMO systems. In this paper, we consider the optimal RRH placement problem to maximize the average rate in the downlink (DL) of a DM-MIMO system employing zero-forcing. To this end, we first develop a mathematically tractable, yet accurate expression for the DL rate. We then use this rate expression to formulate the optimal RRH placement problem. Due to the inherent complexity of this problem, we propose two sub-optimal, yet effective, RRH placement schemes. First, we consider a homogeneous propagation environment, ignoring the effects of any obstructions on the propagation characteristics of the wireless signal. Next, we consider RRH placement in a non-homogeneous propagation environment, specifically, in the presence of buildings in the coverage area. The proposed algorithms yield up to 21% improvement in the average rate and up to 72% improvement in the cell-edge rate compared to the state-of-the-art placement strategies.

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