Marcus Z. Comiter1
, Michael B. Crouse1
and H. T. Kung1
1
John A. Paulson School of Engineering and Applied Science
Harvard University
Cambridge,
MA 02138
Next-generation wireless networks such as 5G and 802.11ad networks will use millimeter waves operating
at 28GHz, 38GHz, or higher frequencies to deliver unprecedentedly high data rates, e.g., 10 gigabits per
second. However, millimeter waves must be used directionally with narrow beams in order to overcome the
large attenuation due to their higher frequency. To achieve high data rates in a mobile setting,
communicating nodes need to align their beams dynamically, quickly, and in high resolution. We propose a
data-driven, deep neural network (DNN) approach to provide robust localization for beam alignment,
using a lower frequency spectrum (e.g., 2.4 GHz). The proposed DNN-based localization methods use the
angle of arrival derived from phase differences in the signal received at multiple antenna arrays to infer the
location of a mobile node. Our methods differ from others that use DNNs as a black box in that the
structure of our neural network model is tailored to address difficulties associated with the domain, such as
collinearity of the mobile node with antenna arrays, fading and multipath. We show that training our
models requires a small number of sample locations, such as 30 or fewer, making the proposed methods
practical. Our specific contributions are: (1) a structured DNN approach where the neural network
topology reflects the placement of antenna arrays, (2) a simulation platform for generating training and
evaluation data sets under multiple noise models, and (3) demonstration that our structured DNN approach
improves localization under noise by up to 25% over traditional off-the-shelf DNNs, and can achieve submeter
accuracy in a real-world experiment.
KEYWORDS
Millimeter wave, 5G, 802.11ad, Localization, Mobile networks, Machine learning, Deep Neural Networks
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