12 research outputs found
On Deep Learning-based Massive MIMO Indoor User Localization
We examine the usability of deep neural networks for multiple-input
multiple-output (MIMO) user positioning solely based on the orthogonal
frequency division multiplex (OFDM) complex channel coefficients. In contrast
to other indoor positioning systems (IPSs), the proposed method does not
require any additional piloting overhead or any other changes in the
communications system itself as it is deployed on top of an existing OFDM MIMO
system. Supported by actual measurements, we are mainly interested in the more
challenging non-line of sight (NLoS) scenario. However, gradient descent
optimization is known to require a large amount of data-points for training,
i.e., the required database would be too large when compared to conventional
methods. Thus, we propose a twostep training procedure, with training on
simulated line of sight (LoS) data in the first step, and finetuning on
measured NLoS positions in the second step. This turns out to reduce the
required measured training positions and thus, reduces the effort for data
acquisition.Comment: submitted to SPAWC 201
DNN-based Localization from Channel Estimates: Feature Design and Experimental Results
We consider the use of deep neural networks (DNNs) in the context of channel
state information (CSI)-based localization for Massive MIMO cellular systems.
We discuss the practical impairments that are likely to be present in practical
CSI estimates, and introduce a principled approach to feature design for
CSI-based DNN applications based on the objective of making the features
invariant to the considered impairments. We demonstrate the efficiency of this
approach by applying it to a dataset constituted of geo-tagged CSI measured in
an outdoors campus environment, and training a DNN to estimate the position of
the UE on the basis of the CSI. We provide an experimental evaluation of
several aspects of that learning approach, including localization accuracy,
generalization capability, and data aging.Comment: Submitted to Globecom 202
Towards Practical Indoor Positioning Based on Massive MIMO Systems
We showcase the practicability of an indoor positioning system (IPS) solely
based on Neural Networks (NNs) and the channel state information (CSI) of a
(Massive) multiple-input multiple-output (MIMO) communication system, i.e.,
only build on the basis of data that is already existent in today's systems. As
such our IPS system promises both, a good accuracy without the need of any
additional protocol/signaling overhead for the user localization task. In
particular, we propose a tailored NN structure with an additional phase branch
as feature extractor and (compared to previous results) a significantly reduced
amount of trainable parameters, leading to a minimization of the amount of
required training data. We provide actual measurements for indoor scenarios
with up to 64 antennas covering a large area of 80m2. In the second part,
several robustness investigations for real-measurements are conducted, i.e.,
once trained, we analyze the recall accuracy over a time-period of several
days. Further, we analyze the impact of pedestrians walking in-between the
measurements and show that finetuning and pre-training of the NN helps to
mitigate effects of hardware drifts and alterations in the propagation
environment over time. This reduces the amount of required training samples at
equal precision and, thereby, decreases the effort of the costly training data
acquisitionComment: Submitted to VTC2019 Fal
Improving Channel Charting with Representation-Constrained Autoencoders
Channel charting (CC) has been proposed recently to enable logical
positioning of user equipments (UEs) in the neighborhood of a multi-antenna
base-station solely from channel-state information (CSI). CC relies on
dimensionality reduction of high-dimensional CSI features in order to construct
a channel chart that captures spatial and radio geometries so that UEs close in
space are close in the channel chart. In this paper, we demonstrate that
autoencoder (AE)-based CC can be augmented with side information that is
obtained during the CSI acquisition process. More specifically, we propose to
include pairwise representation constraints into AEs with the goal of improving
the quality of the learned channel charts. We show that such
representation-constrained AEs recover the global geometry of the learned
channel charts, which enables CC to perform approximate positioning without
global navigation satellite systems or supervised learning methods that rely on
extensive and expensive measurement campaigns.Comment: Presented at the 20th IEEE International Workshop on Signal
Processing Advances in Wireless Communications (SPAWC), 201
Deep Learning-based Symbolic Indoor Positioning using the Serving eNodeB
This paper presents a novel indoor positioning method designed for
residential apartments. The proposed method makes use of cellular signals
emitting from a serving eNodeB which eliminates the need for specialized
positioning infrastructure. Additionally, it utilizes Denoising Autoencoders to
mitigate the effects of cellular signal loss. We evaluated the proposed method
using real-world data collected from two different smartphones inside a
representative apartment of eight symbolic spaces. Experimental results verify
that the proposed method outperforms conventional symbolic indoor positioning
techniques in various performance metrics. To promote reproducibility and
foster new research efforts, we made all the data and codes associated with
this work publicly available.Comment: - accepted paper (ICMLA 2020) - dataset and code:
https://doi.org/10.6084/m9.figshare.13010387.v
Reducing the Complexity of Fingerprinting-Based Positioning using Locality-Sensitive Hashing
Localization of wireless transmitters based on channel state information
(CSI) fingerprinting finds widespread use in indoor as well as outdoor
scenarios. Fingerprinting localization first builds a database containing CSI
with measured location information. One then searches for the most similar CSI
in this database to approximate the position of wireless transmitters. In this
paper, we investigate the efficacy of locality-sensitive hashing (LSH) to
reduce the complexity of the nearest neighbor-search (NNS) required by
conventional fingerprinting localization systems. More specifically, we propose
a low-complexity and memory efficient LSH function based on the sum-to-one
(STOne) transform and use approximate hash matches. We evaluate the accuracy
and complexity (in terms of the number of searches and storage requirements) of
our approach for line-of-sight (LoS) and non-LoS channels, and we show that LSH
enables low-complexity fingerprinting localization with comparable accuracy to
methods relying on exact NNS or deep neural networks
Siamese Neural Networks for Wireless Positioning and Channel Charting
Neural networks have been proposed recently for positioning and channel
charting of user equipments (UEs) in wireless systems. Both of these approaches
process channel state information (CSI) that is acquired at a multi-antenna
base-station in order to learn a function that maps CSI to location
information. CSI-based positioning using deep neural networks requires a
dataset that contains both CSI and associated location information. Channel
charting (CC) only requires CSI information to extract relative position
information. Since CC builds on dimensionality reduction, it can be implemented
using autoencoders. In this paper, we propose a unified architecture based on
Siamese networks that can be used for supervised UE positioning and
unsupervised channel charting. In addition, our framework enables
semisupervised positioning, where only a small set of location information is
available during training. We use simulations to demonstrate that Siamese
networks achieve similar or better performance than existing positioning and CC
approaches with a single, unified neural network architecture.Comment: Presented at Allerton 2019; 8 page
Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems
In this paper, we consider the user positioning problem in the massive
multiple-input multiple-output (MIMO) orthogonal frequency-division
multiplexing (OFDM) system with a uniform planner antenna (UPA) array. Taking
advantage of the UPA array geometry and wide bandwidth, we advocate the use of
the angle-delay channel power matrix (ADCPM) as a new type of fingerprint to
replace the traditional ones. The ADCPM embeds the stable and stationary
multipath characteristics, e.g. delay, power, and angle in the vertical and
horizontal directions, which are beneficial to positioning. Taking ADCPM
fingerprints as the inputs, we propose a novel three-dimensional (3D)
convolution neural network (CNN) enabled learning method to localize users' 3D
positions. In particular, such a 3D CNN model consists of a convolution
refinement module to refine the elementary feature maps from the ADCPM
fingerprints, three extended Inception modules to extract the advanced feature
maps, and a regression module to estimate the 3D positions. By intensive
simulations, the proposed 3D CNN-enabled positioning method is demonstrated to
achieve higher positioning accuracy than the traditional searching-based ones,
with reduced computational complexity and storage overhead, and the ADCPM
fingerprints are more robust to noise contamination
A Survey on Deep-Learning based Techniques for Modeling and Estimation of MassiveMIMO Channels
\textit{Why does the literature consider the channel-state-information (CSI)
as a 2/3-D image? What are the pros-and-cons of this consideration for
accuracy-complexity trade-off?} Next generations of wireless communications
require innumerable disciplines according to which a low-latency, low-traffic,
high-throughput, high spectral-efficiency and low energy-consumption are
guaranteed. Towards this end, the principle of massive multi-input multi-output
(MaMIMO) is emerging which is conveniently deployed for millimeter wave
(mmWave) bands. However, practical and realistic MaMIMO transceivers suffer
from a huge range of challenging bottlenecks in design the majority of which
belong to the issue of channel-estimation. Channel modeling and prediction in
MaMIMO particularly suffer from computational complexity due to a high number
of antenna sets and supported users. This complexity lies dominantly upon the
feedback-overhead which even degrades the pilot-data trade-off in the uplink
(UL)/downlink (DL) design. This comprehensive survey studies the novel
deep-learning (DLg) driven techniques recently proposed in the literature which
tackle the challenges discussed-above - which is for the first time. In
addition, we consequently propose 7 open trends e.g. in the context of the lack
of Q-learning in MaMIMO detection - for which we talk about a possible solution
to the saddle-point in the 2-D pilot-data axis for a \textit{Stackelberg game}
based scenario.Comment: IEEE Journal
A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals
The last few decades have witnessed a growing interest in location-based
services. Using localization systems based on Radio Frequency (RF) signals has
proven its efficacy for both indoor and outdoor applications. However,
challenges remain with respect to both complexity and accuracy of such systems.
Machine Learning (ML) is one of the most promising methods for mitigating these
problems, as ML (especially deep learning) offers powerful practical
data-driven tools that can be integrated into localization systems. In this
paper, we provide a comprehensive survey of ML-based localization solutions
that use RF signals. The survey spans different aspects, ranging from the
system architectures, to the input features, the ML methods, and the datasets.
A main point of the paper is the interaction between the domain knowledge
arising from the physics of localization systems, and the various ML
approaches. Besides the ML methods, the utilized input features play a major
role in shaping the localization solution; we present a detailed discussion of
the different features and what could influence them, be it the underlying
wireless technology or standards or the preprocessing techniques. A detailed
discussion is dedicated to the different ML methods that have been applied to
localization problems, discussing the underlying problem and the solution
structure. Furthermore, we summarize the different ways the datasets were
acquired, and then list the publicly available ones. Overall, the survey
categorizes and partly summarizes insights from almost 400 papers in this
field.
This survey is self-contained, as we provide a concise review of the main ML
and wireless propagation concepts, which shall help the researchers in either
field navigate through the surveyed solutions, and suggested open problems