Lohan, Alén-Savikko, Chen, Järvinen, Leppäkoski, Kuusniemi, and Korpisaari
286
specify any particular positioning technology that has to be used with 5G devices; they
only focus on the target accuracy of less than 1 m in all suburban environments where
5G is available [4].
However, the 5G research papers dealing with positioning focus mostly on TOA
[10,55], TDOA [17] and DOA [10,55,91]. Very few papers also mentioned the RSS in the
context of 5G positioning [43], while some others also talked about Assisted‐GNSS
(AGNSS) in 5G [60]. In addition, Phase Difference of Arrival (PDOA) has been mentioned in the context of wireless localization [15,61].
These various positioning mechanisms are summarized in Table 13.2 and described
briefly below.
In TOA and TDOA, the position estimate is based on the distance between the receiver
and at least 2 (TDOA) or 3 (TOA) transmitters or Access Nodes (AN). TOA measurements require that the clocks of the ANs and the mobile device are synchronized. In
many cases, it is difficult to synchronize accurately mobile devices to the time of ANs.
However, if the ANs can be synchronized together, the problem of clock difference
between the mobile and the ANs can be circumvented by adding one more AN and TOA
to the system. This allows estimation of the clock difference together with the position
coordinates. TDOA does not require the clock of the mobile to be synchronized with
these. With TDOA, we obtain the difference between the distances to two ANs.
An example of the Cramer Rao Lower Bound (CRLB) for the delay error standard
deviation in Additive White Gaussian Noise (AWGN) channel, as a function of the
available bandwidth, is shown in Figure 13.4 (a). Clearly, the higher the bandwidth, the
better delay tracking accuracy we can achieve. In 5G, as the bandwidths are expected to
be of the order of tens or hundreds of MHz, the TOA‐based estimators have the ability
to reach very fine accuracy, even at ns level for standard deviation of error. One problem
in TOA and TDOA estimation is the presence of Non Line of Sight (NLOS) situations.
There are many solutions to perform NLOS detection and to eliminate NLOS paths [18,
46, 50, 88]. In addition to the measurement accuracy of TOA/TDOA, the positioning
accuracy is also affected by the measurement geometry, that is, how the ANs and the
mobile are geometrically located with respect to each other.
The DOA is estimated by measuring the difference in the received phase at each element
of the antenna array. Antenna arrays can be configured into various types of geometry.
Table 13.2 Summary of positioning mechanisms in 5G.
Positioning
mechanism
Time of Arrival
Time difference
of arrival
Direction of
Arrival
Received Signal
Strength
Assisted and cloud
Global Navigation
Satellite System
Abbreviation TOA
TDOA
DOA
RSS
AGNSS and
CGNSS
Underlying
idea
Trilateration
(intersection
of circles)
Trilateration
(intersection
of hyperbolas)
Phase
differences
fingerprinting,
path‐loss
statistical
models
GNSS plus
cellular‐based or
cloud‐based
information
Illustration,
if shown
Figure 13.3 a) Figure 13.3 b) Figure 13.4 b) Figure 13.5
Figure 13.6
286
specify any particular positioning technology that has to be used with 5G devices; they
only focus on the target accuracy of less than 1 m in all suburban environments where
5G is available [4].
However, the 5G research papers dealing with positioning focus mostly on TOA
[10,55], TDOA [17] and DOA [10,55,91]. Very few papers also mentioned the RSS in the
context of 5G positioning [43], while some others also talked about Assisted‐GNSS
(AGNSS) in 5G [60]. In addition, Phase Difference of Arrival (PDOA) has been mentioned in the context of wireless localization [15,61].
These various positioning mechanisms are summarized in Table 13.2 and described
briefly below.
In TOA and TDOA, the position estimate is based on the distance between the receiver
and at least 2 (TDOA) or 3 (TOA) transmitters or Access Nodes (AN). TOA measurements require that the clocks of the ANs and the mobile device are synchronized. In
many cases, it is difficult to synchronize accurately mobile devices to the time of ANs.
However, if the ANs can be synchronized together, the problem of clock difference
between the mobile and the ANs can be circumvented by adding one more AN and TOA
to the system. This allows estimation of the clock difference together with the position
coordinates. TDOA does not require the clock of the mobile to be synchronized with
these. With TDOA, we obtain the difference between the distances to two ANs.
An example of the Cramer Rao Lower Bound (CRLB) for the delay error standard
deviation in Additive White Gaussian Noise (AWGN) channel, as a function of the
available bandwidth, is shown in Figure 13.4 (a). Clearly, the higher the bandwidth, the
better delay tracking accuracy we can achieve. In 5G, as the bandwidths are expected to
be of the order of tens or hundreds of MHz, the TOA‐based estimators have the ability
to reach very fine accuracy, even at ns level for standard deviation of error. One problem
in TOA and TDOA estimation is the presence of Non Line of Sight (NLOS) situations.
There are many solutions to perform NLOS detection and to eliminate NLOS paths [18,
46, 50, 88]. In addition to the measurement accuracy of TOA/TDOA, the positioning
accuracy is also affected by the measurement geometry, that is, how the ANs and the
mobile are geometrically located with respect to each other.
The DOA is estimated by measuring the difference in the received phase at each element
of the antenna array. Antenna arrays can be configured into various types of geometry.
Table 13.2 Summary of positioning mechanisms in 5G.
Positioning
mechanism
Time of Arrival
Time difference
of arrival
Direction of
Arrival
Received Signal
Strength
Assisted and cloud
Global Navigation
Satellite System
Abbreviation TOA
TDOA
DOA
RSS
AGNSS and
CGNSS
Underlying
idea
Trilateration
(intersection
of circles)
Trilateration
(intersection
of hyperbolas)
Phase
differences
fingerprinting,
path‐loss
statistical
models
GNSS plus
cellular‐based or
cloud‐based
information
Illustration,
if shown
Figure 13.3 a) Figure 13.3 b) Figure 13.4 b) Figure 13.5
Figure 13.6