URSI GASS 2021, Rome, Italy, 28 August – 4 September 2021
MISO Measurements in Indoor Environments at 25 GHz
Mohamed Abdulali, Amar Al-Jzari and Sana Salous
Centre for Communications Systems, Department of Engineering, Durham University, Durham, UK, DH1 3LE
This paper presents the results of parameter measurements
in two indoor environments for both line of sight (LoS) and
non-line of sight (NLoS) scenarios at 25 GHz using
multiple directional antennae at the transmitter and a single
omnidirectional antenna at the receiver (MISO) with the
custom-designed channel sounder developed at Durham
University. The data are analysed to estimate the K-factor,
path loss parameters and root mean square (RMS) delay
spread for each scenario. The 50% and 90% values of the
RMS delay spread values for a 20 dB threshold are
presented for both scenarios.
In indoor wireless communication environments, the
received signal is comprised of different MPCs, due to
diffraction, reflection or scattering, depending on the
environment. This paper presents a description of
measurements using a circular array of 8 directional
antennae at the transmitter and an omnidirectional antenna
at the receiver in a corridor and an office environment with
desks and computers in both LoS and NLoS scenarios
centred at 25 GHz which is one of the frequency bands
identified in the World Radiocommunications Conference
in 2019 (WRC19) for 5G networks.
Several parameters have been estimated from the
measurements to characterise the channel parameters
which include the Rician K-factor, path loss and RMS
The measurements were performed using the chirp or
frequency-modulated continuous wave (FMCW) channel
sounder, developed at Durham University,  with new RF
heads operating in the 24.5-30 GHz band with a maximum
bandwidth of 3 GHz. The transmitter RF head used in the
current measurements consists of 8 transmitters feeding an
antenna array of eight directional antennas mounted on a
circle to provide full coverage in azimuth. An eight-way
switch is deployed to transmit sequentially with a gap to
identify the sequence of transmission at the receiver. The
RF heads at the receiver consisted of a single receiver with
an omni-directional antenna. Table 1. gives the parameters
of the measurements where the transmitted signal overed
the full 3 GHz bandwidth with the analysis bandwidth of 1
GHz. Two scenarios were measured in a LoS and NLoS set
up which represent a typical corridor and an office
environment, as shown in Figure 1.
Table 1. Measurement set-up parameters.
Frequency range (GHz)
Sweep rate (kHz)
Sampling rate (MHz)
Nominal 3 dB beamwidth
Tx antenna height
Rx antenna height
Figure 1. Layout of measurements: (a) corridor, (b) office.
3 Measurement analysis
At each location, the omnidirectional antenna receives 8
signals from the 8 transmitters antennas periodically over
one second, then the data were processed to estimate 8
power delay profiles (PDPs) for each location as shown in
figure 2. Since the transmitter is a circular array of 8
directional antennas, In LoS scenario only one antenna has
met the condition of the line of sight with the receiver while
the other 7 antennas considered as obstructed line of sight.
In the NLoS scenario, there is no antenna of 8 transmitters
that has a line of sight with the receiver
Figure 2. The received signals from 8 antennas corridor
environment at location no 23: (a) LoS, (b) NLoS.
Figure 3 illustrates the PDPs in the corridor environment
for the LoS, OLoS and the NLoS scenarios versus the 3D
distance, taking into account the height of the transmitter
and the receiver antennas. The LoS gives the strongest PDP
of the 8 PDPs at each location while the other 7 PDPs were
synthesised to create the OLoS scenario, the synthesisation
methodology which was utilised is firstly to convert the
PDPs into normal scale and then apply equations 1 and 2,
while in NLoS all 8 PDPs were synthesised [2-3].
Figure 3. PDP vs location for the corridor environment:
(a) LoS, (b) OLoS and (c) NLoS.
The PDPs were then used to estimate the rms delay spread
for 20 dB using equation 3 
Following calibration, the path loss was estimated using
two models: the close-in path loss model, and the floating
intercept path loss model, given by equations 4 and 5
where is 1 m and L () is the corresponding free-space
where both the distance coefficient α and the intercept
coefficient β are estimated from the measurements.
The data were also analyzed to estimate the fading of the
channel versus distance by estimating the K-factor using
the method of moments. Since the measurements were
performed stationary over one second the K factor was
estimated from the instantaneous frequency transfer
function as in equations 6-11  and then taking the
one second average.
4 Estimated channel parameters Results
Figure 4 displays the estimated path loss from the computer
foyer and the corridor for both the close-in and the floating
intercept models and Table 2 summarises the values of the
estimated path loss parameters.
Figure 4. Path loss: (a) foyer, (b) corridor.
Table 2. Path loss parameters for the CI and FI
models for LoS and NLoS (corridor & computer foyer).
(α, β, σ))
(1.58, 64.17, 2.17)
(0.93, 73.06, 2.35)
(α, β, σ))
(1.64, 70.78, 1.07)
(1.36, 71.41, 1.56)
(α, β, σ))
(2.54, 67.75, 1.09)
(1.89, 69.32, 1.89)
The tables indicates that the close-in model has a higher
standard deviation than the floating intercept as it takes free
space path loss at 1 meter as a reference, regardless of the
type and size of the environment. In the NLoS scenarios,
the material of the walls and objects that block the line of
sight have significant impact on the path loss. Therefore,
the floating intercept data might be more suitable for an
indoor path loss estimation.
Figure 5. displays the linear fit of the K-factor for both
environments, the corridor and the office for LoS scenario.
Figure 5. K-factor vs distance LoS.
In the LoS scenario, the K-factor is seen to be inversely
proportional to distance which is due to the increased loss
of the LoS component which is subject to free-space path
loss with distance while the reflected components
comparatively increased. the K-factor decreased as the
Figure 6 illustrates the cumulative distribution function
(CDF) of the RMS delay spread with a summary of the
50% and the 90% values given in Table 3 for the LoS,
OLoS and NLoS scenarios for both environments. For each
environment, the RMS delay spread results showed that the
lowest values are in the LoS scenario where the dominant
LoS component was significantly stronger than the
scattered and reflected components leading to a smaller
delay spread value than the OLoS and NLoS cases. For the
corridor environment the OLoS case gave smaller values of
delay spread than the NLoS whereas in the open Foyer
environment the values were comparable.
Figure 6. RMS delay spread.
Table 3. RMS delay spread values.
Measurements at 25 GHz were conducted in two
environments using an 8 transmit by 1 receive antenna
configuration in LoS and NLoS scenarios. The LoS
measurements were classified as LoS which represent the
strongest received signal from the 8 transmit antennas,
OLoS which represent the received signal from the
remaining 7 antennas which were not in the direct LoS of
the receiver. In the NLoS measurements, the received
signal from the 8 transmit antennas were combined to
synthesise the PDP. The data were processed to estimate
the path loss parameters using the CI and FI models, the
Rician K-factor, and the RMS delay spread. The
relationship between distance and the K-factor is found to
be linear with a higher slope for Corridor than Foyer.
The path loss results indicate that both CI model has a
higher standard deviation than the FI model indicating that
the FI is more appropriate for the modelling of path loss in
The RMS delay spread results show that the values are
directly proportional to the size of the environment.
The authors would like to acknowledge the support of
WaveComBE project, under Horizon 2020 research and
innovation program, grant agreement No. 766231. The
original sounder was developed under the EPSRC grant
PATRICIAN EP/I00923X/1 and further funding from
EPSRC Impact Acceleration Account, Ofcom, UK, and
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