ETSI TR 103 395 V1.1.2 (2021-06)

Smart Body Area Network (SmartBAN); Measurements and modelling of SmartBAN Radio Frequency (RF) environment

RTR/SmartBAN-0020

General Information

• Status: Not Published
• Technical Committee: SmartBAN - Smart Body Area Network
• Current Stage: 12 - Completion
• Due Date: 29-Jun-2021
• Completion Date: 10-Jun-2021

Standards Content (Sample)

TECHNICAL REPORT
Smart Body Area Network (SmartBAN);
Measurements and modelling of SmartBAN
Radio Frequency (RF) environment

2 ETSI TR 103 395 V1.1.2 (2021-06)
Reference
RTR/SmartBAN-0020
Keywords
MAC, measurement, network
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3 ETSI TR 103 395 V1.1.2 (2021-06)
Contents
Intellectual Property Rights . 5
Foreword . 5
Modal verbs terminology . 5
1 Scope . 6
2 References . 6
2.1 Normative references . 6
2.2 Informative references . 6
3 Definition of terms, symbols and abbreviations . 8
3.1 Terms . 8
3.2 Symbols . 8
3.3 Abbreviations . 9
4 Introduction and Background . 11
5 Coexistence . 11
5.0 Introduction . 11
5.1 Bands . 12
6 Measurements . 12
6.1 Background & Motivation . 12
6.2 Spectrum Occupancy Evaluations (SOEs) . 13
6.3 Measurement Campaigns . 15
6.3.0 Introduction. 15
6.3.1 Measurement campaigns in Oulu, Finland . 15
6.3.1.0 Introduction . 15
6.3.1.1 Daily Surgery SOEs (Campaign 1) . 15
6.3.1.2 Accident & Emergency Ward SOEs (Campaign 2) . 25
6.3.1.3 X-Ray & Radiology Ward SOEs (Campaign 3). 31
6.3.2 Analytical Stochastic Model for Spectrum Occupancy . 33
6.3.3 Extracting Mathematical Interference model . 36
6.3.4 Measurement Campaigns in Florence, Italy . 39
6.3.4.0 Introduction . 39
6.3.4.1 Occupancy . 40
6.3.4.1.0 Introduction . 40
6.3.4.1.1 Percentiles . 41
6.3.4.2 PDF . 42
6.3.4.3 Interference as a function of time and frequency . 43
6.3.4.4 Parameters characterizing the distribution . 44
6.3.4.5 Home and office environments . 44
6.3.4.6 Extract the mathematical model . 47
6.3.4.6.0 Introduction . 47
6.3.4.6.1 First results of CNIT-UNIFI . 47
6.4 Statistical model of the interference . 55
6.4.0 Introduction. 55
6.4.1 Cluster dimension . 56
6.4.2 Inter-arrival time . 57
6.4.3 Interfering cluster amplitude . 60
6.4.4 Conclusions. 62
6.5 Extracting the mathematical model of the interference . 62
6.6 Further investigations: a more accurate statistical model of the interference . 71
6.6.0 Introduction. 71
6.6.1 Accurate statistical models of the interference . 71
6.6.1.0 Introduction . 71
6.6.1.1 Time–Frequency Statistical Model of the Interference . 72
6.6.1.2 Cluster-Based Statistical Model of the Interference . 74
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4 ETSI TR 103 395 V1.1.2 (2021-06)
7 SmartBAN communication system simulator . 76
7.0 Introduction . 76
7.1 Getting started . 76
7.2 Simulator model . 80
7.2.0 Introduction. 80
7.2.1 Node. 80
7.3 Hub . 80
7.3.0 Introduction. 80
7.3.1 Simulation parameters . 81
7.4 PHY layer . 82
7.4.0 Introduction. 82
7.4.1 PHY transmitter . 82
7.4.2 Channel, interference and noise . 83
7.4.2.0 Introduction . 83
7.4.2.1 Interference . 83
7.4.3 PHY receiver . 84
7.5 MAC - Frame retransmission . 85
7.6 Verification results . 87
8 Simulation results . 87
8.0 Introduction . 87
8.1 Simulation parameters . 88
8.2 AWGN channel . 88
8.3 Fading channel . 90
8.4 Fading channel and interference . 92
8.5 Discussion . 94
Annex A: Spatial Sample Clustering Algorithm . 95
History . 98

ETSI
5 ETSI TR 103 395 V1.1.2 (2021-06)
Intellectual Property Rights
Essential patents
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BLUETOOTH is trademark registered and owned by Bluetooth SIG, Inc.
Foreword
This Technical Report (TR) has been produced by ETSI Technical Committee Smart Body Area Network (SmartBAN).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.
ETSI
6 ETSI TR 103 395 V1.1.2 (2021-06)
1 Scope
The present document specifies the state-of-the-art and the future investigations on coexistence for allowing Smart
Body Area Network (SmartBAN) devices to properly work and co-operate in the Industrial, Scientific and Medical
(ISM) band. Interference appears to be one of the major threats as well as coexistence with other existing systems
radiating in the same portion of the frequency spectrum. The present document describes the coexistence measurements
and analysis that need to be considered in order to specify the requirements for the SmartBAN compatible devices.

Figure 0: Scope of a SmartBAN
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] ETSI TS 103 326 (V1.1.1) (04-2015): "Smart Body Area Network (SmartBAN); Enhanced
Ultra-Low Power Physical Layer".
[i.2] Void.
[i.3] IEEE 802.11™: "IEEE Standard for Information technology--Telecommunications and
information exchange between systems Local and metropolitan area networks--Specific
requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)
Specifications".
ETSI
7 ETSI TR 103 395 V1.1.2 (2021-06)
[i.4] Valenta, V. (2010): "Survey on spectrum utilization in Europe: Measurements, analyses and
th
observations", 5 International Conference on Cognitive Radio Oriented Wireless Networks
Communications.
[i.5] ITU-R (2011): "ITU-R handbook for spectrum monitoring".
[i.6] Recommendation ITU-R SM.2256: "Spectrum occupancy measurements and evaluation".
[i.7] Recommendation ITU-R SM.2180 (2010): "Impact of ISM equipment on radio communication
services".
[i.8] Vuohtoniemi R.,Virk M. H., Hämäläinen M., Iinatti J., & Mäkela J.-P. (2015): "Stochastic Spectral
th
International
Occupancy Modeling: A Body Area Network Perspective in ISM Band", 9
Symposium on Medical Information & Communication Technology (ISMICT). Kamakura, Japan.
[i.9] J. J. Lehtomäki, e. a. (2012): "Energy detection based estimation of channel occupancy rate with
adaptive noise estimation", IEICE Transactions on Communications.
[i.10] Virk M. H., Vuohtoniemi R., Hämäläinen M., Iinatti J., & Mäkela J.-P. (2014): "Spectrum
Occupancy Evaluations at 2.35-2.50 GHz ISM Band in a Hospital Environment", International
Conference on Body Area Networks (BodyNets'14). London, UK.
[i.11] ETSI TS 103 325 (2015): "Smart Body Area Network (SmartBAN); Low Complexity Medium
Access Control (MAC) for SmartBAN".
[i.12] MATLAB, Product help, R2011b.
[i.13] Yazdandoost K.Y. and Sayrafian-Pour K.: "Channel Model for Body Area Network (BAN)" IEEE
P802.15-08-0780-09-0006, 2009.
[i.14] Proakis J.G.: "Digital Communications", McGraw-Hill, 2001.
[i.15] Griffin A.: "Coding CPFSK for Differential Demodulation", University of Canterbury
Christchurch, New Zealand, 2000.
[i.16] IEEE 802.15.6™ (2012): "IEEE Standard for Local and metropolitan area networks -- Part 15.6:
Wireless Body Area Networks".
[i.17] Rahman M., Elbadry M. and Harjani R.: "An IEEE 802.15.6 Standard Compliant 2.5 nJ/Bit
Multiband WBAN Transmitter Using Phase Multiplexing and Injection Locking" IEEE Journal of
Solid-State Circuits, Vol. 50, No. 5, May 2015, pp. 1126 -1136.
[i.18] L. Mucchi, R. Vuohtoniemi, H. Virk, A. Conti , Matti Hämäläinen, Jari Iinatti, and Moe Z. Win:
"Spectrum Occupancy and Interference Model Based on Network Experimentation in Hospital", in
IEEE Transactions on Wireless Communications, vol. 19, no. 9, pp. 5666-5675, September 2020,
doi: 10.1109/TWC.2020.2995116.
[i.19] P. C. Pinto and M. Z. Win, "Communication in a Poisson field of interferers-Part I: Interference
distribution and error probability" in IEEE Transactions on Wireless Communications, vol. 9, no.
7, pp. 2176-2186, July 2010.
[i.20] M. Z. Win and P. C. Pinto: "Communication in a Poisson field of interferers-Part II: Channel
capacity and interference spectrum" in IEEE Transactions on Wireless Communications, vol. 9,
no. 7, pp. 2187-2195, July 2010.
[i.21] P. C. Pinto, A. Giorgetti, M. Z. Win, and M. Chiani: "A stochastic geometry approach to
coexistence in heterogeneous wireless networks," IEEE Journal on Selected Areas in
Communications, vol. 27, no. 7, pp. 1268-1282, September 2009.
[i.22] A. Rabbachin, A. Conti, and M. Z. Win: "Wireless network intrinsic secrecy", IEEE/ACM
Transactions on Networking, vol. 23, no. 1, pp. 56-69, February 2015.
[i.23] M. Win, A. Rabbachin, J. Lee, and A. Conti: "Cognitive network secrecy with interference
engineering", IEEE Network, vol. 28, no. 5, pp. 86-90, September 2014.
ETSI
8 ETSI TR 103 395 V1.1.2 (2021-06)
[i.24] H. ElSawy, A. Sultan-Salem, M.-S. Alouini, and M. Z. Win: "Modeling and analysis of cellular
networks using stochastic geometry: A tutorial", IEEE Communications Surveys and Tutorials,
st
Quart., 2017.
vol. 19, no. 1, pp. 167-203, 1
[i.25] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: "Forecasting
th
and Control", 5 ed. Hoboken, NJ, USA: Wiley, 2015.
[i.26] J. Lin: "Divergence measures based on the Shannon entropy", IEEE Transactions on Information.
Theory, vol. 37, no. 1, pp. 145-151, January 1991.
[i.27] M. Sheppard. MIT Lincoln Laboratory. March 11, 2019.
NOTE: Available at FBD - "Find the Best Distribution" tool.
nd
[i.28] K. Krishnamoorthy, Handbook of Statistical Distributions With Applications, 2 edition. Boca
Raton, FL, USA: CRC, Press, 2016.
[i.29] F. Zabini and A. Conti: "Inhomogeneous Poisson Sampling of Finite-Energy Signals With
Uncertainties in Rd", IEEE Transaction on Signal Processing, vol. 64, iss. 18, pp. 4679-4694,
2016.
[i.30] IEEE 802.11b™: "IEEE Standard for Information Technology -- Telecommunications and
information exchange between systems - Local and Metropolitan networks -- Specific
requirements -- Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)
specifications: Higher Speed Physical Layer (PHY) Extension in the 2.4 GHz band".
[i.31] IEEE 802.11g™: "IEEE Standard for Information technology -- Local and metropolitan area
networks -- Specific requirements -- Part 11: Wireless LAN Medium Access Control (MAC) and
Physical Layer (PHY) Specifications: Further Higher Data Rate Extension in the 2.4 GHz Band".
[i.32] IEEE 802.11n™: "IEEE Standard for Information technology -- Local and metropolitan area
networks -- Specific requirements -- Part 11: Wireless LAN Medium Access Control (MAC)and
Physical Layer (PHY) Specifications Amendment 5: Enhancements for Higher Throughput".
3 Definition of terms, symbols and abbreviations
3.1 Terms
Void.
3.2 Symbols
For the purposes of the present document, the following symbols apply:
C Channel Number
.
� , E() Expected Value
�
f Centre Frequency
c
H Null hypothesis
H Alternative Hypothesis
i Channel Identifier
K Number of Samples Collected from the Band in One Sweep
k Shape Parameter
�
� Maximized Value Of Likelihood Function
n Number of Samples Collected in the Channel
� Observed Value
�
��� Probability of False Alarm
���
�(� (�)) Sample Power j at Channel i
�
T Number of Sweeps
� Threshold for Consecutive Mean Excision
���
t Time
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9 ETSI TR 103 395 V1.1.2 (2021-06)
X Sample Space
α Significance Level
Γ() Gamma Function
� Arrival Rate
� Location Parameter
� Scale Parameter
� Noise Threshold
� shape parameter
σk The log-normal variance of the measured data between path loss and K-factor
σ The log-normal variance in dB around the mean, representing the variations measured at different
p
body and room locations.
NOTE: This parameter will depend on variations in the body curvature, tissue properties and antenna radiation
properties at different body locations.
E /N Energy per bit to noise power spectral density ratio
b 0
h Modulation index
I Implementation losses in dB
dB
K The fit with measurement data for the K-factor for low path loss
K K factor of Ricean distribution in dB
dB
L Pulse length
L Length of slot
slot
m Numerator of modulation index
m The average decay rate in dB/cm for the surface wave travelling around the perimeter of the body
m The slope of the linear correlation between path loss and K-factor
k
M M-ary number
NF Noise Figure in dB
dB
n Zero mean and unit variance Gaussian random variable
k
n Zero mean and unit variance Gaussian random variable
p
p Denominator of modulation index
P The average loss close to the antenna
P The average attenuation of components in an indoor environment radiated away from the body and
reflected back towards the receiving antenna
P Bit error probability
b
PL Path loss in dB
dB
PPDU Times of PPDU repetition
rep
Q( ) Q function
R Data rate
S Receiver sensitivity
dBm
T T /L
min s slot
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
ACK ACKnowledgement
AIC Akaike Information Criterion
ANL Average Noise Level
ARA Antenna Research Associate
AWGN Additive White Gaussian Noise
BAN Body Area Network
BCH Bose, Chaudhuri, and Hocquenghem
BER Bit Error Rate
BIC Bayesian Information Criterion ®
BLE Bluetooth Low Energy
BPF Bandpass Filter ®
BT Bluetooth
CCA Clear Channel Assessment
CCA-ED Clear Channel Assessment based on Energy Detection
CDF Cumulative Distribution Function
CM Channel Model
CO Channel Occupancy
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10 ETSI TR 103 395 V1.1.2 (2021-06)
CSRR Clean Sample Rejection Rate
DSSS Direct Sequence Spread Spectrum
ED Energy Detection
EGC Equal Gain Combining
FBO Frequency Band Occupancy
FCME Forward Consecutive Mean Excision
FER Frame Error Rate
FH Frequency Hopping
GEV Generalized Extreme Value
GEVD Generalized Extreme Value Distribution
GFSK Gaussian Frequency Shift Keying
HI High Interference
ICT Information and Communication Technology
ISM Industrial, Scientific and Medical
ITU-R International Telecommunication Union - Radio communication sector
JPG Joint Photographic experts Group
JSD Jensen-Shannon Divergence
KS Kolmogorov-Smirnov
LI Low Interference
LNA Low Noise Amplifier
LTE Long Term Evolution
MAC Medium Access Control
MATLAB Matrix Laboratory
NOTE: A multi-paradigm numerical computing environment and fourth-generation programming language. A
TM
.
proprietary programming language developed by MathWorks
MC Measurement Campaign
Med-FCME Median Forward Consecutive Mean Excision
ML Maximum Likelihood
MLE Maximum Likelihood Estimate
MLSD Maximum-Likelihood Sequence Detector
MPDU MAC Protocol Data Unit
MRI Magnetic Resonance Imaging
OBW Occupied BandWidth
OFDM Orthogonal Frequency Division Multiplexing
OYS Oulun Yliopistollinen Sairaala (Oulu University Hospital)
PDF Probability Distribution Function
PHY PHYsical layer
PLCP Physical Layer Convergence Procedure
PMF Probability Mass Function
PPDU Physical-layer Protocol Data Unit
PSDU Physical-layer Service Data Unit
RBW Resolution BandWidth
RF Radio Frequency
RV Random Variable
SA Spectrum Analyser
SNR Signal-to-Noise Ratio
SOE Spectrum Occupancy Evaluation
SRO Spectrum Resource Occupancy
SSC Spatial Sample Clustering
TC Technical Committee
TCME Threshold for Consecutive Mean Excision
TLSD t Location-Scale Distribution
TS Technical Specification
UHF Ultra High Frequency
UWB Ultra WideBand
WBAN Wireless Body Area Network
WLAN Wireless Local Area Network
WPAN Wireless Personal Area Networks
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11 ETSI TR 103 395 V1.1.2 (2021-06)
4 Introduction and Background
Modern medical and health monitoring equipment is moving towards the trend of wireless connectivity between the
data collection or control centre and the medical devices or sensors. Therefore, the need for standardized
communication interfaces and protocols between the actors is required. This network of actors performing some
medical monitoring or functions in this context is called a Smart Body Area Network (SmartBAN).
Most emerging radio technologies for Wireless Personal Area Networks (WPAN) are designed to operate around the ®
2,4 GHz ISM band. Since both standardized (such as Bluetooth and IEEE 802.11 [i.3]) and non-standardized
(proprietary) devices use the same frequency band, interference may lead to significant performance degradation of
medical (and other) devices operating in the band. The main goal of the present document is to describe the interference
problem and to highlight a coexistence framework for the medical Information and Communication Technology (ICT)
to operate in a proximal environment. In the present document, a synthesis of the problem of interference and
coexistence around the 2,4 GHz ISM band is given. Measurements carried out in hospital and campus will be described
in order to have a better insight on the problem. Then, the measurement campaigns exhaustively accumulated data in
order to formulate a mathematical model of the interference at the channel in the 2,36 to 2,5 GHz band will be
described.
5 Coexistence
5.0 Introduction
A number of use cases have been identified as potential scenarios for SmartBAN. These use cases serve as scenarios
where real channel occupancy measurements are needed. The environments to be considered for investigating the
coexistence issues are such as:
• Hospital
• Home
• Office
• Outdoor
These cases include the typical environments where a patient wearing a SmartBAN system lives and stays. However,
the present document is focusing on indoor environments only.
Moreover, existing interferers are classified into two classes based on their usage of the spectrum. Devices
implementing the Direct Sequence Spread Spectrum (DSSS) technique constitute one class of interferers that utilize a
fixed channel in the band. Typically this channel is 22 MHz wide, although the width of the signal depends on the
transmitter's implementation. The second class of interferers is represented by devices implementing a type of
Frequency Hopping (FH) mechanism. Note that the IEEE 802.11 [i.3] specifications include a frequency hopping ®
technique that uses a deterministic frequency pattern. On the other hand, the Bluetooth specifications define a
pseudo-random frequency sequence based on the Bluetooth device's address and its internal clock. While interference
among systems from the same type, such as Bluetooth on Bluetooth, or IEEE 802.11 [i.3] on IEEE 802.11 [i.3],
interference can be significant, it is usually considered early on in the design stages of the protocol (phenomena is
called as multiuser interference). A third class can be included, which comprehend the devices using the Orthogonal
Frequency Division Multiplexing (OFDM) technique. Therefore, the worst realistic interference scenario consists of a
mix of heterogeneous devices, i.e. devices belonging to different classes.
In evaluating the performance with respect to coexistence issues, variations in the operational environment need to be
considered, including both the characteristics of the interfering wireless services and the Radio Frequency (RF)
propagation characteristics. This ensures that the evaluation takes into account the uncertainty in an installation's
location and in the interfering traffic. Evaluating the performance requirements in terms of coexistence issues provides a
method for quantifying the applications interference susceptibility and assists in establishing usage policies.
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12 ETSI TR 103 395 V1.1.2 (2021-06)
The analytical model for evaluating the coexistence in terms of the operational environment is developed based on the
following process:
• Characterize the interference under static conditions, i.e. when both interfering and desired signals remain
stationary. Empirical test results are used to estimate model parameters and to substantiate the model.
• Extract a mathematical model of the aggregate interference in all operational environments.
5.1 Bands
For coexistence purposes the typical interference levels are evaluated in the following bands:
• ISM band
• 30 MHz before the ISM band
• 30 MHz after the ISM band
• Option for UWB lower band
6 Measurements
6.1 Background & Motivation
Radio frequency is a finite resource coordinated by regulatory bodies all over the globe. For medical usage, varying
regulations are imposed in different countries involving the allocation of various chunks of both licensed and unlicensed
frequency resource [i.4]. Some of the license-free solutions include e.g. sub-gigahertz ISM band, 2,4 GHz ISM band
and 3 GHz to 10 GHz ultrawide band, etc. [i.4]. 2,4 GHz ISM band is an unregulated, license-free frequency band
where many communication technologies share the frequency resources, e.g. Wireless Local Area Networks (WLANs), ®
Bluetooth (BT), wireless sensor networks, cordless phones, etc. In a hospital environment, if a Wireless Body Area
Network (WBAN) is planned to be deployed in the 2,4 GHz ISM band, it will require certain measures in order to
co-exist with other wireless communication technologies.
ETSI SmartBAN Physical Layer (PHY) defines 40 channels in the 2,4 GHz ISM band, including 37 data channels and
3 control channels. Each channel is 2 MHz wide and no guard bands between two adjacent channels are defined [i.1].
There had been other considerations as well for PHY solutions, e.g. channels in 2,36 GHz to 2,40 GHz band, which has
already been allocated for medical use in USA. However, in Europe, this particular band has been allocated for LTE
special purpose use.
Increasing deployments of wireless technologies inside hospital premises will significantly increase the electromagnetic
clutter in hospital environments and hence interference will be the limiting factor. Spectrum Occupancy Evaluations
(SOEs) provide statistical quantification of spectrum utilization patterns and highlight temporal characteristics of the
band under consideration. In essence, SOEs can also be used as a probe to reflect upon the degree to which a victim
network would suffer under the influence of an aggressor. In other words, SOE can provide an insight regarding the
opportunities a wireless network will have, causing presumably no interference to already existing networks. Yet
another way to look at SOE is to study the suitability of a particular frequency band is for the deployment of a new kind
of network. In a nut-shell, it is highly motivating to perform SOEs in 2,36 GHz to 2,4 GHz band and 2,4 GHz ISM band
in order to characterize aggregate interference which would potentially be harmful to ETSI SmartBAN compliant
devices.
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13 ETSI TR 103 395 V1.1.2 (2021-06)
6.2 Spectrum Occupancy Evaluations (SOEs)
Generally, spectrum occupancy measurements involve the collection of measurement data, processing the measured
data for occupancy assessment and development of models to characterize the spectrum utilization. The key factors
influencing the measurements are measurement bandwidth span, channel bandwidths, number of channels, observation
time per channel, revisit time to a specific channel, total duration of monitoring and statistical integration time if
monitoring is sufficiently long. The International Telecommunication Union - Radio communication sector (ITU-R)
guidelines for spectrum occupancy measurements are elaborated in a special handbook [i.5] and also in two short
reports [i.7] and [i.6]. Stochastic parameters regarding spectrum occupancy evaluations include Channel Occupancy
(CO), Frequency Band Occupancy (FBO) and Spectrum Resource Occupancy (SRO). Spectrum occupancy
measurements usually involve:
• Spectrum sensing, utilizing an antenna coupled with a band pass filter and a spectrum analyser.
• Sample collection and saving the records in a hard disk drive.
• Processing and analysis of recorded data in order to calculate occupancies of interest.
Spectrum occupancy measurement handbook published by ITU-R [i.5] suggests to divide the sample space, i.e. one full
record of the band (one sweep) into channels of the expected system, which is utilizing the spectrum resource. Then,
each channel is searched for a number of samples above a predefined noise threshold. If more than 50 % of the samples
in the channel are above a noise threshold, the channel is marked as occupied. In this way, individual Channel
Occupancies (COs) are calculated for all the channels.
There are two more metrics which can also be calculated, Frequency Band Occupancy and Spectrum Resource
Occupancy. FBO provides statistical information about how much the whole frequency band is used, independently of a
particular system. SRO is a system-specific metric, which gives information about the utilization of resources available
to a specific system [i.5]. After an intensive review of literature, it was found that there were propositions which lacked
objectivity in relation to the fully unregulated band, like ISM, with so diverse access technologies. Even those studies
which were performed especially for the ISM band could not grasp the whole picture.
For example:
i) Many of the studies considered revisit time of more than a second for a span of more or less 100 MHz, which
is impractical for bursty transmissions, and a large portion of the band might appear empty during observation.
If a too fast revisit time is used, one might not get enough samples above the noise threshold in a certain
channel to declare it occupied with enough confidence.
ii) Another example of ambiguity is the channelization of the band under observation. A number of studies talked
about ISM band but end up finding occupancies for WLAN only. Although ITU-R suggests a method which
involved at the first division of the sample space into channels of the wideband system, calculation of
occupancies and then modifying the sample space by deleting the samples identified inside a wideband
channel. Secondly, the modified sample space was supposed to be divided in channels of the narrowband
system (or next wideband system with narrower bandwidth as compared to the previous one) and then
following the same procedure as before. But in real scenarios, sometimes it can lead to misclassification or
even no detection, depending on the quality of samples acquired, which in turn depends on the radio channel
conditions.
iii) Overestimation or underestimation of occupancies due to less careful noise threshold setting, an insufficient
number of samples in order to obtain statistical confidence, and inability to dig the signal out in case of a very
low Signal-to-Noise Ratio (SNR). 50 % premise, used by ITU-R handbook [i.5], results in underestimation of
true occupancies because channel nulls or under-sampling can vanquish a formidable part of the channel,
which would then appear to be as noise.
iv) A comprehensive study of occupancies in the ISM band considering possible coexisting systems had been
lacking.
v) Efficient and robust bandwidth and centre frequency estimation algorithms are missing.
ETSI
14 ETSI TR 103 395 V1.1.2 (2021-06)
In order to dig legitimate signals very close to noise levels, especially in the case of low Signal-to-Noise Ratio (SNR) or
spread spectrum scenarios, one cannot rely upon static noise thresholds. From previous research works, a dynamic noise
thresholding algorithm known as median Forward Consecutive Mean Excision (Med-FCME) [i.9] is adopted. Contrary
to previous implementations, the algorithm is applied per each sweep. The idea was to establish a noise threshold for
every sweep because, after a single sweep, a blind period is encountered, when measurement equipment was saving the
sweep data, and supposedly the noise floor is also fluctuating. Clean Sample Rejection Rate (CSRR) is a measure to
quantify the number of noise-only samples wrongly classified as outliers having signal components by FCME. The
concept of CSRR is presented in [i.9]. CSRR can be preset as a probabilistic limit so that only a given amount of
misclassification is tolerated, i.e. a false alarm. This metric is termed as the desired probability of false alarm, ��� .
���
The desired probability of false alarm value is given as a parameter to the FCME algorithm which calculates a threshold
for consecutive mean excision � as:
���
T = -ln(PFA ) , (1)
CME DES
and then performs the iterative algorithm to find out the noise thresholds. In the measurement campaign, a target CSRR
is set to be 5 %, i.e. ��� = 0,05. Hence, � = 2,99.
��� ���
The most important goals achieved through this undertaking can be listed as:
a) CO, FBO and SRO evaluation for the 2,35 to 2,50 GHz band in a hospital environment to characterize
potential interferers in two different countries (Finland and Italy).
b) A novel centre frequency and bandwidth estimation algorithm named Spatial Sample Clustering (SSC).
c) An overhauled, robust and much more objective mechanism for SOE evaluations with a sufficiently low
desired probability of false alarm.
d) Mathematical models for quantification of interference.
For various equations including CO, FBO, SRO calculation and the complete analytical appro
...