Opportunistic cognitive radio network with primary user activity model
A Cognitive radio (CR) network is the solution to increase spectrum utilization in wireless systems. The main objective of CR is to get the best frequency that is not in use based on its ability to detect the environment. The CR allows the secondary user (SU) to operate in a licensed spectru...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/85417/1/FK%202019%20150%20-%20ir.pdf |
| _version_ | 1825952116983726080 |
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| author | Mohamad, Mas Haslinda |
| author_facet | Mohamad, Mas Haslinda |
| author_sort | Mohamad, Mas Haslinda |
| collection | UPM |
| description | A Cognitive radio (CR) network is the solution to increase spectrum utilization in wireless
systems. The main objective of CR is to get the best frequency that is not in use based on its
ability to detect the environment. The CR allows the secondary user (SU) to operate in a licensed
spectrum without interfering with the primary user (PU). The SU can remain on the spectrum without
reducing the transmission power levels. The CR performance is limited by interference from both PU
and other CRs. Most previous studies analysed interference through different fading model. The
interference to PU was usually measured as the quantity of SUs signal power at the primary
receiver. Very few study evaluates temporal interference by considering spectrum occupancy in
terms of time used in the spectrum. Therefore, the opportunistic access of CR to the PU network
by considering the temporal interference is the main focus of the research.
This research investigates the opportunistic access of a CR system which enables the
SU to access the detected transmission opportunity length (TOL). The probability of collision
between SU and PU and the SU throughput is observed as the PU traffic pattern change. In this part,
the research presents the empirical time-dimension of PU activity pattern by the detected of TOL
from an empirical measurement data. An experimental setup of a wireless local area network (WLAN)
is executed to measure the TOL in the system. The experiment was run in two different scenarios
which involved: scenario 1 with one PU and scenario 2 with one PU and one SU accessed the WLAN.
The energy detection is performed throughout the detected signals to extract the TOL. The TOLs in
both scenarios were analysed and characterised to be used for opportunistic access. An empirical
model based on PU traffic for opportunistic access (EM-PuO) is developed. The EM-PuO model
characterised the PU traffic pattern with a few distributions fits such as exponential, Generalized
Pareto (GP), and normal distribution.
Among these distributions, the GP is the best fit for idle states as the DKS = 0.2655 is the
lowest.
The second part of the work characterises the TOL using Primary User Activity based on a duty
cycle (PUA-DC) model. The PU activity is modelled to represent the occupancy spectrum
in the time domain in a realistic scenario. The spectrum occupancy in this model indicated as the
percentage of a duty cycle. The probability of interference between SU and PU and the data rate of
PU are observed as the PU traffic pattern change. Then, the PUA-DC model compared to the existing
work to validate the behaviour of the SU and PU performance as there are changing pattern in PU
activity.
Next, this research studies the SU throughput by clustering the TOL in CTOL model. This model
clustered TOL to large and small duration and used Markov model to maximize the SU throughput under
detection probability constraint. Then, the performance of the SU is then analysed and compared
with static and dynamic PU models. The results showed that the SU throughput in the CTOL model was
higher than the static and dynamic models by almost 45% and 12.2% respectively. Furthermore, the
probability of collisions in the network and the SU throughput were influenced by the value of the
minimum contention window and the maximum back-off stage. The simulation results revealed that the
higher contention window had worsened the SU throughput even though the channel has a higher number
of TOLs.
The last part of this work investigates the scalability effect on CTOL model. Two scenarios of
scalability effect have been discussed to answer the third objective are scenario 1 with mul- tiple
PU and scenario 2 with multiple SU nodes. The result discovered that SU throughput increased as
the number of PU increased. But as the longer SU frame duration, the perfor-
mance of throughput degraded, although there are numerous PU in the system. |
| first_indexed | 2024-03-06T10:40:10Z |
| format | Thesis |
| id | upm.eprints-85417 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:40:10Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | upm.eprints-854172021-12-16T04:01:02Z http://psasir.upm.edu.my/id/eprint/85417/ Opportunistic cognitive radio network with primary user activity model Mohamad, Mas Haslinda A Cognitive radio (CR) network is the solution to increase spectrum utilization in wireless systems. The main objective of CR is to get the best frequency that is not in use based on its ability to detect the environment. The CR allows the secondary user (SU) to operate in a licensed spectrum without interfering with the primary user (PU). The SU can remain on the spectrum without reducing the transmission power levels. The CR performance is limited by interference from both PU and other CRs. Most previous studies analysed interference through different fading model. The interference to PU was usually measured as the quantity of SUs signal power at the primary receiver. Very few study evaluates temporal interference by considering spectrum occupancy in terms of time used in the spectrum. Therefore, the opportunistic access of CR to the PU network by considering the temporal interference is the main focus of the research. This research investigates the opportunistic access of a CR system which enables the SU to access the detected transmission opportunity length (TOL). The probability of collision between SU and PU and the SU throughput is observed as the PU traffic pattern change. In this part, the research presents the empirical time-dimension of PU activity pattern by the detected of TOL from an empirical measurement data. An experimental setup of a wireless local area network (WLAN) is executed to measure the TOL in the system. The experiment was run in two different scenarios which involved: scenario 1 with one PU and scenario 2 with one PU and one SU accessed the WLAN. The energy detection is performed throughout the detected signals to extract the TOL. The TOLs in both scenarios were analysed and characterised to be used for opportunistic access. An empirical model based on PU traffic for opportunistic access (EM-PuO) is developed. The EM-PuO model characterised the PU traffic pattern with a few distributions fits such as exponential, Generalized Pareto (GP), and normal distribution. Among these distributions, the GP is the best fit for idle states as the DKS = 0.2655 is the lowest. The second part of the work characterises the TOL using Primary User Activity based on a duty cycle (PUA-DC) model. The PU activity is modelled to represent the occupancy spectrum in the time domain in a realistic scenario. The spectrum occupancy in this model indicated as the percentage of a duty cycle. The probability of interference between SU and PU and the data rate of PU are observed as the PU traffic pattern change. Then, the PUA-DC model compared to the existing work to validate the behaviour of the SU and PU performance as there are changing pattern in PU activity. Next, this research studies the SU throughput by clustering the TOL in CTOL model. This model clustered TOL to large and small duration and used Markov model to maximize the SU throughput under detection probability constraint. Then, the performance of the SU is then analysed and compared with static and dynamic PU models. The results showed that the SU throughput in the CTOL model was higher than the static and dynamic models by almost 45% and 12.2% respectively. Furthermore, the probability of collisions in the network and the SU throughput were influenced by the value of the minimum contention window and the maximum back-off stage. The simulation results revealed that the higher contention window had worsened the SU throughput even though the channel has a higher number of TOLs. The last part of this work investigates the scalability effect on CTOL model. Two scenarios of scalability effect have been discussed to answer the third objective are scenario 1 with mul- tiple PU and scenario 2 with multiple SU nodes. The result discovered that SU throughput increased as the number of PU increased. But as the longer SU frame duration, the perfor- mance of throughput degraded, although there are numerous PU in the system. 2019-05 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/85417/1/FK%202019%20150%20-%20ir.pdf Mohamad, Mas Haslinda (2019) Opportunistic cognitive radio network with primary user activity model. Doctoral thesis, Universiti Putra Malaysia. Cognitive radio networks Autonomic computing Wireless communication systems |
| spellingShingle | Cognitive radio networks Autonomic computing Wireless communication systems Mohamad, Mas Haslinda Opportunistic cognitive radio network with primary user activity model |
| title | Opportunistic cognitive radio network with primary user activity model |
| title_full | Opportunistic cognitive radio network with primary user activity model |
| title_fullStr | Opportunistic cognitive radio network with primary user activity model |
| title_full_unstemmed | Opportunistic cognitive radio network with primary user activity model |
| title_short | Opportunistic cognitive radio network with primary user activity model |
| title_sort | opportunistic cognitive radio network with primary user activity model |
| topic | Cognitive radio networks Autonomic computing Wireless communication systems |
| url | http://psasir.upm.edu.my/id/eprint/85417/1/FK%202019%20150%20-%20ir.pdf |
| work_keys_str_mv | AT mohamadmashaslinda opportunisticcognitiveradionetworkwithprimaryuseractivitymodel |