Spectrum assignment allocation based on artificial swarm intelligence using a bioinspired hybrid metaheuristic algorithm
Published
:Issue
:Section
:License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Dimensions Metrics
spectrum allocation, inteligencia de enjambre, metaheuristic algorithm, heterogeneous network
The exponential growth of the new mobile and wireless services, inherent to the needs of a hyperconnected society, demands more and more the almost immediate and permanent access of different radio resources, which implies attending in a careful way, aspects such as the expected shortage of the electromagnetic spectrum and its considerable underutilization in current radiocommunications, thus motivating the investigation of emerging models for dynamic spectrum allocation. In this way, Dynamic Spectrum Access is presented as an efficient solution for the resilient reuse of wireless communication channels in a shared scheme of frequency bands. The main challenge of dynamic spectrum allocation is to be able to guarantee to all the users of a network, protection against the interference that could be generated during the simultaneous occupation of a communication channel. Therefore, it is considered pertinent to restrict access to an overloaded number of users, in order to achieve peaceful coexistence in a given coverage area, taking into account that the simultaneous use of a channel by one or more users will be possible as long as they do not exceed the interference threshold imposed by the system. Consequently, this work uses Artificial Intelligence based on a bio-inspired hybrid metaheuristic algorithm, called Optimization by Swarm of Socio-Cognitive Particles, in order to solve the problem of Dynamic Spectrum Access in a Heterogeneous Network, having as a multi-objective utility function the metrics associated with user assignment and data rate maximization, thus mitigating the harmful impact of interference and successfully assigning spectrum to an average of 80 % of the users considered in the analysis scenario.
A. Martínez. “Control de admisión y asignación de canal para acceso dinámico de espectro usando cómputo multi-objetivo”. Computación y Sistemas, Vol. 19, No. 2, pp. 337-355, 2015
C. Salgado. “Técnicas inteligentes en la asignación de espectro dinámica para redes inalámbricas cognitivas”. TECNURA, Vol. 20, No. 49, 2016 DOI: https://doi.org/10.14483/udistrital.jour.tecnura.2016.3.a09
A. Martinez y Á. Andrade. “Comparing particle swarm optimization variants for a cognitive radio network”. Applied Soft Computing, Vol. 13 No. 2, pp. 1222–1234, 2013
A. Galvis y R. Márquez. “Simulación y Análisis de Alternativas para la Asignación Dinámica de Espectro en ambientes TDMA”. Proceedings IEEEE de Sistemas de Telecomunicaciones, Escuela Politécnica Nacional/IEEE ComSoc, Quito-Ecuador, 2008
D.A. Roberson, C.S. Hood, y J.L. LoCicero. “Spectral Occupancy and Interference Studies in support of Cognitive Radio Technology Deployment”. 1st IEEE Workshop on Networking Technologies for Software Defined Radio Networks (SDR), Reston, USA, pp. 26–35, 2006 DOI: https://doi.org/10.1109/SDR.2006.4286323
N. Abbas, Y. Nasser, y K. El Ahmad. “Recent advances on artificial intelligence and learning techniques in cognitive radio networks”. EURASIP Journal on Wireless Communications and Networking, 1, 1-20, 201
Y. Zhang, Z. Zhang, Luo y H. Wang. “Initial spectrum access control with QoS protection for active users in cognitive wireless networks”. International Journal of Communication Systems, Vol. 25, No. 5, pp. 636–651, 2012 DOI: https://doi.org/10.1002/dac.1287
J. Tadrous, A. Sultan y M. Nafie. “Admission and Power Control for Spectrum Sharing Cognitive Radio Networks”. IEEE Transactions on Wireless, Vol. 10, No. 6, pp. 1945–1955, 2011. DOI: 10.1109/TWC.2011.040411.101571 DOI: https://doi.org/10.1109/TWC.2011.040411.101571
S.D. Roy y S. Kundu. “Gradual removal of secondary user in cognitive-CDMA spectrum underlay network”. International Conference on Devices and Communications (ICDeCom), Mesra, Algeria, pp. 1–4, 2011 DOI: https://doi.org/10.1109/ICDECOM.2011.5738471
B. Wang y D. Zhao. “Performance analysis in CDMA-based cognitive wireless networks with spectrum underlay”. IEEE Global Telecommunications Conference (IEEE GLOBECOM), New Orleans, USA, pp. 1–6, 2008 DOI: https://doi.org/10.1109/GLOCOM.2008.ECP.959
P. Liu, J. Li y H. Li. “An Iteration Resource Allocation Method to Maximize Number of Users with QoS Demand in Femtocell Networks”. 2nd IEEE/CIC Conference on Communications in China, pp. 554-558, 2013 DOI: https://doi.org/10.1109/ICCChina.2013.6671176
O. Ulgen, B. John, y J. Betty. “Simulation Methodology – A Practitioner’s Perspective”, University of Michigan – Dearborn and Production Modeling Corporation. 2006
J. Kennedy y R. Eberhart. “Particle swarm optimization”. In Proceedings of IEEE International Conference on Neural Networks, Vol. 4, pp. 1942–1948, 1995 DOI: https://doi.org/10.1109/ICNN.1995.488968
A. Cervantes. “Clasificación mediante enjambre de prototipos”. Tesis Doctoral. Departamento de Informática. Universidad Carlos III de Madrid. 2016
K. Deep y J.C. Bansal. “A Socio-Cognitive Particle Swarm Optimization for Multi-Dimensional Knapsack Problem”. Emerging Trends in Engineering and Technology Conference (ICETET), pp. 355–360, 2008 DOI: https://doi.org/10.1109/ICETET.2008.163
K. Deb. Multi-objective optimization using evolutionary algorithms. New York: John Wiley & Sons. 2001
R.T. Marler y J.S. Arora. “The weighted sum method for multi-objective optimization: new insights”. Structural and Multidisciplinary Optimization, Vol. 41 No. 6, pp. 853–862, 2010 DOI: https://doi.org/10.1007/s00158-009-0460-7
A. Martínez y A. Andrade. “Deployment analysis and optimization of heterogeneous networks under the spectrum underlay strategy”. EURASIP Journal on Wireless Communications and Networking, vol. 1, pp. 1-15, 2015 DOI: https://doi.org/10.1186/s13638-015-0276-7
A. Martínez y A. Andrade. “Comparing particle swarm optimization variants for a cognitive radio network”. ELSEVIER, pp. 1222-1234, 2012 DOI: https://doi.org/10.1016/j.asoc.2012.10.016
Z. Haibo, Y. Quan, S. Xuemin, W, Shaohua y Z, Qinyu. “Dynamic Sharing of Wireless Spectrum”. Springer, 2017
A. Karandikar, N. Akhtar y M. Mehta. Mobility Management in LTE Heterogeneous Networks. Singapore: Springer. 2017 DOI: https://doi.org/10.1007/978-981-10-4355-0
T. Erpek, M.A. Mchenry y A. Stirling. “Dynamic spectrum access operational parameters with wireless microphones”. IEEE Communications Magazine, Vol. 49, No. 3, pp. 38–45, 2011. DOI: 10.1109/MCOM.2011.5723798 DOI: https://doi.org/10.1109/MCOM.2011.5723798
M. Esteban. “Optimización Binaria por Cúmulo de Partículas con Memoria (MBPSO) para Resolver un Problema de Espectro Compartido”. Computación y Sistemas, vol. 20, No. 1, pp. 153-168, 2016
M. Ali, S. Qaisar y M. Naeem. "Resource allocation for licensed and unlicensed spectrum in 5G heterogeneus networks".Transactions on Emerging Telecommunications Technologies, e3299, 2018. DOI: https://doi.org/10.1002/ett.3299
The exponential growth of the new mobile and wireless services, inherent to the needs of a hyperconnected society, demands more and more the almost immediate and permanent access of different radio resources, which implies attending in a careful way, aspects such as the expected shortage of the electromagnetic spectrum and its considerable underutilization in current radiocommunications, thus motivating the investigation of emerging models for dynamic spectrum allocation. In this way, Dynamic Spectrum Access is presented as an efficient solution for the resilient reuse of wireless communication channels in a shared scheme of frequency bands. The main challenge of dynamic spectrum allocation is to be able to guarantee to all the users of a network, protection against the interference that could be generated during the simultaneous occupation of a communication channel. Therefore, it is considered pertinent to restrict access to an overloaded number of users, in order to achieve peaceful coexistence in a given coverage area, taking into account that the simultaneous use of a channel by one or more users will be possible as long as they do not exceed the interference threshold imposed by the system. Consequently, this work uses Artificial Intelligence based on a bio-inspired hybrid metaheuristic algorithm, called Optimization by Swarm of Socio-Cognitive Particles, in order to solve the problem of Dynamic Spectrum Access in a Heterogeneous Network, having as a multi-objective utility function the metrics associated with user assignment and data rate maximization, thus mitigating the harmful impact of interference and successfully assigning spectrum to an average of 80 % of the users considered in the analysis scenario.
A. Martínez. “Control de admisión y asignación de canal para acceso dinámico de espectro usando cómputo multi-objetivo”. Computación y Sistemas, Vol. 19, No. 2, pp. 337-355, 2015
C. Salgado. “Técnicas inteligentes en la asignación de espectro dinámica para redes inalámbricas cognitivas”. TECNURA, Vol. 20, No. 49, 2016 DOI: https://doi.org/10.14483/udistrital.jour.tecnura.2016.3.a09
A. Martinez y Á. Andrade. “Comparing particle swarm optimization variants for a cognitive radio network”. Applied Soft Computing, Vol. 13 No. 2, pp. 1222–1234, 2013
A. Galvis y R. Márquez. “Simulación y Análisis de Alternativas para la Asignación Dinámica de Espectro en ambientes TDMA”. Proceedings IEEEE de Sistemas de Telecomunicaciones, Escuela Politécnica Nacional/IEEE ComSoc, Quito-Ecuador, 2008
D.A. Roberson, C.S. Hood, y J.L. LoCicero. “Spectral Occupancy and Interference Studies in support of Cognitive Radio Technology Deployment”. 1st IEEE Workshop on Networking Technologies for Software Defined Radio Networks (SDR), Reston, USA, pp. 26–35, 2006 DOI: https://doi.org/10.1109/SDR.2006.4286323
N. Abbas, Y. Nasser, y K. El Ahmad. “Recent advances on artificial intelligence and learning techniques in cognitive radio networks”. EURASIP Journal on Wireless Communications and Networking, 1, 1-20, 201
Y. Zhang, Z. Zhang, Luo y H. Wang. “Initial spectrum access control with QoS protection for active users in cognitive wireless networks”. International Journal of Communication Systems, Vol. 25, No. 5, pp. 636–651, 2012 DOI: https://doi.org/10.1002/dac.1287
J. Tadrous, A. Sultan y M. Nafie. “Admission and Power Control for Spectrum Sharing Cognitive Radio Networks”. IEEE Transactions on Wireless, Vol. 10, No. 6, pp. 1945–1955, 2011. DOI: 10.1109/TWC.2011.040411.101571 DOI: https://doi.org/10.1109/TWC.2011.040411.101571
S.D. Roy y S. Kundu. “Gradual removal of secondary user in cognitive-CDMA spectrum underlay network”. International Conference on Devices and Communications (ICDeCom), Mesra, Algeria, pp. 1–4, 2011 DOI: https://doi.org/10.1109/ICDECOM.2011.5738471
B. Wang y D. Zhao. “Performance analysis in CDMA-based cognitive wireless networks with spectrum underlay”. IEEE Global Telecommunications Conference (IEEE GLOBECOM), New Orleans, USA, pp. 1–6, 2008 DOI: https://doi.org/10.1109/GLOCOM.2008.ECP.959
P. Liu, J. Li y H. Li. “An Iteration Resource Allocation Method to Maximize Number of Users with QoS Demand in Femtocell Networks”. 2nd IEEE/CIC Conference on Communications in China, pp. 554-558, 2013 DOI: https://doi.org/10.1109/ICCChina.2013.6671176
O. Ulgen, B. John, y J. Betty. “Simulation Methodology – A Practitioner’s Perspective”, University of Michigan – Dearborn and Production Modeling Corporation. 2006
J. Kennedy y R. Eberhart. “Particle swarm optimization”. In Proceedings of IEEE International Conference on Neural Networks, Vol. 4, pp. 1942–1948, 1995 DOI: https://doi.org/10.1109/ICNN.1995.488968
A. Cervantes. “Clasificación mediante enjambre de prototipos”. Tesis Doctoral. Departamento de Informática. Universidad Carlos III de Madrid. 2016
K. Deep y J.C. Bansal. “A Socio-Cognitive Particle Swarm Optimization for Multi-Dimensional Knapsack Problem”. Emerging Trends in Engineering and Technology Conference (ICETET), pp. 355–360, 2008 DOI: https://doi.org/10.1109/ICETET.2008.163
K. Deb. Multi-objective optimization using evolutionary algorithms. New York: John Wiley & Sons. 2001
R.T. Marler y J.S. Arora. “The weighted sum method for multi-objective optimization: new insights”. Structural and Multidisciplinary Optimization, Vol. 41 No. 6, pp. 853–862, 2010 DOI: https://doi.org/10.1007/s00158-009-0460-7
A. Martínez y A. Andrade. “Deployment analysis and optimization of heterogeneous networks under the spectrum underlay strategy”. EURASIP Journal on Wireless Communications and Networking, vol. 1, pp. 1-15, 2015 DOI: https://doi.org/10.1186/s13638-015-0276-7
A. Martínez y A. Andrade. “Comparing particle swarm optimization variants for a cognitive radio network”. ELSEVIER, pp. 1222-1234, 2012 DOI: https://doi.org/10.1016/j.asoc.2012.10.016
Z. Haibo, Y. Quan, S. Xuemin, W, Shaohua y Z, Qinyu. “Dynamic Sharing of Wireless Spectrum”. Springer, 2017
A. Karandikar, N. Akhtar y M. Mehta. Mobility Management in LTE Heterogeneous Networks. Singapore: Springer. 2017 DOI: https://doi.org/10.1007/978-981-10-4355-0
T. Erpek, M.A. Mchenry y A. Stirling. “Dynamic spectrum access operational parameters with wireless microphones”. IEEE Communications Magazine, Vol. 49, No. 3, pp. 38–45, 2011. DOI: 10.1109/MCOM.2011.5723798 DOI: https://doi.org/10.1109/MCOM.2011.5723798
M. Esteban. “Optimización Binaria por Cúmulo de Partículas con Memoria (MBPSO) para Resolver un Problema de Espectro Compartido”. Computación y Sistemas, vol. 20, No. 1, pp. 153-168, 2016
M. Ali, S. Qaisar y M. Naeem. "Resource allocation for licensed and unlicensed spectrum in 5G heterogeneus networks".Transactions on Emerging Telecommunications Technologies, e3299, 2018. DOI: https://doi.org/10.1002/ett.3299