Abstract
5G ultra-dense network (UDN) systems consist of massive deployment of small cells. This technology allows increasing spectral efficiency and solving the spectrum scarcity problem. However, as small cell count increases, the probability of severe interference increases, causing a network capacity degradation. The resource allocation (RA) algorithms distribute the available spectrum resources with the least interference. It is modeled as an optimization problem, and allocating the different resources results exceedingly complex. In this work, a new design approach for RA is proposed. The strategy is based on allocating a single block of channels to either users or cells instead of disjoint channels across the available spectrum. We call them user block allocation and cell block allocation, respectively. They consider a filtered search space of channel allocations providing two-dimensionality reduction levels to the channel allocation problem. The scenario evaluation consists of an unplanned UDN and a uniform small cell deployment, where at least one active user is present for each cell. The results obtained through the genetic algorithm solution on the network’s spectral efficiency, cell’s average capacity, and subchannel allocation rate show that the proposed arrangements alleviate the high complexity of the channel allocation problem and find feasible solutions for UDN scenarios.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adedoyin M, Falowo O (2017) QoS-aware radio resource allocation for ultra-dense heterogeneous networks. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), IEEE, https://doi.org/10.1109/pimrc.2017.8292177
Andrews JG, Zhang X, Durgin GD, Gupta AK (2016) Are we approaching the fundamental limits of wireless network densification? IEEE Commun Mag 54(10):184–190. https://doi.org/10.1109/mcom.2016.7588290
Bellman R, Corporation R, Collection KMR (1957) Dynamic programming. Rand Corporation research study. Princeton University Press, New Jersey
Bhardwaj P, Panwar A, Ozdemir O, Masazade E, Kasperovich I, Drozd AL, Mohan CK, Varshney PK (2016) Enhanced dynamic spectrum access in multiband cognitive radio networks via optimized resource allocation. IEEE Trans Wireless Commun 15(12):8093–8106. https://doi.org/10.1109/twc.2016.2612627
Cao J, Peng T, Qi Z, Duan R, Yuan Y, Wang W (2018) Interference management in ultradense networks: a user-centric coalition formation game approach. IEEE Trans Veh Tech 67(6):5188–5202. https://doi.org/10.1109/tvt.2018.2799568
Chen J, Gao Z, Zhao Q (2015) Load-aware dynamic spectrum access in ultra-dense small cell networks. In: 2015 International conference on wireless communications & signal processing (WCSP), IEEE, https://doi.org/10.1109/wcsp.2015.7341028
Chen S, Montgomery J, Bolufé-Röhler A (2014) Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution. Appl Intell 42(3):514–526. https://doi.org/10.1007/s10489-014-0613-2
Chuang MC, Chen MC, S Y (2015) Resource management issues in 5g ultra dense smallcell networks. In: 2015 International Conference on Information Networking (ICOIN), IEEE, https://doi.org/10.1109/icoin.2015.7057875
Colás SG (2019) Ultra dense networks deployment for beyond 2020 technologies. Ph.D. thesis, Universitat Politecnica de Valencia, https://doi.org/10.4995/thesis/10251/86204
Deep K, Singh KP, Kansal M, Mohan C (2009) A real coded genetic algorithm for solving integer and mixed integer optimization problems. Appl Math Comput 212(2):505–518. https://doi.org/10.1016/j.amc.2009.02.044
Ding M, Lopez-Perez D, Claussen H, Kaafar MA (2018) On the fundamental characteristics of ultra-dense small cell networks. IEEE Netw 32(3):92–100. https://doi.org/10.1109/mnet.2018.1700096
Estrada R, Otrok H, Dziong Z (2013) Resource allocation model based on particle swarm optimization for OFDMA macro-femtocell networks. In: 2013 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), IEEE, https://doi.org/10.1109/ants.2013.6802850
Fooladivanda D, Daoud AA, Rosenberg C (2011) Joint channel allocation and user association for heterogeneous wireless cellular networks. In: 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, IEEE, https://doi.org/10.1109/pimrc.2011.6139988
Yuan Gao H, Long Cao J (2013) Non-dominated sorting quantum particle swarm optimization and its application in cognitive radio spectrum allocation. J Central South Univ 20(7):1878–1888. https://doi.org/10.1007/s11771-013-1686-5
He Q, Zhang P (2012) Dynamic channel assignment using ant colony optimization for cognitive radio networks. In: 2012 IEEE Vehicular Technology Conference (VTC Fall), IEEE, https://doi.org/10.1109/vtcfall.2012.6398951
Hussain K, Salleh MNM, Cheng S, Shi Y (2018) Metaheuristic research: a comprehensive survey. Artif Intell Rev 52(4):2191–2233. https://doi.org/10.1007/s10462-017-9605-z
Jang J, Lee KB (2003) Transmit power adaptation for multiuser OFDM systems. IEEE J Select Areas Commun 21(2):171–178. https://doi.org/10.1109/jsac.2002.807348
Jiang C, Chen Y, Liu KJR, Ren Y (2014) Optimal pricing strategy for operators in cognitive femtocell networks. IEEE Trans Wireless Commun 13(9):5288–5301. https://doi.org/10.1109/twc.2014.2327970
Jung HB, Kim DK (2013) Power control of femtocells based on max–min fairness in heterogeneous networks. IEEE Commun Lett 17(7):1372–1375. https://doi.org/10.1109/lcomm.2013.052013.130421
Kamel M, Hamouda W, Youssef A (2016) Ultra-dense networks: a survey. IEEE Commun Surv Tutor 18(4):2522–2545. https://doi.org/10.1109/comst.2016.2571730
Li W, Zhang J (2018) Cluster-based resource allocation scheme with QoS guarantee in ultra-dense networks. IET Commun 12(7):861–867. https://doi.org/10.1049/iet-com.2017.1331
Liu J, Sheng M, Liu L, Li J (2017) Interference management in ultra-dense networks: challenges and approaches. IEEE Netw 31(6):70–77. https://doi.org/10.1109/mnet.2017.1700052
Marshoud H, Otrok H, Barada H, Estrada R, Jarray A, Dziong Z (2015) Realistic framework for resource allocation in macro–femtocell networks based on genetic algorithm. Telecommun Syst 63(1):99–110. https://doi.org/10.1007/s11235-015-9976-x
Martínez-Vargas A, Domínguez-Guerrero J, Andrade ÁG, Sepúlveda R, Montiel-Ross O (2016) Application of NSGA-II algorithm to the spectrum assignment problem in spectrum sharing networks. Appl Soft Comput 39:188–198. https://doi.org/10.1016/j.asoc.2015.11.010
Oughton EJ, Frias Z, van der Gaast S, van der Berg R (2019) Assessing the capacity, coverage and cost of 5g infrastructure strategies: analysis of the netherlands. Telemat Inf 37:50–69. https://doi.org/10.1016/j.tele.2019.01.003
Peng C, Zheng H, Zhao BY (2006) Utilization and fairness in spectrum assignment for opportunistic spectrum access. Mobile Netw Appl 11(4):555–576. https://doi.org/10.1007/s11036-006-7322-y
Reeves CR (2010) Genetic Algorithm. Gendreau M, Potvin JY(eds) Handbook of Metaheuristics, vol 146, Springer, pp 109–139. https://doi.org/10.1007/978-1-4419-1665-5_5
Reyna-Orta A (2019) aeroreyna/AISearchMatlab: AISearch library for Matlab. https://github.com/aeroreyna/AISearchMatlab, https://doi.org/10.5281/zenodo.3247876, accessed: 2020-08-10
Rhee W, Cioffi J (2000) Increase in capacity of multiuser OFDM system using dynamic subchannel allocation. In: VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026), IEEE, https://doi.org/10.1109/vetecs.2000.851292
Romanous B, Bitar N, Imran A, Refai H (2015) Network densification: Challenges and opportunities in enabling 5g. In: 2015 IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), IEEE, https://doi.org/10.1109/camad.2015.7390494
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Tang X, Ren P, Gao F, Du Q (2017) Interference-aware resource competition toward power-efficient ultra-dense networks. IEEE Trans Commun 65(12):5415–5428. https://doi.org/10.1109/tcomm.2017.2744648
Xu L, Mao Y, Leng S, Qiao G, Zhao Q (2017) Energy-efficient resource allocation strategy in ultra dense small-cell networks: A stackelberg game approach. In: 2017 IEEE International Conference on Communications (ICC), IEEE, https://doi.org/10.1109/icc.2017.7997289
Yang X (2010) Engineering optimization: an introduction with metaheuristic applications. Yang X-S (ed) Chapter 11: Genetic algorithms. pp 171–180, ISBN:9780470582466. https://doi.org/10.1002/9780470640425.ch11
Yao W, Li J, Tan B, Hao S (2017) Interference management scheme of ultra dense network based on clustering. In: 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), IEEE, https://doi.org/10.1109/itnec.2017.8284755
Ye Y, Zhang H, Xiong X, Liu Y (2015) Dynamic min-cut clustering for energy savings in ultra-dense networks. In: 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), IEEE, https://doi.org/10.1109/vtcfall.2015.7390904
Zhang G, Zhang H, Han Z, Karagiannidis GK (2019) Spectrum allocation and power control in full-duplex ultra-dense heterogeneous networks. IEEE Trans Commun 67(6):4365–4380. https://doi.org/10.1109/tcomm.2019.2897765
Zhao Z, Peng Z, Zheng S, Shang J (2009) Cognitive radio spectrum allocation using evolutionary algorithms. IEEE Trans Wireless Commun 8(9):4421–4425. https://doi.org/10.1109/twc.2009.080939
Funding
This work was supported in part by the National Council of Science and Technology (CONACyT, Mexico) through the Fondo Sectorial de Investigación para la Educación under Grant 288670.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Reyna-Orta, A., Andrade, Á.G. Dimensionality reduction to solve resource allocation problem in 5G UDN using genetic algorithm. Soft Comput 25, 4629–4642 (2021). https://doi.org/10.1007/s00500-020-05473-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05473-8