Skip to main content
SpringerLink
Log in

Optimized channel allocation in emerging mobile cellular networks

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The task of optimizing service quality in wireless networks is a continuous research that requires the design of efficient channel allocation schemes. The problem is how limited channel resources can be maximally utilized, to guarantee seamless communication while maintaining excellent service quality. Whereas, fixed channel allocation (FCA) schemes treat new and handoff calls equally without preference to normally prioritized handoff calls; dynamic channel allocation (DCA) schemes accommodate users mobility in randomly changing network conditions. However, classical Erlang-B models are deficient and do not consider users mobility and dynamically changing traffic of the mobile network environment. A modified Erlang-B dynamic channel allocation (MEB-DCA) scheme is therefore introduced in this paper, for improved network performance. The MEB-DCA algorithm introduces a conditional threshold for handoff request assignment to ensure that communication systems do not unnecessarily prioritize handoff calls at the detriment of new calls. Deriving knowledge from imprecise network data is difficult when developing functional relationships between parameters, requiring advanced modeling techniques with cognitive experience. Soft computing techniques have been shown to handle this challenge given its ability to represent precisely, both data and expert knowledge. An adaptive neuro-fuzzy inference system-based dynamic channel allocation (ANFIS-DCA) framework was proposed to automate the learning of communication parameters for optimized channel allocation decisions. Network parameters considered were received signal strength indication impacted by user mobility, number of guard and general channels, carried traffic, and handoff blocking threshold. The performance of the proposed ANFIS-DCA model was found to outsmart the static FCA and back propagation neural network-based DCA (NN-DCA) schemes using mean square error and root mean square error as performance measures. Our approach can be effectively deployed to improve channel allocation, resource utilization, network capacity, and satisfy users experience.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Adiego D, Cordier C (2002) Multiservice radio dimensioning for UMTS packet-switched services. Proc IEEE Pers Indoor Mob Radio Commun 5:2409–2413

    Article  Google Scholar 

  • Ahmed RE (2006) A hybrid channel allocation algorithm using hot-spot notification for wireless cellular networks. In: Proceedings of IEEE Canadian conference on electrical and computer engineering, Canada, pp 891–894. https://doi.org/10.1109/CCECE.2006.277671

  • Alagu S, Meyyappan T (2012) A novel handoff decision algorithm in call admission control strategy to utilize the scarce spectrum in wireless mobile networks. Int J Wirel Mob Netw 4(6):99–113. https://doi.org/10.5121/ijwmn.2012.4608

    Article  Google Scholar 

  • Ali SH (2013) Novel approach for generating the key of stream cipher system using random forest data mining algorithm. In: 2013 sixth international conference on developments in esystems engineering, pp 259–269. IEEE. https://doi.org/10.1109/DeSE.2013.54

  • Al-Janabi S, Alkaim AF (2019) A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation. Soft Comput 24(1):555–569. https://doi.org/10.1007/s00500-019-03972-x

  • Al-Janabi S, Alwan E (2017) Soft mathematical system to solve black box problem through development the FARB based on hyperbolic and polynomial functions. In: 2017 10th international conference on developments in esystems engineering (DeSE), pp 37–42. IEEE. https://doi.org/10.1109/DeSE.2017.23

  • Al-Janabi S, Hussein NY (2019) The reality and future of the secure mobile cloud computing (SMCC): survey. In: International Conference on big data and networks technologies, pp 231–261. Springer, Cham. https://doi.org/10.1007/978-3-030-23672-4_18

  • Al-Janabi S, Patel A, Fatlawi H, Kalajdzic K, Al Shourbaji I (2014) Empirical rapid and accurate prediction model for data mining tasks in cloud computing environments. In: 2014 international congress on technology, communication and knowledge (ICTCK), pp 1–8. IEEE. https://doi.org/10.1109/ICTCK.2014.7033495

  • Al-Janabi S, Al-Shourbaji I, Shojafar M, Abdelhag M (2017) Mobile cloud computing: challenges and future research directions. In: 2017 10th international conference on developments in esystems engineering (DeSE), pp 62–67. IEEE. https://doi.org/10.1109/DeSE.2017.21

  • Alkaim AF, Al-Janabi S (2019) Multi objectives optimization to gas flaring reduction from oil production. In: International conference on big data and networks technologies, pp 117–139. Springer, Cham. https://doi.org/10.1007/978-3-030-23672-4_10

  • Alshanyour A, Baroudi U (2010) A simulation study: the impact of random and realistic mobility models on the performance of bypass-AODV in ad hoc wireless networks. EURASIP J Wirel Commun Netw 2010:1–10

  • Amaldi E, Capone A, Malucelli F, Mannino C (2006) Optimization problems and models for planning cellular networks. In: Resende M, Pardalos P (eds) Handbook of optimization in telecommunication, vol 31. Springer, New York, pp 879–901

    MATH  Google Scholar 

  • Amaldi E, Capone A, Malucelli F (2008) Radio planning and coverage optimization of 3G cellular networks. Wirel Netw 14(4):435–447. https://doi.org/10.1007/s11276-006-0729-3

    Article  Google Scholar 

  • Asuquo DE, Williams EE, Nwachukwu EO, Inyang UG (2013) An intelligent call admission control scheme for quality of service provisioning in a multi-traffic CDMA network. Int J Sci Eng Res 4(2):152–161

    Google Scholar 

  • Deng W, Zhao H, Zou L, Li G, Yang X, Wu D (2017) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21:4387–4398. https://doi.org/10.1007/s00500-016-2071-8

    Article  Google Scholar 

  • Deng W, Xu J, Zhao H (2019) An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access 7:20281–20292. https://doi.org/10.1109/ACCESS.2019.2897580

    Article  Google Scholar 

  • Doo-Won L, Gye-Tae G, Dong-Hoi K (2010) A cost-based adaptive handover hysteresis scheme to minimize the handover failure rate in 3GPP LTE system. EURASIP J Wirel Commun Netw 10:1–7. https://doi.org/10.1155/2010/750173

    Article  Google Scholar 

  • Ekpenyong ME, Asuquo DE (2017) HMM-based quality of service survivability in mobile cellular networks. J Niger Math Soc 35:191–217

    MathSciNet  Google Scholar 

  • Ekpenyong M, Isabona J (2011) An enhanced SINR-based call admission control in 3G networks. Int J Wirel Mob Netw 3(5):49–64. https://doi.org/10.5121/ijwmn.2011.3505

    Article  Google Scholar 

  • Ekpenyong M, Isabona J, Isong E (2015) Handoffs decision optimization of mobile cellular networks. In: Proceedings of international conference on computational science and computational intelligence, pp 697–702. https://doi.org/10.1109/CSCI.2015.155

  • Ekpenyong ME, Inyang UG, Asuquo DE, Ekong UO, Usip PU, Umoh UA, Aniekpeno J, Isabona J, Tom A (2018) Intelligent test bed tuning for improved wireless local area networks service quality in academic environments. Int J Artif Intell 16(1):60–87

    Google Scholar 

  • Ekpenyong ME, Asuquo DE, Umoren IJ (2019) Evolutionary optimisation of energy-efficient communication in wireless sensor networks. Int J Wirel Inf Netw. https://doi.org/10.1007/s10776-019-00450-x

    Article  Google Scholar 

  • Gangwar AK, Singh VV (2014) Quality of service improvement, handoff prioritization and channel utilization for cellular network. Int J Eng Res Appl 4(10):46–49

    Google Scholar 

  • Gehlod L, Jain V, Sharma GJ (2013) Handover management using adaptive and prioritization scheme in cellular mobile systems. Int J Adv Res Comput Commun Eng 2(7):2689–2692

    Google Scholar 

  • Ghaderi M, Boutaba R (2006) Call admission control in mobile cellular network: a comprehensive survey. Wirel Commun Mob Comput 6(1):69–93. https://doi.org/10.1002/wcm.246

    Article  Google Scholar 

  • Gharbi N (2016) Using GSPNs for performance analysis of a new admission control strategy with retrials and guard channels. In: Kim K, Wattanapongsakorn N, Joukov N (eds) 2016 mobile and wireless technologies lecture notes in electrical engineering, vol 391. Springer, Singapore

    Google Scholar 

  • Hamid NIB, Kawser MT, Hoque MA (2012) Coverage and capacity analysis of LTE radio network planning considering Dhaka city. Int J Comput Appl 46(15):49–56. https://doi.org/10.5120/6989-9604

    Article  Google Scholar 

  • Huang J, Huang CY, Chou CM (2004) Soft-blocking based resource dimensioning for CDMA systems. Proc IEEE Veh Technol Conf 6:4306–4309

    Google Scholar 

  • Huang R, Zhang C, Zhao H, Fang Y (2010) Enhancing handoff performance by introducing Ad-hoc mode into cellular networks. In: Proceedings of IEEE global telecommunications conference, Miami, pp 1–5

  • Isabona J, Ekpenyong M (2009) Performance evaluation of coverage capacity interaction in CDMA wireless networks. J Niger Math Soc 28:153–167

    MATH  Google Scholar 

  • ITU-D (2007) Telecom network planning for evolving network architectures: reference manual, draft version 4.1, document NPM/4.1, Geneva

  • Iversen VB, Stepanov SN, Kostrov AV (2006) Dimensioning of multiservice links taking account of soft blocking. In: International conference on next generation wired/wireless networking, pp 3–10. Springer, Berlin

  • Kar RR, Nayak SS (2014) An efficient adaptive channel allocation scheme for cellular networks. IOSR J Comput Eng 16(2):75–79

    Article  Google Scholar 

  • Katzis K, Pearce DAJ, Grace D (2004) Fixed channel allocation techniques exploiting cell overlap for high altitude platforms. In: Proceedings of 5th European wireless conference mobile and wireless systems beyond 3G, Barcelona, Spain, Academic Press

  • Kolate VS, Patil GI, Bhide AS (2012) Call admission control schemes and handoff prioritization in 3G wireless mobile networks. Int J Eng Innov Technol 1(3):92–97

    Google Scholar 

  • Kumar S, Kumar K, Pandey AK (2016) Dynamic channel allocation in mobile multimedia networks using error back propagation and hopfield neural network (EHP-HOP). In: Proceedings of 12th international multi-conference on information processing, procedia computer science, vol 89, pp 107–116

  • Liu Z-H, Chen J-C (2012) Design and analysis of the gateway relocation and admission control algorithm in mobile WiMax networks. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2010.265

    Article  Google Scholar 

  • Mahalungkar SP, Sambare SS (2013) Improved call blocking probability reducing technique using auxiliary stations. Int J Sci Eng Res 4(3):1–5

    Google Scholar 

  • Nie S, Wu D, Zhao M, Gu X, Zhang L, Lu L (2015) An enhanced mobility state estimation based handover optimization algorithm in LTE-A self-organizing network. In: Proceedings of 6th international conference on ambient systems, networks and technologies, vol 52, pp 270–277. https://doi.org/10.1016/j.procs.2015.05.078

  • Nyangaresi VO, Abeka SO, Rodgrigues A (2018) Security evaluation of cellular networks handover techniques. Int J Comput Netw Inf Secur 5:45–59. https://doi.org/10.5815/ijcnis.2018.05.06

    Article  Google Scholar 

  • Parija S, Nanda SK, Sahu PK, Singh SS (2013) Location prediction of mobility management using soft computing techniques in cellular network. Int J Comput Netw Inf Secur 6:27–33

    Google Scholar 

  • Prakash R, Shivaratri NG, Singhal M (2015) Distributed dynamic fault-tolerant channel allocation for mobile computing. IEEE Trans Veh Technol 52:47–56

    MATH  Google Scholar 

  • Premkumar N, Kannan M (2015) Optimal fast handover algorithm to reduce handover latency in wireless communication. Int J Adv Eng 1(3):378–381

    Google Scholar 

  • Shamar A, Kumar D (2011) Call blocking performance of new reservation-based channel assignment scheme in cellular networks. In: Proceedings of IJCA special issue on 2nd national conference on computing, communication and sensor network, pp 6–9

  • Sriwastava VP, Singh J, Verma VK (2014) Decreasing call blocking rate by using optimization technique. Int J Sci Res Publ 4(6):1–5

    Google Scholar 

  • Udoh SS, Akinyokun OC, Inyang UG, Olabode O, Iwasokun GB (2017) Discrete event based hybrid framework for petroleum products pipeline activities classification. J Artif Intell Res 6(2):39–50

    Article  Google Scholar 

  • Walia N, Singh H, Sharma A (2015) ANFIS: adaptive neuro-fuzzy inference system—a survey. Int J Comput Appl 123(13):32–38

    Google Scholar 

  • Zeng Q-A, Agrawal DP (2003) Modeling and efficient handling of handoff in integrated wireless mobile networks. IEEE Trans Veh Technol 51(6):1469–1478

    Article  Google Scholar 

  • Zhang J, Guo L, Yang J, Wu TY (2004) Automation of 3G/4G cellular network planning. In: Proceedings of IEEE 5th international conference on 3G mobile communications technologies, London, pp 412–416

  • Zhao C, Gan L (2011) Dynamic channel assignment for large-scale cellular networks using noisy chaotic neural network. IEEE Trans Neural Netw 22:222–232. https://doi.org/10.1109/TNN.2010.2091653

    Article  Google Scholar 

  • Zhao X, Liang H, Gu Z (2008) A Markov chain-based capacity dimensioning method for wireless communication system with AMC, HARQ and packet multimedia traffic source. IEEE Int 1:2. https://doi.org/10.1109/ICC.2008.86

    Article  Google Scholar 

  • Zhao H, Liu H, Xu J, Deng W (2019) Performance prediction using high-order differential mathematical morphology gradient spectrum entropy and extreme learning machine. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2019.2948414

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank all the reviewers for their constructive comments. This research was supported by the Tertiary Education Trust Fund (TETFund)-Nigeria, research Grant. The program for the study, training and simulation of the proposed algorithm in this article was written in the toolbox of MATLAB R2015a produced by the Math-Works, Inc.

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Science University of Uyo, Uyo, Nigeria

    Daniel Asuquo, Moses Ekpenyong, Samuel Udoh & Samuel Robinson

  2. Department of Mathematics and Computer Science, Ritman University, Ikot Ekpene, Nigeria

    Kingsley Attai

Authors
  1. Daniel Asuquo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moses Ekpenyong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samuel Udoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samuel Robinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kingsley Attai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Asuquo.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest for any of them in this article.

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Asuquo, D., Ekpenyong, M., Udoh, S. et al. Optimized channel allocation in emerging mobile cellular networks. Soft Comput 24, 16361–16382 (2020). https://doi.org/10.1007/s00500-020-04947-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04947-z

Keywords

Search

Navigation