Joint Access Selection and Bandwidth Allocation Algorithm Supporting User Requirements and Preferences in Heterogeneous Wireless Networks

The next generation of heterogeneous wireless networks (HWNs) will integrate various radio access technologies, which will make how to connect mobile users based on the performance parameters of each wireless network and the quality of service requirements (as to enable mobile users to be connected to the most suitable wireless network) a hot topic for HWNs. This paper designs an algorithm for joint access selection and bandwidth allocation in HWNs. Taking into account the environment in which worldwide interoperability for microwave access, long term evolution, and wireless local area network may co-exist, the algorithm uses received signal strength, network load, and user rate requirements as input decision parameters and adjusts the parameters of the membership function in the five-layer fuzzy neural network structure through supervised learning to obtain the score and bandwidth allocation value for each candidate network. The simulation results show that the proposed algorithm can enable users to choose the most suitable network to access and may modify the fuzzy rules and adjust the resource utilization of different networks based on user preferences.

[1]  Gabriel-Miro Muntean,et al.  Game Theory-Based Network Selection: Solutions and Challenges , 2012, IEEE Communications Surveys & Tutorials.

[2]  Hoon Kim,et al.  Joint Resource Allocation for Parallel Multi-Radio Access in Heterogeneous Wireless Networks , 2010, IEEE Transactions on Wireless Communications.

[3]  Awais Ahmad,et al.  Fuzzy based multi-criteria vertical handover decision modeling in heterogeneous wireless networks , 2017, Multimedia Tools and Applications.

[4]  Sylvain Kubler,et al.  A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications , 2016, Expert Syst. Appl..

[5]  Oriol Sallent,et al.  A Framework for JRRM with Resource Reservation and Multiservice Provisioning in Heterogeneous Networks , 2006, Mob. Networks Appl..

[6]  Ning Qian,et al.  On the momentum term in gradient descent learning algorithms , 1999, Neural Networks.

[7]  Dominique Gaïti,et al.  Enabling Vertical Handover Decisions in Heterogeneous Wireless Networks: A State-of-the-Art and A Classification , 2014, IEEE Communications Surveys & Tutorials.

[8]  Mansi S. Subhedar,et al.  Handover Decision in Wireless Heterogeneous Networks Based on Feedforward Artificial Neural Network , 2017 .

[9]  Lusheng Wang,et al.  Mathematical Modeling for Network Selection in Heterogeneous Wireless Networks — A Tutorial , 2013, IEEE Communications Surveys & Tutorials.

[10]  Chonggang Wang,et al.  Handover schemes in heterogeneous LTE networks: challenges and opportunities , 2016, IEEE Wireless Communications.

[11]  Olabisi Emmanuel Falowo,et al.  Network selection in heterogeneous wireless networks using multi-criteria decision-making algorithms: a review , 2017, Wirel. Networks.

[12]  Celal Ceken,et al.  Artificial Neural Network Based Vertical Handoff Algorithm for Reducing Handoff Latency , 2013, Wirel. Pers. Commun..

[13]  Dusit Niyato,et al.  Dynamics of Network Selection in Heterogeneous Wireless Networks: An Evolutionary Game Approach , 2009, IEEE Transactions on Vehicular Technology.

[14]  Mehrdad Taki,et al.  Fuzzy-Based Optimized QoS-Constrained Resource Allocation in a Heterogeneous Wireless Network , 2016, Int. J. Fuzzy Syst..

[15]  Mahrokh G. Shayesteh,et al.  Adaptive handover algorithm in heterogeneous femtocellular networks based on received signal strength and signal-to-interference-plus-noise ratio prediction , 2014, IET Commun..

[16]  Oriol Sallent,et al.  A Novel Approach for Joint Radio Resource Management Based on Fuzzy Neural Methodology , 2008, IEEE Transactions on Vehicular Technology.

[17]  Qinghe Du,et al.  Utility-function-based radio-access-technology selection for heterogeneous wireless networks , 2016, Comput. Electr. Eng..

[18]  Yufeng Li,et al.  QoE-Aware Intelligent Vertical Handoff Scheme Over Heterogeneous Wireless Access Networks , 2018, IEEE Access.

[19]  Chung-Ju Chang,et al.  Fuzzy Q-Learning Admission Control for WCDMA/WLAN Heterogeneous Networks with Multimedia Traffic , 2009, IEEE Transactions on Mobile Computing.

[20]  Rabha W. Ibrahim,et al.  An intelligent selection method based on game theory in heterogeneous wireless networks , 2016, Trans. Emerg. Telecommun. Technol..

[21]  Mykhailo Klymash,et al.  A survey of converging solutions for heterogeneous mobile networks , 2014, IEEE Wireless Communications.

[22]  Brahmjit Singh,et al.  Network Selection in Wireless Heterogeneous Environment by C-P-F Hybrid Algorithm , 2018, Wirel. Pers. Commun..

[23]  Mohammad Hossein Anisi,et al.  MDP-Based Network Selection Scheme by Genetic Algorithm and Simulated Annealing for Vertical-Handover in Heterogeneous Wireless Networks , 2017, Wirel. Pers. Commun..

[24]  Brahmjit Singh,et al.  Particle swarm optimization based network selection in heterogeneous wireless environment , 2014 .

[25]  Sumit Maheshwari,et al.  QoS-aware fuzzy rule-based vertical handoff decision algorithm incorporating a new evaluation model for wireless heterogeneous networks , 2012, EURASIP J. Wirel. Commun. Netw..

[26]  Kok-Lim Alvin Yau,et al.  QoS in IEEE 802.11-based wireless networks: A contemporary review , 2014, J. Netw. Comput. Appl..

[27]  Yongbin Wei,et al.  A survey on 3GPP heterogeneous networks , 2011, IEEE Wireless Communications.

[28]  E. Gustafsson,et al.  Always best connected , 2003, IEEE Wirel. Commun..

[29]  Mung Chiang,et al.  HetNets Selection by Clients: Convergence, Efficiency, and Practicality , 2017, IEEE/ACM Transactions on Networking.

[30]  Ping Dong,et al.  Fuzzy and Utility Based Network Selection for Heterogeneous Networks in High-Speed Railway , 2017, Wirel. Commun. Mob. Comput..

[31]  Pratit Santiprabhob,et al.  Adaptive Multi-fuzzy Engines for Handover Decision in Heterogeneous Wireless Networks , 2017, Wirel. Pers. Commun..

[32]  Jeffrey G. Andrews,et al.  Heterogeneous cellular networks: From theory to practice , 2012, IEEE Communications Magazine.

[33]  Sakshi Kaushal,et al.  The utility based non-linear fuzzy AHP optimization model for network selection in heterogeneous wireless networks , 2018, Appl. Soft Comput..

[34]  Liangrui Tang,et al.  A Heterogeneous Network Access Selection Algorithm Based on Attribute Dependence , 2017, Wirel. Pers. Commun..

[35]  Oriol Sallent,et al.  Fuzzy Neural Control for Economic-Driven Radio Resource Management in Beyond 3G Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).