Robust resource allocation scheme under channel uncertainties for LTE-A systems

Nowadays, resource allocation is one of the major problems in the cellular networks. Due to the increasing number of autonomous heterogeneous devices in future mobile networks, a proper scheduling scheme is required to provide the adequate resources for the service flow. However, provisioning quality-of-service (QoS) for real-time applications with the constraint on transporting delay is hard to achieve without compromising other QoS parameters. In this paper, an intelligent QoS-aware bandwidth allocation solution is proposed for the uplink traffic when the channel condition is uncertain. The system is designed based on a specific maximum latency assurance for real-time applications as well as considering fairness to the throughput of non-real-time services. The scheduling system employs a channel-aware Kalman filter based interval type-2 fuzzy logic controller to estimate channel uncertainty as well as satisfying the QoS requirements for user equipment. Through simulations, the performance of the proposed system in terms of optimal bandwidth allocation, bandwidth wastage, fairness, jitters, various delays and throughputs for delay sensitive and delay tolerant services is analyzed. The numerical results show that the proposed scheme provides reliable scheduling for real-time services without harming the performance of non-real-time QoS parameters.

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

[2]  Claus Pahl,et al.  Autonomic resource provisioning for cloud-based software , 2014, SEAMS 2014.

[3]  Saeid M. Jafari,et al.  BANDWIDTH ALLOCATION IN WIMAX NETWORKS USING REINFORCEMENT LEARNING , 2011 .

[4]  Matthew Baker,et al.  From LTE-advanced to the future , 2012, IEEE Communications Magazine.

[5]  Kamran Arshad,et al.  Energy-Efficient Resource Allocation for LTE-A Networks , 2016, IEEE Communications Letters.

[6]  Tsung-Chih Lin,et al.  Synchronization of Fuzzy Modeling Chaotic Time Delay Memristor-Based Chua’s Circuits with Application to Secure Communication , 2015, Int. J. Fuzzy Syst..

[7]  N. Rengarajan,et al.  An Intelligent Technique to Detect Jamming Attack in Wireless Sensor Networks (WSNs) , 2015, Int. J. Fuzzy Syst..

[8]  Hossein Bobarshad,et al.  Robust Uplink Resource Allocation in LTE Networks with M2M Devices as an Infrastructure of Internet of Things , 2016, 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud).

[9]  Hassan Ouahmane,et al.  Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory , 2017, Int. J. Commun. Networks Inf. Secur..

[10]  El-Sayed M. El-Rabaie,et al.  Resource allocation for Real-Time services using Earliest Due Date mechanism in LTE networks , 2016, 2016 Fourth International Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC).

[11]  Hani Hagras,et al.  Interval Type-2 Fuzzy Logic Congestion Control for Video Streaming Across IP Networks , 2009, IEEE Transactions on Fuzzy Systems.

[12]  Mehdi Bennis,et al.  Delay-sensitive resource allocation for relay-aided M2M communication over LTE-advanced networks , 2017, 2017 IEEE Symposium on Computers and Communications (ISCC).

[13]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[14]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[15]  Manisha Kaushal,et al.  Proposal and Evaluation of a Fuzzy Logic-Driven Resource Allocation Mechanism , 2017, Int. J. Fuzzy Syst..

[16]  Arkadi Nemirovski,et al.  Robust solutions of uncertain linear programs , 1999, Oper. Res. Lett..

[17]  Hossam S. Hassanein,et al.  Robust resource allocation for predictive video streaming under channel uncertainty , 2014, 2014 IEEE Global Communications Conference.

[18]  Xuemin Shen,et al.  Operator controlled device-to-device communications in LTE-advanced networks , 2012, IEEE Wireless Communications.

[19]  Jerry M. Mendel,et al.  MPEG VBR video traffic modeling and classification using fuzzy technique , 2001, IEEE Trans. Fuzzy Syst..

[20]  Lili Zhang,et al.  Adaptive Fuzzy Synchronization for Uncertain Chaotic Systems with Different Dimensions and Disturbances , 2015, Int. J. Fuzzy Syst..

[21]  You-Chiun Wang,et al.  A Pricing-Aware Resource Scheduling Framework for LTE Networks , 2017, IEEE/ACM Transactions on Networking.

[22]  H. Hagras,et al.  Type-2 FLCs: A New Generation of Fuzzy Controllers , 2007, IEEE Computational Intelligence Magazine.

[23]  Erik Dahlman,et al.  3G Evolution: HSPA and LTE for Mobile Broadband , 2007 .

[24]  Junyi Li,et al.  Toward proximity-aware internetworking , 2010, IEEE Wireless Communications.

[25]  R. Piché Online tests of Kalman filter consistency , 2016 .

[26]  Bob Newell An introduction to fuzzy control : by D. Driankov, H. Hellendoorn and M. Reinfrank (Springer-Verlag, Berlin, 1993, ISBN 3 540 56362 8, 316 pp, SwFr 97) , 1994 .

[27]  Borhanuddin Mohd Ali,et al.  Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin , 2014, J. Netw. Comput. Appl..

[28]  Chung-Ju Chang,et al.  An Intelligent Priority Resource Allocation Scheme for LTE-A Downlink Systems , 2012, IEEE Wireless Communications Letters.

[29]  Abdorasoul Ghasemi,et al.  Overload control in the network domain of LTE/LTE-A based machine type communications , 2018, Wirel. Networks.

[30]  Javier Romero,et al.  GSM, Gprs and Edge Performance , 2003 .

[31]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[32]  Muhammad Zeeshan,et al.  A utility based resource allocation scheme with delay scheduler for LTE service-class support , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[33]  Mohammad Ghanbari,et al.  On the achievable rate bounds in multi-pair massive antenna relaying with correlated antennas , 2019, Wirel. Networks.

[34]  M. Mary Linda,et al.  A Comprehensive Study on Efficient Resource Allocation by QoS in Wireless Networks , 2017 .

[35]  Qilian Liang,et al.  Wireless Sensor Network Lifetime Analysis Using Interval Type-2 Fuzzy Logic Systems , 2005, IEEE Transactions on Fuzzy Systems.

[36]  Jerry M. Mendel,et al.  General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial , 2014, IEEE Transactions on Fuzzy Systems.

[37]  P.J. King,et al.  The application of fuzzy control systems to industrial processes , 1977, Autom..

[38]  Yuan-Cheng Lai,et al.  Highest Urgency First (HUF): A latency and modulation aware bandwidth allocation algorithm for WiMAX base stations , 2009, Comput. Commun..

[39]  Jerry M. Mendel,et al.  Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[40]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[41]  Juan Melero,et al.  GSM, GPRS and EDGE Performance: Evolution Toward 3G/UMTS , 2002 .

[42]  Satoshi Nagata,et al.  LTE-advanced: an operator perspective , 2012, IEEE Communications Magazine.

[43]  Nadeem Javaid,et al.  A Survey on Fuzzy Logic Applications in Wireless and Mobile Communication for LTE Networks , 2016, 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS).