Self-Resource Allocation and Scheduling Challenges for Heterogeneous Networks Deployment

A reliable solution for meeting the high demand of throughput in areas called hotspots is the heterogeneous network. Heterogeneous networks are different depending on their coverage, their type of radio access technique and the way there are connected to the core network. This paper proposes a novel algorithm for semi-coordinated resource allocation and scheduling based on mobile positioning information, game theory and reinforcement learning technique. The capabilities of such an approach to support the practical deployment of heterogeneous networks is analyzed. Further, a reasoning strategy is proposed to justify the choice of Wi-Fi versus other small cell technologies from a practical deployment viewpoint.

[1]  Mehdi Bennis,et al.  On spectrum sharing with underlaid femtocell networks , 2010, 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops.

[2]  Ramjee Prasad,et al.  Use of positioning information for performance enhancement of uncoordinated heterogeneous network deployment , 2013, Wireless VITAE 2013.

[3]  Stephen B. Wicker,et al.  Game theory and the design of self-configuring, adaptive wireless networks , 2001, IEEE Commun. Mag..

[4]  Rong Peng,et al.  Angle of Arrival Localization for Wireless Sensor Networks , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[5]  Mehdi Bennis,et al.  A Q-learning based approach to interference avoidance in self-organized femtocell networks , 2010, 2010 IEEE Globecom Workshops.

[6]  Christophe Diot,et al.  An Experimental Performance Comparison of 3G and Wi-Fi , 2010, PAM.

[7]  András Rácz,et al.  Intercell Interference Coordination in OFDMA Networks and in the 3GPP Long Term Evolution System , 2009, J. Commun..

[8]  Jeffrey G. Andrews,et al.  Femtocell networks: a survey , 2008, IEEE Communications Magazine.

[9]  Anjali Agarwal,et al.  Spectrum sharing in multi-service cognitive network using reinforcement learning , 2009, 2009 First UK-India International Workshop on Cognitive Wireless Systems (UKIWCWS).

[10]  Bo Li,et al.  Inter-cell downlink co-channel interference management through cognitive sensing in heterogeneous network for LTE-A , 2011 .

[11]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[12]  Xi Li,et al.  Cross-layer scheduling with fairness for multi-user OFDM system: a restless bandit approach , 2011 .

[13]  Erik G. Ström,et al.  Improved Position Estimation Using Hybrid TW-TOA and TDOA in Cooperative Networks , 2012, IEEE Transactions on Signal Processing.

[14]  Wei Kuang Lai,et al.  Application of support vector machines to bandwidth reservation in sectored cellular communications , 2005, Eng. Appl. Artif. Intell..

[15]  Zesong Fei,et al.  Heterogeneous network in LTE-advanced system , 2010, 2010 IEEE International Conference on Communication Systems.

[16]  Rui Chang,et al.  Interference coordination and cancellation for 4G networks , 2009, IEEE Communications Magazine.

[17]  Vladimir Poulkov,et al.  Combined power and inter-cell interference control for LTE based on role game approach , 2014, Telecommun. Syst..

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

[19]  Marc Werner,et al.  Cost Assessment of Radio Access Network Deployments with Relay Nodes , 2008 .