Virtual Spectrum Hole: Exploiting User Behavior-Aware Time-Frequency Resource Conversion

In this paper, to address network congestion stemmed from traffic generated by advanced user equipment, we propose a novel network resource allocation strategy, i.e., time-frequency resource conversion (TFRC), via exploiting user behavior, a specific kind of context information. The key idea is to use radio resources mainly on the traffic/connection to which a user pays attention. The TFRC withdraws spectrum resources strategically from connection(s) not focused on by the user, providing reuseable spectrum called “virtual spectrum hole”. Considering an LTE-type cellular network, a double-threshold guard channel policy is proposed to facilitate the implementation of TFRC. An analytical model is established to study benefits of exploiting TFRC in terms of call-level performance, including new call blocking, handoff call dropping, and recovering call dropping probabilities. Numerical results demonstrate the effectiveness of the proposed approach, in increasing the cell capacity (maximum user number per cell) while limiting potential service quality degradation introduced by the newly proposed technique.

[1]  Stefan Valentin,et al.  Simple Channel Predictors for Lookahead Scheduling , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[2]  Xuemin Shen,et al.  Impact of Network Dynamics on User's Video Quality: Analytical Framework and QoS Provision , 2010, IEEE Transactions on Multimedia.

[3]  Anja Feldmann,et al.  A First Look at Mobile Hand-Held Device Traffic , 2010, PAM.

[4]  Don Towsley,et al.  Personal & wireless communications: digital technology & standards , 1997, MOCO.

[5]  Stefan Valentin,et al.  Context-Aware Resource Allocation to Improve the Quality of Service of Heterogeneous Traffic , 2011, 2011 IEEE International Conference on Communications (ICC).

[6]  Samir Ranjan Das,et al.  Understanding traffic dynamics in cellular data networks , 2011, 2011 Proceedings IEEE INFOCOM.

[7]  Aggeliki Sgora,et al.  Handoff prioritization and decision schemes in wireless cellular networks: a survey , 2009, IEEE Communications Surveys & Tutorials.

[8]  Sheldon M. Ross,et al.  Introduction to probability models , 1975 .

[9]  Dusit Niyato,et al.  Radio resource management games in wireless networks: an approach to bandwidth allocation and admission control for polling service in IEEE 802.16 [Radio Resource Management and Protocol Engineering for IEEE 802.16] , 2007, IEEE Wireless Communications.

[10]  Adam Wolisz,et al.  Primary user behavior in cellular networks and implications for dynamic spectrum access , 2009, IEEE Communications Magazine.

[11]  Phuoc Tran-Gia,et al.  Code Division Multiple Access wireless network planning considering clustered spatial customer traffic , 1998 .

[12]  Hai Zhou,et al.  Deprioritization of heavy users in wireless networks , 2011, IEEE Communications Magazine.

[13]  Myung-Sup Kim,et al.  A study on Smart-phone traffic analysis , 2011, 2011 13th Asia-Pacific Network Operations and Management Symposium.

[14]  Weihua Zhuang,et al.  State Transition Analysis of Time-Frequency Resource Conversion-based Call Admission Control for LTE-Type Cellular Network , 2013, ArXiv.

[15]  Stefan Valentin,et al.  Context-aware resource allocation for cellular wireless networks , 2012, EURASIP J. Wirel. Commun. Netw..

[16]  Hossam S. Hassanein,et al.  Congestion Pricing in Wireless Cellular Networks , 2011, IEEE Communications Surveys & Tutorials.

[17]  Markus Fiedler,et al.  A generic quantitative relationship between quality of experience and quality of service , 2010, IEEE Network.

[18]  SawahashiMamoru,et al.  Coordinated multipoint transmission/reception techniques for LTE-advanced , 2010 .

[19]  Meryem Simsek,et al.  When cellular meets WiFi in wireless small cell networks , 2013, IEEE Communications Magazine.

[20]  Hsiao-Hwa Chen,et al.  Queueing analysis for OFDM subcarrier allocation in broadband wireless multiservice networks , 2008, IEEE Transactions on Wireless Communications.

[21]  Walid Saad,et al.  Matching with externalities for context-aware user-cell association in small cell networks , 2013, 2013 IEEE Globecom Workshops (GC Wkshps).

[22]  Hossam S. Hassanein,et al.  Efficient lookahead resource allocation for stored video delivery in multi-cell networks , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).

[23]  Sheldon M. Ross,et al.  Introduction to Probability Models (4th ed.). , 1990 .

[24]  Geng-Sheng Kuo,et al.  An Efficient Admission Control Scheme for Adaptive Multimedia Services in IEEE 802.16e Networks , 2006, IEEE Vehicular Technology Conference.

[25]  Mamoru Sawahashi,et al.  Coordinated multipoint transmission/reception techniques for LTE-advanced [Coordinated and Distributed MIMO] , 2010, IEEE Wireless Communications.

[26]  Hossam S. Hassanein,et al.  Predictive green wireless access: exploiting mobility and application information , 2013, IEEE Wireless Communications.

[27]  Deborah Estrin,et al.  Diversity in smartphone usage , 2010, MobiSys '10.

[28]  Hossam S. Hassanein,et al.  Optimal predictive resource allocation: Exploiting mobility patterns and radio maps , 2013, 2013 IEEE Globecom Workshops (GC Wkshps).

[29]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[30]  Shengli Xie,et al.  Cross-Layer Optimized Call Admission Control in Cognitive Radio Networks , 2010, Mob. Networks Appl..