Network capacity analysis for cellular based cognitive radio VANET in urban grid scenario

The spectrum scarcity of VANETs (Vehicular Ad hoc Networks) can be alleviated by spectrum sharing technology. We present a framework of CCR-VANETs (Cellular-based Cognitive-radio Vehicular Ad hoc Networks). In CCR-VANETs, cellular network performs as primary network while VANET shares the downlink spectrum of cellular network. We consider a scalable urban grid scenario in which vehicles detect available spectrum holes and opportunistically access them according to a carrier-sensing multiple-access protocol. To restrict vehicles’ interference to primary receivers, we set a square preservation region around each particular street block where an active base station is located. The number of street blocks in the preservation region is calculated with the practical assumption that vehicles only know the locations of primary transmitters. We analyze the aggregate interference power from primary and secondary networks, then derive the lower-bound of downlink capacity for the primary network and lower-bound of V2V (Vehicle-to-Vehicle) channel capacity for the secondary network respectively. The numerical results demonstrate the impacts of different network parameters on inter-networks interference level and network capacities.

[1]  A Siksna,et al.  The effects of block size and form in North American and Australian city centres , 1997 .

[2]  Luciano Bononi,et al.  Analyzing the potential of cooperative Cognitive Radio technology on inter-vehicle communication , 2010, 2010 IFIP Wireless Days.

[3]  François Baccelli,et al.  Stochastic geometry and wireless networks , 2009 .

[4]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[5]  Hassan Artail,et al.  Improving reliability of safety applications in vehicle ad hoc networks through the implementation of a cognitive network , 2010, 2010 17th International Conference on Telecommunications.

[6]  M. Haenggi,et al.  Interference in Large Wireless Networks , 2009, Found. Trends Netw..

[7]  Xuemin Shen,et al.  Bounds of Asymptotic Performance Limits of Social-Proximity Vehicular Networks , 2014, IEEE/ACM Transactions on Networking.

[8]  Athanasios V. Vasilakos,et al.  Computation of an Equilibrium in Spectrum Markets for Cognitive Radio Networks , 2014, IEEE Transactions on Computers.

[9]  N.B. Shroff,et al.  Joint resource allocation and base-station assignment for the downlink in CDMA networks , 2006, IEEE/ACM Transactions on Networking.

[10]  Athanasios V. Vasilakos,et al.  QoE-Driven Channel Allocation Schemes for Multimedia Transmission of Priority-Based Secondary Users over Cognitive Radio Networks , 2012, IEEE Journal on Selected Areas in Communications.

[11]  Xuemin Shen,et al.  Connected Vehicles: Solutions and Challenges , 2014, IEEE Internet of Things Journal.

[12]  P. R. Kumar The Capacity of Wireless Networks yz , 2003 .

[13]  Zhang Ning,et al.  Software defined Internet of vehicles: architecture, challenges and solutions , 2016 .

[14]  Athanasios V. Vasilakos,et al.  Routing Metrics of Cognitive Radio Networks: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[15]  Jeffrey G. Andrews,et al.  Transmission capacity of wireless ad hoc networks with outage constraints , 2005, IEEE Transactions on Information Theory.

[16]  Vincent K. N. Lau,et al.  Spectrum sharing between cellular and mobile ad hoc networks: transmission-capacity trade-off , 2008, IEEE Journal on Selected Areas in Communications.

[17]  Weihua Zhuang,et al.  Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey , 2016, IEEE Transactions on Vehicular Technology.

[18]  Fan Zhang,et al.  Characterizing On-Bus WiFi Passenger Behaviors by Approximate Search and Cluster Analysis , 2016, 2016 7th International Conference on Cloud Computing and Big Data (CCBD).

[19]  Shuguang Cui,et al.  Generalized results of transmission capacities for overlaid wireless networks , 2009, 2009 IEEE International Symposium on Information Theory.

[20]  Seong Keun Oh,et al.  Cognitive Ad-hoc Networks under a Cellular Network with an Interference Temperature Limit , 2008, 2008 10th International Conference on Advanced Communication Technology.

[21]  Bhaskar Krishnamachari,et al.  Exploiting the wisdom of the crowd: localized, distributed information-centric VANETs [Topics in Automotive Networking] , 2010, IEEE Communications Magazine.

[22]  Li Zhao,et al.  LTE-V: A TD-LTE-Based V2X Solution for Future Vehicular Network , 2016, IEEE Internet of Things Journal.

[23]  Tao Luo,et al.  Survey of Cognitive Radio VANET , 2014, KSII Trans. Internet Inf. Syst..

[24]  Jean-Marie Bonnin,et al.  Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges , 2014, EURASIP J. Wirel. Commun. Netw..

[25]  Philippe Jacquet,et al.  Mean Number of Transmissions with CSMA in a Linear Network , 2010, 2010 IEEE 72nd Vehicular Technology Conference - Fall.

[26]  Xuemin Shen,et al.  Vehicles Meet Infrastructure: Toward Capacity–Cost Tradeoffs for Vehicular Access Networks , 2013, IEEE Transactions on Intelligent Transportation Systems.

[27]  Soheil Feizi,et al.  Lower and Upper Bounds for Throughput Capacity of a Cognitive Ad Hoc Network Overlaid on a Cellular Network , 2008, 2008 IEEE Wireless Communications and Networking Conference.

[28]  Jiming Chen,et al.  VTube: Towards the media rich city life with autonomous vehicular content distribution , 2011, 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[29]  S. Kostof The City Shaped: Urban Patterns and Meanings Through History , 1991 .

[30]  Michele Garetto,et al.  Restricted Mobility Improves Delay-Throughput Tradeoffs in Mobile Ad Hoc Networks , 2008, IEEE Transactions on Information Theory.