Performance and Comparative Analysis of ADSA in a Vehicular Network: MAC Approach in IEEE 802.11p

Vehicular network (VN) technologies have become an attractive area of attention all over the world. Two factors that have contributed in the development, design and implementation of the VN standards include the need to ensure safety and the need to consider road accident avoidance strategies. However, the innate dynamic and the high topological mobility of the nodes in Vehicular Ad Hoc Networks (VANETs) raise complex and challenging issues with the standard. One of the complexities is the problem posed by Doppler Effect (DE) resulting from the high mobility of the VANET nodes. In an attempt to compensate the induced Doppler Shift (DS), Automatic Doppler Shift Adaptation (ADSA) was recently introduced to combat DE in a VANET. ADSA proved to be more resilient and effective in term of Bit Error Rate (BER). Moreover, for realistic applications, BER tests alone are insufficient. Therefore, this work explores the strength of the refined ADSA method in terms of throughput and presents a comparative analysis of ADSA versus Adaptive Modulation Code (AMC) and Auto-Rate Fallback (ARF). Results from the analysis shows that the ADSA approach demonstrates strong robustness compared to AMC and ARF with up to 44 to 55% improvement in throughput and a 174 to 182% reduction in consumed time.

[1]  Karim Djouani,et al.  An MCS Adaptation Technique for Doppler Effect in IEEE 802.11p Vehicular Networks , 2013, ANT/SEIT.

[2]  Karim Djouani,et al.  Doppler Effect Analysis and Modulation Code Derivation , 2012, ANT/MobiWIS.

[3]  Rafidah Md Noor,et al.  OPTIMIZING WIRELESS CHANNEL USING ADAPTIVE MODULATION TO IMPROVE QOS IN VANET , 2013 .

[4]  Baoyu Zheng,et al.  Image-based Position Estimation and Adaptive Modulation Coding in Vehicular Communication , 2011, J. Networks.

[5]  Jun Chen,et al.  Link Adaptation for Cooperative Wireless LANs , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[6]  Liviu Iftode,et al.  CARS: Context-Aware Rate Selection for vehicular networks , 2008, 2008 IEEE International Conference on Network Protocols.

[7]  Thiago Meireles Paixão,et al.  A 802.11p prototype implementation , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[8]  Nj Piscataway,et al.  Wireless LAN medium access control (MAC) and physical layer (PHY) specifications , 1996 .

[9]  Thierry Turletti,et al.  IEEE 802.11 rate adaptation: a practical approach , 2004, MSWiM '04.

[10]  Karim Djouani,et al.  A Survey of IEEE 802 . 11 p MAC Protocol , .

[11]  Lambros Lambrinos,et al.  Dynamically adjusting the min-max contention window for providing quality of service in vehicular networks , 2012, 2012 The 11th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[12]  Leo Monteban,et al.  WaveLAN®-II: A high-performance wireless LAN for the unlicensed band , 1997, Bell Labs Technical Journal.

[13]  Vinny Cahill,et al.  Vehicular Networks and Applications , 2009, Middleware for Network Eccentric and Mobile Applications.

[14]  Rahim Tafazolli,et al.  Analytical Study of the IEEE 802.11p MAC Sublayer in Vehicular Networks , 2012, IEEE Transactions on Intelligent Transportation Systems.

[15]  Sunghyun Choi,et al.  Link adaptation strategy for IEEE 802.11 WLAN via received signal strength measurement , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[16]  Kenneth Tze Kin Teo,et al.  A Wireless Network with Adaptive Modulation and Network Coding in Intelligent Transportation Systems , 2012, 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation.