Robust Vehicular Communications Using the Fast-Frequency-Hopping-OFDM Technology and the MIMO Spatial Multiplexing

Vehicle-to-Vehicle communication is one of the more emerging technologies in the 21st century from either the comfortable transportation or safer transportation point of view. Vehicle-to-Vehicle communication has one crucial factor, which is the huge information to be shared among vehicles, such as the position, the road data. In such situation, the accurate information sharing process is the most important factor in order to make the vehicles operating in the most feasible way. This work proposes a more robust vehicle communication system to make the existing vehicle transportation system more efficient. In this paper, we propose a fast frequency hopping orthogonal frequency division multiplexing to mitigate the Doppler spread effect on our previously published clustering benchmark.  This benchmark contains both of a clustering weighting factor based stage and a multiparallel processing stage. This is in addition to modify the PHY layer of the existing IEEE 802.11p standard in order to impose Multiple Input Multiple Output for higher throughput purposes.The results show a noticeable stability compared to our previously published work. Furthermore, the results are almost exceeds the achieved results from the Lower-ID Distributed Clustering Algorithm (DCA) from both of the speed and communication range

Towards understanding the performance of IEEE 802.11 MAC in heterogeneous traffic conditions

Motivated by the need to study the performance of vehicular communication protocols as applicable to heterogeneous traffic conditions, we study the performance of IEEE 802.11p medium access protocol under such a traffic setup. We consider a setup comprising connected vehicles and human-driven Motorised Two Wheelers (MTWs), where the connected vehicles are required to move as platoon with a desired constant headway despite interruptions from the two wheelers. We invoke specific mobility models for the movement of the vehicles--car following models for connected vehicle platoons and gap-acceptance model to capture the movement of the MTWs--and use them to configure (i) the traffic setup and (ii) the rate at which data packets related to safety-critical messages need to be transmitted. A control-theoretic analysis of the car-following models yields a bound on the admissible communication delay to ensure non-oscillatory convergence of the platoon headway. We then use suitable Markov chain models to derive the distribution of the MAC access delay experienced by packets pertaining to safety-critical events as well as routine safety messages. The distribution along with the bound on the admissible delay enables us to derive the reliability of the 802.11p MAC protocol in terms of traffic and EDCA parameters. Our study highlights the need for redesign of MAC protocols for vehicular communications for safety-critical applications in heterogeneous conditions.Comment: 15 page

Time Dependent Performance Analysis of Wireless Networks

Many wireless networks are subject to frequent changes in a combination of network topology, traffic demand, and link capacity, such that nonstationary/transient conditions always exist in packet-level network behavior. Although there are extensive studies on the steady-state performance of wireless networks, little work exists on the systematic study of their packet-level time varying behavior. However, it is increasingly noted that wireless networks must not only perform well in steady state, but must also have acceptable performance under nonstationary/transient conditions. Furthermore, numerous applications in today's wireless networks are very critical to the real-time performance of delay, packet delivery ratio, etc, such as safety applications in vehicular networks and military applications in mobile ad hoc networks. Thus, there exists a need for techniques to analyze the time dependent performance of wireless networks. In this dissertation, we develop a performance modeling framework incorporating queuing and stochastic modeling techniques to efficiently evaluate packet-level time dependent performance of vehicular networks (single-hop) and mobile ad hoc networks (multi-hop). For vehicular networks, we consider the dynamic behavior of IEEE 802.11p MAC protocol due to node mobility and model the network hearability as a time varying adjacency matrix. For mobile ad hoc networks, we focus on the dynamic behavior of network layer performance due to rerouting and model the network connectivity as a time varying adjacency matrix. In both types of networks, node queues are modeled by the same fluid flow technique, which follows flow conservation principle to construct differential equations from a pointwise mapping of the steady-state queueing relationships. Numerical results confirm that fluid-flow based performance models are able to respond to the ongoing nonstationary/transient conditions of wireless networks promptly and accurately. Moreover, compared to the computation time of standard discrete event simulator, fluid-flow based model is shown to be a more scalable evaluation tool. In general, our proposed performance model can be used to explore network design alternatives or to get a quick estimate on the performance variation in response to some dynamic changes in network conditions

Time-Dependent Performance Analysis of IEEE 802.11p Vehicular Networks

The performance of vehicular ad hoc networks (VANETs) is subject to frequent changes due to node mobility so that nonstationary/transient network behavior always exists. In addition, safety applications in VANETs are very sensitive to the real-time performance of delay and packet delivery ratio (PDR). Although there are extensive studies on the steady-state (SS) performance analysis of vehicular networks, little work exists on evaluating their time-varying behavior. In this paper, we develop a performance model to efficiently estimate the dynamic behavior of VANETs. In this paper, vehicle's transmission queue is modeled using fluid-flow (FF)-based differential equations, which are solved using numerical methods, whereas the network hearing topology is modeled by a time-varying adjacency matrix that can be determined from stochastic models, measurements, or discrete event simulations. Numerical results illustrate that our performance model is able to promptly respond to the ongoing dynamic conditions of VANETs and provide reasonably accurate performance results