18,511 research outputs found
Scaling Laws for Vehicular Networks
Equipping automobiles with wireless communications and networking capabilities is becoming the frontier in the evolution to the next generation intelligent transportation systems (ITS). By means of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, information generated by the vehicle-borne computer, vehicle control system, on-board sensors, or roadside infrastructure, can be effectively disseminated among vehicles/infrastructure in proximity or to vehicles/infrastructure multiple hops away, known as vehicular networks (VANETs), to enhance the situational awareness of vehicles and provide motorist/passengers with an information-rich travel environment. Scaling law for throughput capacity and delay in wireless networks has been considered as one of the most fundamental issues, which characterizes the trend of throughput/delay behavior when the network size increases. The study of scaling laws can lead to a better understanding of intrinsic properties of wireless networks and theoretical guidance on network design and deployment. Moreover, the results could also be applied to predict network performance, especially for the large-scale vehicular networks. However, map-restricted mobility and spatio-temporal dynamics of vehicle density dramatically complicate scaling laws studies for VANETs. As an effort to lay a scientific foundation of vehicular networking, my thesis investigates capacity scaling laws for vehicular networks with and without infrastructure, respectively.
Firstly, the thesis studies scaling law of throughput capacity and end-to-end delay for a social-proximity vehicular network, where each vehicle has a restricted mobility region around a specific social spot and services are delivered in a store-carry-and-forward paradigm. It has been shown that although the throughput and delay may degrade in a high vehicle density area, it is still possible to achieve almost constant scaling for per vehicle throughput and end-to-end delay.
Secondly, in addition to pure ad hoc vehicular networks, the thesis derives the capacity scaling laws for networks with wireless infrastructure, where services are delivered uniformly from infrastructure to all vehicles in the network. The V2V communication is also required to relay the downlink traffic to the vehicles outside the coverage of infrastructure. Three kinds of infrastructures have been considered, i.e., cellular base stations, wireless mesh backbones (a network of mesh nodes, including one mesh gateway), and roadside access points. The downlink capacity scaling is derived for each kind of infrastructure. Considering that the deployment/operation costs of different infrastructure are highly variable, the capacity-cost tradeoffs of different deployments are examined. The results from the thesis demonstrate the feasibility of deploying non-cellular infrastructure for supporting high-bandwidth vehicular applications.
Thirdly, the fundamental impact of traffic signals at road intersection on drive-thru Internet access is particularly studied. The thesis analyzes the time-average throughput capacity of a typical vehicle driving through randomly deployed roadside Wi-Fi networks. Interestingly, we show a significant throughput gain for vehicles stopping at intersections due to red signals. The results provide a quick and efficient way of determining the Wi-Fi deployment scale according to required quality of services.
In summary, the analysis developed and the scaling laws derived in the thesis provide should be very useful for understanding the fundamental performance of vehicular networks
Spectral Efficiency Scaling Laws in Dense Random Wireless Networks with Multiple Receive Antennas
This paper considers large random wireless networks where
transmit-and-receive node pairs communicate within a certain range while
sharing a common spectrum. By modeling the spatial locations of nodes based on
stochastic geometry, analytical expressions for the ergodic spectral efficiency
of a typical node pair are derived as a function of the channel state
information available at a receiver (CSIR) in terms of relevant system
parameters: the density of communication links, the number of receive antennas,
the path loss exponent, and the operating signal-to-noise ratio. One key
finding is that when the receiver only exploits CSIR for the direct link, the
sum of spectral efficiencies linearly improves as the density increases, when
the number of receive antennas increases as a certain super-linear function of
the density. When each receiver exploits CSIR for a set of dominant interfering
links in addition to the direct link, the sum of spectral efficiencies linearly
increases with both the density and the path loss exponent if the number of
antennas is a linear function of the density. This observation demonstrates
that having CSIR for dominant interfering links provides a multiplicative gain
in the scaling law. It is also shown that this linear scaling holds for direct
CSIR when incorporating the effect of the receive antenna correlation, provided
that the rank of the spatial correlation matrix scales super-linearly with the
density. Simulation results back scaling laws derived from stochastic geometry.Comment: Submitte
Spatial networks with wireless applications
Many networks have nodes located in physical space, with links more common
between closely spaced pairs of nodes. For example, the nodes could be wireless
devices and links communication channels in a wireless mesh network. We
describe recent work involving such networks, considering effects due to the
geometry (convex,non-convex, and fractal), node distribution,
distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina
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