arXiv subjects

Dalia Popescu

Publications and source records attributed to Dalia Popescu.

4 recordsLinked to original sources

arXiv2021

Connecting flying backhauls of UAVs to enhance vehicular networks with fixed 5G NR infrastructure

This paper investigates moving networks of Unmanned Aerial Vehicles (UAVs), such as drones, as one of the innovative opportunities brought by the 5G. With a main purpose to extend connectivity and guarantee data rates, the drones require hovering locations due to limitations such as flight time and coverage surface. We provide analytic bounds on the requirements in terms of connectivity extension for vehicular networks served by fixed Enhanced Mobile BroadBand (eMBB) infrastructure, where both vehicular networks and infrastructures are modeled using stochastic and fractal geometry as a model for urban environment. We prove that assuming $n$ mobile nodes (distributed according to a hyperfractal distribution of dimension $d_F$) and an average of $ρ$ Next Generation NodeB (gNBs), distributed like an hyperfractal of dimension $d_r$ if $ρ=n^θ$ with $θ>d_r/4$ and letting $n$ tending to infinity (to reflect megalopolis cities), then the average fraction of mobile nodes not covered by a gNB tends to zero like $O\left(n^{-\frac{(d_F-2)}{d_r}(2θ-\frac{d_r}{2})}\right)$. Interestingly, we then prove that the average number of drones, needed to connect each mobile node not covered by gNBs is comparable to the number of isolated mobile nodes. We complete the characterisation by proving that when $θ<d_r/4$ the proportion of covered mobile nodes tends to zero. We provide insights on the intelligent placement of the "garage of drones", the home location of these nomadic infrastructure nodes, such as to minimize what we call the "flight-to-coverage time". We provide a fast procedure to select the relays that will be garages (and store drones) in order to minimize the number of garages and minimize the delay. Finally we confirm our analytical results using simulations carried out in Matlab.

cs.DC↗

arXiv2021

Characterizing the Energy Trade-Offs of End-to-End Vehicular Communications using an Hyperfractal Urban Modelling

We characterize trade-offs between the end-to-end communication delay and the energy in urban vehicular communications with infrastructure assistance. Our study exploits the self-similarity of the location of communication entities in cities by modeling them with an innovative model called "hyperfractal". We show that the hyperfractal model can be extended to incorporate road-side infrastructure and provide stochastic geometry tools to allow a rigorous analysis. We compute theoretical bounds for the end-to-end communication hop count considering two different energy-minimizing goals: either total accumulated energy or maximum energy per node. We prove that the hop count for an end-to-end transmission is bounded by $O(n^{1-α/(d_F-1)})$ where $α<1$ and $d_F>2$ is the fractal dimension of the mobile nodes process. This proves that for both constraints the energy decreases as we allow choosing routing paths of higher length. The asymptotic limit of the energy becomes significantly small when the number of nodes becomes asymptotically large. A lower bound on the network throughput capacity with constraints on path energy is also given. We show that our model fits real deployments where open data sets are available. The results are confirmed through simulations using different fractal dimensions in a Matlab simulator.

cs.DC↗

arXiv2020

Vision-Aided Radio: User Identity Match in Radio and Video Domains Using Machine Learning

5G is designed to be an essential enabler and a leading infrastructure provider in the communication technology industry by supporting the demand for the growing data traffic and a variety of services with distinct requirements. The use of deep learning and computer vision tools has the means to increase the environmental awareness of the network with information from visual data. Information extracted via computer vision tools such as user position, movement direction, and speed can be promptly available for the network. However, the network must have a mechanism to match the identity of a user in both visual and radio systems. This mechanism is absent in the present literature. Therefore, we propose a framework to match the information from both visual and radio domains. This is an essential step to practical applications of computer vision tools in communications. We detail the proposed framework training and deployment phases for a presented setup. We carried out practical experiments using data collected in different types of environments. The work compares the use of Deep Neural Network and Random Forest classifiers and shows that the former performed better across all experiments, achieving classification accuracy greater than 99%.

cs.CV↗

arXiv2019

Information Dissemination Speed in Delay Tolerant Urban Vehicular Networks in a Hyperfractal Setting

This paper studies the fundamental communication properties of urban vehicle networks by exploiting the self-similarity and hierarchical organization of modern cities. We use an innovative model called "hyperfractal" that captures the self-similarities of both the traffic and vehicle locations but avoids the extremes of regularity and randomness. We use analytical tools to derive theoretical upper and lower bounds for the information propagation speed in an urban delay tolerant network (i.e., a network that is disconnected at all time, and thus uses a store-carry-and-forward routing model). We prove that the average broadcast time behaves as $n^{1-δ}$ times a slowly varying function, where $δ$ depends on the precise fractal dimension. Furthermore, we show that the broadcast speedup is due in part to an interesting self-similar phenomenon, that we denote as {\em information teleportation}. This phenomenon arises as a consequence of the topology of the vehicle traffic, and triggers an acceleration of the broadcast time. We show that our model fits real cities where open traffic data sets are available. We present simulations confirming the validity of the bounds in multiple realistic settings, including scenarios with variable speed, using both QualNet and a discrete-event simulator in Matlab.

cs.NI↗