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2018 | OriginalPaper | Buchkapitel

D2D Data Offloading in Vehicular Networks with Delivery Time Selection

verfasst von : Loreto Pescosolido, Marco Conti, Andrea Passarella

Erschienen in: Wired/Wireless Internet Communications

Verlag: Springer International Publishing

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Abstract

Within the framework of a Device-to-Device (D2D) data offloading system for cellular networks, we propose a Content Delivery Management System (CDMS) in which the instant for transmitting a content to a requesting node, through a D2D communication, is selected to minimize the energy consumption required for transmission. The proposed system is particularly fit to highly dynamic scenarios, such as vehicular networks, where the network topology changes at a rate which is comparable with the order of magnitude of the delay tolerance. Through extensive system level simulations, we compare the energy consumed by the devices to perform D2D data offloading using the proposed scheme with the energy consumed when using a benchmark scheme (proposed in previous works) without optimal transmission instant selection. The results show that, in specific scenarios, compared to the benchmark system in which the transmission instant is not optimized, the proposed system allows a reduction of the energy consumed for D2D communications above 90%.

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Fußnoten
1
\(\sigma _{c}^{2}\) is computed as the thermal noise power spectral density \(N_{0}=-174\,\text {dBm/Hz}\), plus a 10 dB receiver noise figure, times the subcarrier bandwidth.
 
2
The function g(d) is one of the components of our channel model, the other component being random frequency selective fluctuations added on top of g(d), see Sect. 5. In this work, we assume that g(d) is computed according to Eqs. 5-4, 5-5, and 5-6 in [12]. Interestingly, the considered channel model distinguishes between I2D and D2D channels, using different path loss functions in the two cases. This is one of the main reasons to opt for this model.
 
3
The RRR set partitioning algorithm is similar to [9, Algorithm 1].
 
4
In this work, we assume a perfect prediction of the vehicles’ trajectory for an amount of time equal to the content timeout, leaving the evaluation of the robustness of the system with respect to trajectory prediction errors to a future work.
 
5
The reason to use a custom simulator, as opposed to classic network simulators like ns-3 or OMNET++, is to obtain a fine grain control on the physical layer aspects, retaining an acceptable level of scalability, using a state of the art channel model.
 
6
We implemented the Geometry-based Stochastic Channel Model (GSCM) described in [12, 14], up to the detail of generating the spatially correlated large scale parameters (LSPs): delay spread, shadow fading standard deviation, and Rician K-factor mean and variance, according to the procedure described in [12]. These parameters are then used to generate the random component of the set of spatially correlated frequency selective channels between any two points in a square grid with spatial step-size of 5 m, representing the region of interest. The frequency response experienced by each transmission is obtained by adding the so generated random component on top of the deterministic path loss component g(d) described in Sect. 3.2, taking the random component between the grid points closest to the transmitter and receiver, respectively.
 
7
\(E_{\text {D2D}}[\text {dB Re 1mJ]}=10\log _{10}(E_{\text {D2D}}[\text {mJ}])\), e.g, a value of 10 dB Re 1mJ means that \(E_{\text {D2D}}[\text {mJ}]\) = 10 mJ. A value of -10 dB Re 1mJ, means that \(E_{\text {D2D}}[\text {mJ}]\) = 0.1 mJ.
 
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Metadaten
Titel
D2D Data Offloading in Vehicular Networks with Delivery Time Selection
verfasst von
Loreto Pescosolido
Marco Conti
Andrea Passarella
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-02931-9_23