Cellular Vehicle-to-everything (C-V2X) was introduced by 3GPP in Release 14, which supports direct vehicle-to-vehicle (V2V) communication using sidelink channels. There are two modes in C-V2X for supporting V2V communication: Mode 3 and Mode 4. Both modes support V2V communications but differ on their spectrum allocation methodology. In out-of-coverage scenario defined under Mode 4, vehicles reserve spectrum resources using semi-persistent scheduling to enable direct communication. Currently, Intelligent Transportation System (ITS) spectrum at 5.9 GHz is emerging as an immediate resource to facilitate vehicular communication which has already been assigned for IEEE 802.11p. However, these dedicated channels will not be sufficient to support the connected vehicles in densely populated cities. Leveraging the existing underutilized wireless spectrum elsewhere, such as in digital television spectrum band is a likely solution. An intelligent scheme is required which can make adaptive decisions based on channel environment to efficiently use the secondary spectrum. Bumblebee behavioral model possess evolved decision-making mechanisms to adaptively solve similar problems while foraging in environments containing multiple floral resources (channels). Simulation results using bumblebee-based algorithm have shown that a memory system based on the averaging of stored channel energy information dramatically increased VDSA performance over a memoryless system in both urban and highway scenarios by 52% and 20%, respectively. In this paper, we propose a vehicular dynamic spectrum access (VDSA) solution based on bumblebee behavioral model for C-V2X and evaluate its performance using OpenAirInterface (OAI) LTE-Sidelink platform.


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    Titel :

    Bumblebee-Inspired C-V2X Dynamic Spectrum Access Testbed Using OpenAirInterface


    Beteiligte:


    Erscheinungsdatum :

    2020-05-01


    Format / Umfang :

    604877 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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