Enabling Multi-Carrier Relay Selection by Sensing Fusion and Cascaded ANN for Intelligent Vehicular Communications

Shuping Dang, Miaowen Wen, Shahid Mumtaz, Jun Li, Chengzhong Li

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Cooperative relaying has been adopted as one of the most important techniques to enhance the energy efficiency and coverage. Multi-carrier relay selection is an efficient method to allocate spatial/spectral resources in cooperative relay networks and provides diversity gain. However, the implementation of multicarrier relay selection is not straightforward, and could render the high system complexity (for centralized implementation schemes) or long processing delay (for distributed implementation schemes). These issues hinder the promotion and implementation of multicarrier relay selection for intelligent vehicular communications. To mitigate aforementioned issues, we propose an enabling technique of multi-carrier relay selection based on sensing fusion (SF) and cascaded artificial neural networks (CANNs) for intelligent vehicular communications. We employ two well-known multicarrier relay selection schemes, i.e. bulk and per-subcarrier relay selection, to verify the effectiveness of the CANN based enabling technique. With the powerful processing ability with intelligent vehicles, the numerical results illustrate a promising vision of applying CANNs to enable multi-carrier relay selection for fast deployment in intelligent vehicular communication networks.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Sensors Journal
StatePublished - 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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