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The proliferation of connected vehicles along with the high demand for rich multimedia services constitute key challenges for the emerging 5G-enabled vehicular networks. These challenges include, but are not limited to, high spectral efficiency and low latency requirements. Recently, the integration of cache-enabled networks with non-orthogonal multiple access (NOMA) has been shown to reduce the content delivery time and traffic congestion in wireless networks. Accordingly, in this article, we envisage cache-aided NOMA as a technology facilitator for 5G-enabled vehicular networks. In particular, we present a cache-aided NOMA architecture, which can address some of the aforementioned challenges in these networks. We demonstrate that the spectral efficiency gain of the proposed architecture, which depends largely on the cached contents, significantly outperforms that of conventional vehicular networks. Finally, we provide deep insights into the challenges, opportunities, and future research trends that will enable the practical realization of cache-aided NOMA in 5G-enabled vehicular networks.

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A new goodness-of-fit test for spectrum sensing in cognitive radios under heavy-tailed noise is proposed, based on the geometric power (also called the zero-order statistics) of the received observations. The noise statistics is assumed to follow a symmetric-alpha-stable distribution, motivated by statistics observed in realistic scenarios. The expressions are provided for the test statistic and the asymptotic detection threshold, in terms of the number of observations under the null hypothesis. Through extensive Monte Carlo simulations, the superior performance of the proposed technique over existing non-linear detection techniques is demonstrated, such as the fractional lower-order statistics, zero-memory non-linear and myriad filtering. In addition, the advantages of the proposed technique on experiment-captured data are demonstrated.