A cross-layer adaptive channel selection mechanism for IEEE 802.11P suite

Abstract

Recent advances in the automotive industry enabled us to build fast, reliable, and comfortable vehicles with lots of safety features. Also, roads are designed and made safer than ever before. However, traffic accidents remain one of the major causes of death. Intelligent transport systems are expected to reduce if not prevent accidents with interconnected vehicles and infrastructures. These vehicular ad hoc networks are highly dynamic and fragile. Although the standardization efforts are mature enough, the non-emergency/service channel selection mechanisms are not explicitly defined. In this paper, a novel cross-layer prediction-based algorithm is proposed to select the best possible service channel to decrease collisions beforehand. Theoretical analysis regarding the mean squared error prediction performance is established. It is shown that the proposed method outperforms the general Markovian-based prediction schemes under various traffic load scenarios.

A Voting-Based Distributed Cooperative Spectrum Sensing Strategy for Connected Vehicles

“…Although likelihood approaches are commonly used [24], they require a likelihood threshold, which is an additional unknown that may incur extra computational cost and time. In [25], a prediction mechanism for channel selection based on energy detector employing a fixed detection threshold was proposed while Tsukamoto et al performed on-road VDSA experiments using a fixed threshold [26]. Al-Ali et al proposed a binary decision-based radio mechanism that switches between a cooperative spectrum database and an individual sensing mechanism in [27].…”