Dynamic spectrum access and cognitive radio for vehicular communication networks

Abstract The high mobility of vehicles and a crowded frequency spectrum result in fast-changing channel conditions that make optimal wireless channel selection a challenging problem. Vehicular dynamic spectrum access (VDSA) combines the advantages of dynamic spectrum access and knowledge of special mobility pattern of vehicles to achieve higher spectrum efficiency. It can learn from the varying channel congestion levels and automatically selects the least congested channel to maintain link quality. Such a system has the potential of supporting future automotive technologies such as vehicle autonomous driving, vehicle platooning, and is an emerging trend that could significantly increase road capacity as well as achieve semi-auto driving by grouping individual cars into tight platoons. This chapter introduces a framework and algorithms for optimizing VDSA via adaptation and learning. A test-bed implementation of VDSA is presented and a few applications are developed within the context of a VDSA environment, demonstrating the feasibility and benefits of some features in a future transportation system. We implemented this system integrating radio subsystems based on off-the-shelf networking boards and implemented essential features for achieving dynamic channel access in vehicular environment, including mobility awareness, channel congestion measurement, and channel coordination between mobile nodes. With real-world experiments we showed performance enhancements offered by proposed dynamic channel selection system by avoiding congestion.

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