r of vehicles grows, and the need L. Azpilicueta et al. for information for autonomous driving and smart operation, interference becomes relevant to having reliable communications and network links. Densification, power control, new air interface standards, and coexistence strategies are key to solve the problem of interference [49]. Algorithms from statistical signal processing to mitigate or cancel interference need to evolve towards the smart paradigm where new machine learning and deep learning techniques are developed to be suitable to a scenario where mobility can change the environment in a very short time. Spatial stochastic modeling is important to establish behavior of coverage, capacity positioning, and tracking of vehicles and pedestrians. In contrast, security and privacy are needed for some of these operations in order to avoid cyberattacks and identity theft. Scalability for urban environments in vehicular networks will increase interference and reduce coverage unless vehicles and devices work together and align with coexistence, cooperation, and consensus to achieve high levels of quality in the network. Resource allocation and sharing will be essential, but the organization of the resources will also be fundamental to achieve an efficient communication network that delivers quality and reliability under widely varying conditions such as those in vehicular scenarios [50]. Signal processing for channel estimation needs to evolve by generating solutions in those widely varying conditions. The imperfect knowledge of the channel does not help to achieve high levels of quality and to mitigate channel impairments to a degree needed under these highly dense and highly mobile scenarios. The road towards 6G is full of software-defined solutions to add flexibility and automation of network operations and functions. The main idea is to have network as an autonomous system (AS) that can adapt itself according to network conditions such as traffic, outage, signal-to-noise ratio, cyberattacks, etc. Intelligence