  Future wireless networks will integrate sensing, learning and communication
to provide new services beyond communication and to become more resilient.
Sensors at the network infrastructure, sensors on the user equipment, and the
sensing capability of the communication signal itself provide a new source of
data that connects the physical and radio frequency environments. A wireless
network that harnesses all these sensing data can not only enable additional
sensing services, but also become more resilient to channel-dependent effects
like blockage and better support adaptation in dynamic environments as networks
reconfigure. In this paper, we provide a vision for integrated sensing and
communication (ISAC) networks and an overview of how signal processing,
optimization and machine learning techniques can be leveraged to make them a
reality in the context of 6G. We also include some examples of the performance
of several of these strategies when evaluated using a simulation framework
based on a combination of ray tracing measurements and mathematical models that
mix the digital and physical worlds.
