  In future 6G cellular networks, a joint communication and sensing protocol
will allow the network to perceive the environment, opening the door for many
new applications atop a unified communication-perception infrastructure.
However, interpreting the sparse radio representation of sensing scenes is
challenging, which hinders the potential of these emergent systems. We propose
to combine radio and vision to automatically learn a radio-only sensing model
with minimal human intervention. We want to build a radio sensing model that
can feed on millions of uncurated data points. To this end, we leverage recent
advances in self-supervised learning and formulate a new label-free
radio-visual co-learning scheme, whereby vision trains radio via cross-modal
mutual information. We implement and evaluate our scheme according to the
common linear classification benchmark, and report qualitative and quantitative
performance metrics. In our evaluation, the representation learnt by
radio-visual self-supervision works well for a downstream sensing demonstrator,
and outperforms its fully-supervised counterpart when less labelled data is
used. This indicates that self-supervised learning could be an important
enabler for future scalable radio sensing systems.
