  Information exchange over networks can be affected by various forms of delay.
This causes challenges for using the network by a multi-agent system to solve a
distributed optimisation problem. Distributed optimisation schemes, however,
typically do not assume network models that are representative for real-world
communication networks, since communication links are most of the time
abstracted as lossless. Our objective is therefore to formulate a
representative network model and provide practically verifiable network
conditions that ensure convergence of distributed algorithms in the presence of
interference and possibly unbounded delay. Our network is modelled by a
sequence of directed-graphs, where to each network link we associate a process
for the instantaneous signal-to-interference-plus-noise ratio. We then
formulate practical conditions that can be verified locally and show that the
age of information (AoI) associated with data communicated over the network is
in $\mathcal{O}(\sqrt{n})$. Under these conditions we show that a penalty-based
gradient descent algorithm can be used to solve a rich class of stochastic,
constrained, distributed optimisation problems. The strength of our result lies
in the bridge between practical verifiable network conditions and an abstract
optimisation theory. We illustrate numerically that our algorithm converges in
an extreme scenario where the average AoI diverges.
