  We study the distributed optimization of transmit strategies in a
multiple-input, single-output (MISO) interference channel (IFC). Existing
distributed algorithms rely on stricly synchronized update steps by the
individual users. They require a global synchronization mechanism and
potentially suffer from the synchronization penalty caused by e.g., backhaul
communication delays and fixed update sequences. We establish a general
optimization framework that allows asynchronous update steps. The users perform
their computations at arbitrary instants of time, and do not wait for
information that has been sent to them. Based on certain bounds on the amount
of asynchronism that is present in the execution of the algorithm, we are able
to characterize its convergence. As illustrated by our numerical results, the
proposed algorithm can alleviate communication overloads and is not excessively
slowed down by neither communication delays, nor by differences in the
computation intervals.
