  The compute-and-forward framework permits each receiver in a Gaussian network
to directly decode a linear combination of the transmitted messages. The
resulting linear combinations can then be employed as an end-to-end
communication strategy for relaying, interference alignment, and other
applications. Recent efforts have demonstrated the advantages of employing
unequal powers at the transmitters and decoding more than one linear
combination at each receiver. However, neither of these techniques fit
naturally within the original formulation of compute-and-forward. This paper
proposes an expanded compute-and-forward framework that incorporates both of
these possibilities and permits an intuitive interpretation in terms of signal
levels. Within this framework, recent achievability and optimality results are
unified and generalized.
