  The distributed adaptive signal fusion (DASF) framework allows to solve
spatial filtering optimization problems in a distributed and adaptive fashion
over a bandwidth-constrained wireless sensor network. The DASF algorithm
requires each node to sequentially build a compressed version of the original
network-wide problem and solve it locally. However, these local problems can
still result in a high computational load at the nodes, especially when the
required solver is iterative. In this paper, we study the particular case of
fractional programs, i.e., problems for which the objective function is a
fraction of two continuous functions, which indeed require such iterative
solvers. By exploiting the structure of a commonly used method for solving
fractional programs and interleaving it with the iterations of the standard
DASF algorithm, we obtain a distributed algorithm with a significantly reduced
computational cost compared to the straightforward application of DASF as a
meta-algorithm. We prove convergence and optimality of this "fractional DASF"
(FDASF) algorithm and demonstrate its performance via numerical simulations.
