Blind Compute-and-Forward

Compute-and-forward (C&F) is a promising new approach to interference management, enjoying several advantages over other information-theoretic schemes. C&F usually requires channel state information (CSI) at the receivers so that an “optimal” scaling factor can be computed for the purposes of decoding. In this paper, a blind C&F scheme-i.e., one not requiring CSI-is developed. Rather than attempting to compute the optimal scaling factor, this new scheme seeks one or more “good” scalars, i.e., scalars that allow correct decoding despite possibly being suboptimal. The region of all such good scalars is characterized. To find a good scalar, a computationally efficient scheme is proposed which involves error-detection, a hierarchically organized list, as well as a use of the smoothing lemma from lattice theory. Simulation results show that our blind C&F scheme achieves-for a class of nested lattice codes-the same throughput as its CSI-enabled counterpart at the expense of, approximately, a two-fold increase in computational complexity in the high-throughput region. Moreover, our blind C&F scheme can be applied to multisource multirelay networks with a good performance/complexity tradeoff.