Generalized degrees of freedom for network-coded cognitive interference channel

We study a two-user cognitive interference channel (CIC) where one of the transmitters (primary) has knowledge of a linear combination (over an appropriate finite-field) of the two information messages. We refer to this channel model as Network-Coded CIC, since the linear combination may be the result of some linear network coding scheme implemented in the backbone wired network. In this paper, we characterize the generalized degrees of freedom (GDoF) for the Gaussian Network-Coded CIC. For achievability, we use the novel Precoded Compute-and-Forward (PCoF) and Dirty Paper Coding (DPC), based on nested lattice codes. Through the GDoF characterization, we show that knowing “mixed data” (a linear combination of the information messages) provides an unbounded spectral efficiency gain over the classical CIC counterpart, if the ratio (in dB) of signal-to-noise (SNR) to interference-to-noise (INR) is larger than ceratin threshold. For example, when SNR = INR, the Network-Coded cognition yields a 100% gain over the classical Gaussian CIC.