|| Distributed Interference Alignment for K -user Interference Channels via Deep Learning
||Rajesh K Mishra, Karl Chahine, Hyeji Kim, The University of Texas at Austin, United States; Syed Jafar, The University of California, Irvine, United States; Sriram Vishwanath, GenxComm Inc., United States|
||Monday, 19 July, 22:40 - 23:00
||Monday, 19 July, 23:00 - 23:20
In this paper, we develop a framework for an autoencoder based transmission strategy for achieving distributed interference alignment and optimal power allocation in a multi-user interference channel. The users in the interference channel have access to the local channel state information only. We compare the explicit schemes, such as MaxSINR , against the autoencoder schemes. We find that the MaxSINR schemes outperform the autoencoder networks which are either jointly or distributively trained from scratch. However, we find that the autoencoders which are pretrained with the beamforming vectors and the power allocation obtained from the explicit schemes outperform the explicit schemes when the interference gets stronger. The explicit schemes perform well as they are effective in choosing the set of users which are to be suppressed. The pretrained autoencoders benefit from this initialization, and also from the fact that end to end training can improve their performance even further. We showcase our performance comparison results for 5 user interference channels with different levels of interference.