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The robust principles of treating interference as noise (TIN) when it is sufficiently weak, and avoiding it when it is not, form the background of this work. Combining TIN with the topological interference management (TIM) framework that identifies optimal interference avoidance schemes, we formulate a TIM-TIN problem for multilevel topological interference management, wherein only a coarse knowledge of channel strengths and no knowledge of channel phases is available to transmitters. To address the TIM-TIN problem, we first propose an analytical baseline approach, which decomposes a network into TIN and TIM components, allocates the signal power levels to each user in the TIN component, allocates signal vector space dimensions to each user in the TIM component, and guarantees that the product of the two is an achievable number of signal dimensions available to each user in the original network. Next, a distributed numerical algorithm called ZEST is developed. The convergence of the algorithm is demonstrated, leading to the duality of the TIM-TIN problem in terms of generalized degrees-of-freedom (GDoF). Numerical results are also provided to demonstrate the superior sum-rate performance and fast convergence of ZEST. 

In this paper, we develop a framework for an autoencoder based transmission strategy for achieving distributed interference alignment and optimal power allocation in a multiuser 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 [1], 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.