More Like this

1. Mobile apps for 30×30 equity

   https://doi.org/10.1038/s41893-024-01309-7  

   Gaillard, Camille; Keany, Jenna M; Diehl, Jen L; Ranjan, Pranay; Biggs, Duan (June 2024, Nature Sustainability)
2. America the Beautiful: Meeting “30 × 30” Conservation Goals Through Connected Protected Areas

   https://doi.org/10.1080/24694452.2024.2410008  

   Frazier, Amy E; Kedron, Peter; Yang, Wenxin; Quan, Hejun (October 2024, Annals of the American Association of Geographers)
3. Distributed Interference Alignment for K-user Interference Channels via Deep Learning

   https://doi.org/10.1109/ISIT45174.2021.9517735  

   Mishra, Rajesh K; Chahine, Karl; Kim, Hyeji; Jafar, Syed; Vishwanath, Sriram (July 2021, IEEE International Symposium on Information Theory (ISIT))

   null (Ed.)

   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. 

   more » « less
4. Degree of Interference: A General Framework For Causal Inference Under Interference},

   Ohnishi, Yuki; Karmakar, Bikram; Sabbaghi, Arman (June 2025, Journal of Machine Learning Research)
5. Secure Retrospective Interference Alignment

   https://doi.org/10.3390/e21111092  

   Seif, Mohamed; Tandon, Ravi; Li, Ming (November 2019, Entropy)

   In this paper, the K-user interference channel with secrecy constraints is considered with delayed channel state information at transmitters (CSIT). We propose a novel secure retrospective interference alignment scheme in which the transmitters carefully mix information symbols with artificial noises to ensure confidentiality. Achieving positive secure degrees of freedom (SDoF) is challenging due to the delayed nature of CSIT, and the distributed nature of the transmitters. Our scheme works over two phases: Phase one, in which each transmitter sends information symbols mixed with artificial noises, and repeats such transmission over multiple rounds. In the next phase, each transmitter uses the delayed CSIT of the previous phase and sends a function of the net interference and artificial noises (generated in previous phase), which is simultaneously useful for all receivers. These phases are designed to ensure the decodability of the desired messages while satisfying the secrecy constraints. We present our achievable scheme for three models, namely: (1) K-user interference channel with confidential messages (IC-CM), and we show that 1 2 ( K - 6 ) SDoF is achievable; (2) K-user interference channel with an external eavesdropper (IC-EE); and 3) K-user IC with confidential messages and an external eavesdropper (IC-CM-EE). We show that for the K-user IC-EE, 1 2 ( K - 3 ) SDoF is achievable, and for the K-user IC-CM-EE, 1 2 ( K - 6 ) is achievable. To the best of our knowledge, this is the first result on the K-user interference channel with secrecy constrained models and delayed CSIT that achieves an SDoF which scales with K , square-root of number of users. 

   more » « less