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• W3161017057 abstract "This paper provides a numerical framework for computing the achievable rate region of memoryless multiple access channel (MAC) with a continuous alphabet from data. In particular, we use recent results on variational lower bounds on mutual information and KL-divergence to compute the boundaries of the rate region of MAC using a set of functions parameterized by neural networks. Our method relies on a variational lower bound on KL-divergence and an upper bound on KL-divergence based on the f-divergence inequalities. Unlike previous work, which computes an estimate on mutual information, which is neither a lower nor an upper bound, our method estimates a lower bound on mutual information. Our numerical results show that the proposed method provides tighter estimates compared to the MINE-based estimator at large SNRs while being computationally more efficient. Finally, we apply the proposed method to the optical intensity MAC and obtain a new achievable rate boundary tighter than prior works." @default.
• W3161017057 created "2021-05-24" @default.
• W3161017057 creator A5042862413 @default.
• W3161017057 creator A5062337971 @default.
• W3161017057 date "2021-05-10" @default.
• W3161017057 modified "2023-09-27" @default.
• W3161017057 title "Neural Computation of Capacity Region of Memoryless Multiple Access Channels" @default.
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• W3161017057 hasPublicationYear "2021" @default.
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