Abstract
In this letter, the problem of determining the capacity of a communication channel is formulated as a cooperative game, between a generator and a discriminator, that is solved via deep learning techniques. The task of the generator is to produce channel input samples for which the discriminator ideally distinguishes conditional from unconditional channel output samples. The learning approach, referred to as cooperative channel capacity learning (CORTICAL), provides both the optimal input signal distribution and the channel capacity estimate. Numerical results demonstrate that the proposed framework learns the capacity-achieving input distribution under challenging non-Shannon settings.
Original language | English (US) |
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Pages (from-to) | 1984-1988 |
Number of pages | 5 |
Journal | IEEE Communications Letters |
Volume | 27 |
Issue number | 8 |
DOIs | |
State | Published - Aug 1 2023 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Modeling and Simulation
- Computer Science Applications
- Electrical and Electronic Engineering
Keywords
- Channel capacity
- capacity learning
- capacity-achieving distribution
- deep learning