Name
Abstract | ||
|---|---|---|
End-to-end deep learning for communication systems, i.e., systems whose encoder and decoder are learned, has attracted significant interest recently, due to its performance which comes close to well-developed classical encoder-decoder designs. However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks. In real-world scenarios, such a channel model is hardly available and often the channel density is not even known at all. Some works, therefore, focus on a generative approach, i.e., generating the channel from samples, or rely on reinforcement learning to circumvent this problem. We present a novel approach which utilizes a recently proposed neural estimator of mutual information. We use this estimator to optimize the encoder for a maximized mutual information, only relying on channel samples. Moreover, we show that our approach achieves the same performance as state-of-the-art end-to-end learning with perfect channel model knowledge. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SPAWC.2019.8815464 | 2019 IEEE 20TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC 2019) |
Field | DocType | Volume |
Mathematical optimization,Communication channel,Communications system,Artificial intelligence,Encoder,Mutual information,Deep learning,Artificial neural network,Mathematics,Reinforcement learning,Estimator | Journal | abs/1903.02865 |
ISSN | Citations | PageRank |
2325-3789 | 1 | 0.35 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rick Fritschek | 1 | 4 | 4.44 |
| Rafael F. Schaefer | 2 | 165 | 35.85 |
| Gerhard Wunder | 3 | 464 | 50.42 |