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About An Introduction To Deep Learning For The Physical Layer
1. The document introduces several novel applications of deep learning (DL) for the physical layer of communications systems. It presents a new way to think of system design as an end-to-end reconstruction task that jointly optimizes transmitter and receiver components using DL. 2. It shows how DL can be used to learn full transmitter and receiver implementations directly from a channel model to minimize a loss function, such as block error rate. These "learned" systems can perform competitively with state-of-the-art techniques. 3. The concept is extended to networks of transmitters and receivers competing for capacity, representing the interference channel as a neural network that can jointly optimize all components with respect to a performance metric
Detailed Information
| Author: | ['jun zhao'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 13 |
| Language: | English |
| Format: | |
| Price: | FREE |
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