议题简介：Deep learning (DL) has great potentials to break the bottleneck of communication systems. In this talk, we will present recent work in DL for future wireless communications, including physical layer processing and resource allocation, impact of wireless communications on federated learning.
DL can improve the performance of each individual (traditional) block in a conventional communication system or jointly optimize the whole transceiver. We can categorize the applications of DL in physical layer processing into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems.
Judicious resource (spectrum, power, etc.) allocation can significantly improve efficiency of wireless networks. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. Deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving and can help solve optimization problems for resource allocation or can be directly used for resource allocation. As an example, we will briefly discuss how to use deep reinforcement learning for wireless resource allocation in vehicular networks.
At the end of this talk, we will also briefly discuss the impact of wireless communications on federated learning, which can be regarded as an example of communications for AI.
