It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in wireless communications, including physical layer processing and resource allocation.
DL can improve the performance of each individual (traditional) block in a conventional communication system or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications 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 with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN).
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. We will first present our research results in using deep learning to reduce the complexity of mixed integer non-linear programming (MINLP). We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.
This seminar was originally scheduled for Friday, April 9, but was rescheduled due to technical difficulties. Please join us on April 23!
Access: Seminar will be delivered live on Friday, April 23, 2021 at 9:00 AM Central (CDT; UTC -5) via the following link: Zoom
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