8 research outputs found
Artificial Intelligence-aided OFDM Receiver: Design and Experimental Results
Orthogonal frequency division multiplexing (OFDM) is one of the key
technologies that are widely applied in current communication systems.
Recently, artificial intelligence (AI)-aided OFDM receivers have been brought
to the forefront to break the bottleneck of the traditional OFDM systems. In
this paper, we investigate two AI-aided OFDM receivers, data-driven fully
connected-deep neural network (FC-DNN) receiver and model-driven ComNet
receiver, respectively. We first study their performance under different
channel models through simulation and then establish a real-time video
transmission system using a 5G rapid prototyping (RaPro) system for
over-the-air (OTA) test. To address the performance gap between the simulation
and the OTA test caused by the discrepancy between the channel model for
offline training and real environments, we develop a novel online training
strategy, called SwitchNet receiver. The SwitchNet receiver is with a flexible
and extendable architecture and can adapts to real channel by training one
parameter online. The OTA test verifies its feasibility and robustness to real
environments and indicates its potential for future communications systems. At
the end of this paper, we discuss some challenges to inspire future research.Comment: 29 pages, 13 figures, submitted to IEEE Journal on Selected Areas in
Communication