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Recent research has shown that integrating artificial intelligence (AI) into wireless communication systems can significantly improve spectral efficiency. However, the prevalent use of simulated radio channel data for training and validating neural network-based radios raises concerns about their generalization capability to diverse real-world environments. To address this, we conducted empirical over-the-air (OTA) experiments using software-defined radio (SDR) technology to test the performance of an NN-based orthogonal frequency division multiplexing (OFDM) receiver in a real-world small cell scenario. Our assessment reveals that the performance of receivers trained on diverse 3GPP TS38.901 channel models and broad parameter ranges significantly surpasses conventional receivers in our testing environment, demonstrating strong generalization to a new environment. Conversely, setting simulation parameters to narrowly reflect the actual measurement environment led to suboptimal OTA performance, highlighting the crucial role of rich and randomized training data in improving the NN-based receiver's performance. While our empirical test results are promising, they also suggest that developing new channel models tailored for training these learned receivers would enhance their generalization capability and reduce training time. Our testing was limited to a relatively narrow environment, and we encourage further testing in more complex environments.

This paper investigates the feasibility of machine learning (ML)-based pilotless spatial multiplexing in multiple-input and multiple-output (MIMO) communication systems. Especially, it is shown that by training the transmitter and receiver jointly, the transmitter can learn such constellation shapes for the spatial streams which facilitate completely blind separation and detection by the simultaneously learned receiver. To the best of our knowledge, this is the first time ML-based spatial multiplexing without channel estimation pilots is demonstrated. The results show that the learned pilotless scheme can outperform a conventional pilot-based system by as much as 15-20% in terms of spectral efficiency, depending on the modulation order and signal-to-noise ratio., Comment: Presented in the 57th Asilomar Conference on Signals, Systems, and Computers

Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance., Comment: 27 pages, this work has been submitted to the IEEE for possible publication; v2: Fixed typo in author name, v3: a revision

Machine learning (ML) has shown great promise in optimizing various aspects of the physical layer processing in wireless communication systems. In this paper, we use ML to learn jointly the transmit waveform and the frequency-domain receiver. In particular, we consider a scenario where the transmitter power amplifier is operating in a nonlinear manner, and ML is used to optimize the waveform to minimize the out-of-band emissions. The system also learns a constellation shape that facilitates pilotless detection by the simultaneously learned receiver. The simulation results show that such an end-to-end optimized system can communicate data more accurately and with less out-of-band emissions than conventional systems, thereby demonstrating the potential of ML in optimizing the air interface. To the best of our knowledge, there are no prior works considering the power amplifier induced emissions in an end-to-end learned system. These findings pave the way towards an ML-native air interface, which could be one of the building blocks of 6G.