On the Intelligentization of Softwarized Modem: From Algorithmic Design to Realization of Real-Time Channel-Learning Random Access

Artificial intelligence has recently permeated every field, and current research trends in wireless communications naturally involve leveraging Machine Learning (ML) for communications processing. Numerous previous research studies have theoretically demonstrated that intelligentizing physical layer holds the promise of enhancing performance for the 6G era. In this paper, we demonstrate the practical implementation of intelligentizing Random Access (RA) through real-time Channel-Learning (CL). Real-time CL adapts an Machine Learning (ML) model to the variations of wireless channel in real-time and requires extremely short and low-complexity training. Our research encompasses algorithmic design through the implementation of an off-the-shelf testbed which incorporates real-time CL in softwarized modem. We initially review the fundamental algorithms of communications processing for RA and then proceed to design a proper ML model for real-time CL. The overall intelligentized RA detector is then implemented using the concept of softwarized modem. The implementation achieves real-time CL via efficient inter-operation of communications processing and the ML model. Experiments with the implementation confirm that intelligentization through real-time CL is feasible and results in a SNR gain up to 2.7dB for RA scenarios.