Demonstration of Machine Learning Based Receiver for MISO System Using Software-Defined Radios

Multi-antenna transmission plays a pivotal role in 5G-New Radio (5G-NR). The latest communication systems, including cellular networks and WiFi access points, consist of multiple transmitting antennas. These antennas collectively transmit shared information to the designated receiver, necessitating the use of sophisticated receivers. We present a machine learning (ML) based radio receiver implementation for a fully wireless Multiple-input–single-output (MISO) communication system using software-defined radios (SDR). MISO system results in significant improvements in received signal gain as the number of antennas increases. However, perfect channel state information (CSI) is required at the transmitter for signal precoding to achieve optimal performance. Here, we designed an ML-based radio receiver that can demodulate signals without any need for CSI. In this study, we showcase our recent implementation of the MISO system on a standard open-source SDR platform, where two antennas send signals that may combine constructively or destructively at the receiver due to varying channels, and an ML-based radio receiver successfully demodulates the combined signal. Also, we validate the results in terms of bit-error rate.