Highly non‐linear and wide‐band mmWave active array OTA linearisation using neural network

Abstract

Abstract This paper proposes a neural network (NN)‐based over‐the‐air (OTA) linearisation technique for a highly non‐linear and wide‐band mmWave active phased array (APA) transmitter and compares it with the conventional memory polynomial model (MPM)‐based technique. The proposed NN effectively learns the distinctive non‐linear distortions, which may not easily fit to existing MPM solutions, and can, therefore, successfully cope with the challenges introduced by the high non‐linearity and wide bandwidth. The proposed technique has been evaluated using a state‐of‐the‐art 4 × 4 APA operating in highly non‐linear regions at 28 GHz with a 100‐MHz‐wide 3GPP base‐station signal as input. Experimental results show the pre‐distortion signal generated by the NN exhibits the peak‐to‐average power ratio (PAPR) much lower than the one generated by MPM and consequently superior linearisation performance in terms of adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) for high non‐linearity cases. Using the proposed NN‐based linearisation technique, an improvement of 5‐dB ACLR and 7% points in EVM are achieved, which demonstrates the promising potential of this technique for emerging broadband communication systems such as 5G/6G and low Earth orbit (LEO) satellite networks.

Keywords

• 5G mobile communication
• active antenna arrays
• learning (artificial intelligence)
• linearisation techniques
• millimetre wave amplifiers
• millimetre wave antenna arrays