Enhancing Wireless PHY With Adaptive OFDM and Multiarmed Bandit Learning on Zynq System-on-Chip

In this work, we present an intelligent and reconfigurable wireless physical layer (PHY) that dynamically adjusts the transmission parameters for a given radio frequency (RF) environment. The proposed PHY is based on orthogonal frequency division multiplexing (OFDM) and can dynamically augment OFDM with a finite impulse response (FIR) low-pass filter to improve the out-of-band emissions (OOBE). To make these adaptations intelligently, we employ multiarmed bandit (MAB)-based online learning algorithms, specifically upper confidence bound with control variate (UCB-CV). UCB-CV enhances traditional UCB by incorporating additional information such as interference level and transmit power, allowing it to manage interference more effectively. These algorithms are integrated into the PHY of an FPGA-based OFDM transceiver on the Zynq system-on-chip (SoC), facilitating real-time decision-making based on side-channel interference and other parameters. Our comparative analysis highlights the enhanced performance of the UCB-CV algorithm over the traditional UCB in terms of reducing the bit-error rate (BER) and managing interference more effectively. Unlike the traditional UCB, UCB-CV leverages side information through a control variate approach, incorporating the coefficient of variation (CV) into reward estimation to better handle interference. Additionally, we underline the advantages of filtered-OFDM (FOFDM) compared to standard OFDM. Notably, FOFDM significantly reduces OOBE by 20–75 dBW/Hz and improves BER. In environments with high interference, UCB-CV achieves a throughput improvement of 29.54% compared to UCB.