NR-U Deep Receiver for WiFi Presence Detection
Tao Tao, Qiang Feng, Chenhui Ye · 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) · 2022
In this paper, we focus on the network deployment of NR-U system coexisting with WiFi system in the same unlicensed spectrum. In NR-U study, detecting the presence of WiFi was proposed as one candidate solution for coexistence fairness. However, it is very challenge to do such inter-RAT signaling detection by current NR-U receiver due to lack of time and frequency synchronization. We propose a dual-functional deep receiver for NR-U system, which is able to conduct both NR-U data receiving and WiFi preamble detection by the same radio frequency (RF) units. Attention mechanism assisted deep learning is used to recognize the signaling pattern of WiFi preamble to better overcome unknown frequency offset and misaligned receive timing. From simulation evaluation, it can be observed that the ML based approach outperforms the legacy correlation and threshold based signal detection method in all SNR regions. Even with the assumption of frequency error and misaligned timing, the ML based algorithm can still achieve larger than 90% detection probability in case of -5dB SNR. Hence, with proposed solution, inter-RAT signaling detection could be a practicable coexistence manner to be utilized in existing unlicensed bands or greenfield 7GHz unlicensed bands.