Channel-uncertainty-aware hybrid learning spectrum sensing for optical OFDM networks with imperfect channel estimation

Abstract This paper proposes a channel-uncertainty-aware hybrid learning spectrum sensing (CUA-HLSS) framework for optical orthogonal frequency division multiplexing (OFDM) networks operating under imperfect channel estimation. In practical optical OFDM systems, channel estimation errors occur due to noise, limited pilot resources, and hardware impairments, which degrade the performance of conventional spectrum sensing techniques relying on fixed thresholds and ideal channel assumptions. To address this issue, the proposed framework explicitly models channel estimation uncertainty and incorporates it into the sensing process through uncertainty-aware feature extraction and hybrid learning-based adaptive decision-making. The CUA-HLSS approach employs multiple sensing features, including normalized subcarrier energy, subcarrier energy variance, residual channel error power, and a channel uncertainty factor, to enhance detection reliability, particularly in low signal-to-noise ratio (SNR) environments. Simulation results demonstrate that the proposed algorithm achieves a detection probability greater than 0.85 at −10 dB SNR, whereas classical energy detection, matched filter, and cyclostationary sensing methods achieve only 0.45–0.65 under the same conditions. Compared with CNN- and RNN-based sensing methods, CUA-HLSS provides a 15–20 % improvement in detection probability while maintaining a false alarm probability below 0.05. Furthermore, the framework requires only 4.2 ms runtime and 5.0 mJ energy per sensing decision.