A Neural Network Detector for Spectrum Sensing under Uncertainties
This addresses the problem of reliable spectrum sensing in cognitive radio systems, but it is incremental as it applies an existing neural network method to a known bottleneck in signal detection.
The paper tackles spectrum sensing under parameter uncertainty by training a neural network as a detector for modulated signals, achieving robustness to uncertainties in carrier frequency, phase, and symbol time offsets.
Spectrum sensing is of critical importance in any cognitive radio system. When the primary user's signal has uncertain parameters, the likelihood ratio test, which is the theoretically optimal detector, generally has no closed-form expression. As a result, spectrum sensing under parameter uncertainty remains an open question, though many detectors exploiting specific features of a primary signal have been proposed and have achieved reasonably good performance. In this paper, a neural network is trained as a detector for modulated signals. The result shows by training on an appropriate dataset, the neural network gains robustness under uncertainties in system parameters including the carrier frequency offset, carrier phase offset, and symbol time offset. The result displays the neural network's potential in exploiting implicit and incomplete knowledge about the signal's structure.