부산대학교 도서관

When cognitive users perform cooperative spectrum sensing (CSS), they can transmit their sensing information at various resolutions, ranging from binary to full-precision, according to reporting schemes. The trade-off between the quantity of information and signaling overhead in reporting schemes can pose challenges for unlicensed cognitive users. In this letter, we propose a method to dequantize the low-bits sensing information based on convolutional neural network (CNN) to improve CSS performance without extra signaling overhead. The dequantization CNN takes low-bits information as input then, through regression, produces an output that approximates the full-precision version of the information. Additionally, our proposed network can function as a module regardless of the type of CSS networks. To verify the effectiveness of dequantization, we compared the distribution of output values with the distribution of target full-precision values using Kullback-Leibler divergence. Finally, we show that the performance of CSS can be improved by the proposed dequantization CNN.