Smart Detection Using the Cascaded Artificial Neural Network for OFDM With Subcarrier Number Modulation
Jun Li, Shuping Dang, Miaowen Wen, Zhenrong Zhang, Qiang Li
Abstract
Since the proposal of orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM), the high detection complexity of this novel modulation scheme becomes a major issue degrading its applicability in practice. To provide an easy-to-implement and low-complexity solution to the detection of the OFDM-SNM signal, we design a cascaded artificial neural network (CANN) at the OFDM-SNM receiver to decouple the detection of subcarrier number pattern (SNP) and constellation symbols on the active subcarriers. This is the first time that the CANN design is introduced to assist in detecting parallel signals. Numerical simulations verify the effectiveness and efficiency of the proposed smart detection scheme and show that the CANN based smart detection with adequate training can yield comparable error performance to the optimal maximum-likelihood detection at much lower complexity.