Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks - Xiao Yan; Yan Zhang; Xiaoxue Rao; Qian Wang; Hsiao-Chun Wu; Yiyan Wu (Sensors, 2022)
Cooperative automatic modulation classification (CAMC) using a swarm of sensors is intriguing nowadays as it would be much more robust than the conventional single-sensing-node automatic modulation classification (AMC) method. We propose a novel robust CAMC approach using vectorized soft decision fusion in this work. In each sensing node, the local Hamming distances between the graph features acquired from the unknown target signal and the training modulation candidate signals are calculated and transmitted to the fusion center (FC). Then, the global CAMC decision is made by the indirect vote which is translated from each sensing node’s Hamming-distance sequence. The simulation results demonstrate that, when the signal-to-noise ratio (SNR) was given by <semantics>η</semantics> ≥ <semantics>0<mspace width="3.33333pt"></mspace>dB</semantics>, our proposed new CAMC scheme’s correct classification probability <semantics>Pcc</semantics> could reach up close to <semantics>100%</semantics>. On the other hand, our proposed new CAMC scheme could significantly outperform the single-node graph-based AMC technique and the existing decision-level CAMC method in terms of recognition accuracy, especially in the low-SNR regime.