Abstract
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 ≥ , our proposed new CAMC scheme’s correct classification probability could reach up close to . 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.
Highlights
Automatic modulation classification (AMC) mechanisms can enable the frontend of cognitive ratio technology by blindly identifying the modulation scheme of the transmitted signal
We propose a new robust cooperative automatic modulation classification (CAMC) method based on the vectorized soft decision fusion (VSDF) mechanism
CAMC method proposed in [19] dynamically selects a sensing node in the wireless sensor networks (WSNs) as a tentative fusion center (TFC) to make the global decision according to the local identification decisions transmitted by other sensor nodes
Summary
Automatic modulation classification (AMC) mechanisms can enable the frontend of cognitive ratio technology by blindly identifying the modulation scheme of the transmitted signal. A reliable cooperative automatic modulation classification (CAMC) approach should facilitate a fusion center (FC), which fuses local information acquired and/or produced by individual sensing nodes according to [15] Such fusion mechanisms can be implemented at the data, feature and decision levels. In our proposed new CAMC approach, to identify the modulation type of an unknown target signal, each sensing node employs our graph-based AMC method, previously proposed in [21,22], to produce a decision-metric sequence, namely, the Hamming-distance sequence between the graph features acquired from the received signal data and all candidate modulations, and transmit the decision-metric sequence to the FC. By integrating the local graph-based AMC scheme at each individual sensing node and the new vectorized soft decision fusion strategy at the FC, we designed a new decision-level CAMC approach for distributed (decentralized) WSNs. Monte Carlo simulations demonstrated its superiority to the existing CAMC approach.
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