A Novel Blind Detection Algorithm Based on Spectrum Sharing and Coexistence for Machine-to-Machine Communication

This paper proposes a new scheme that allows decentralized machine-to-machine (M2M) communication to share spectrum with conventional user communication in an orthogonal frequency division multiplexing system. This scheme can effectively separate and recover mixed signals at the receiving end. It mainly uses the signal space cancellation-complex system Hopfield neural network (SSC-CSHNN) blind detection algorithm to reconstruct the complementary projection operator and the blind detection performance function to restore the M2M communication signals. In order to further improve the anti-interference performance of the system and accelerate the convergence of the algorithm, the double sigmoid idea is introduced, and the signal space cancellation-double sigmoid complex system Hopfield neural network (SSC-DSCSHNN) blind detection algorithm is proposed. The proposed blind detection algorithm improves the anti-interference ability and the convergence speed and prevents the Hopfield neural network from falling into the local optimal solution based on the successful separation and recovery of mixed signals. Compared with existing methods, the blind detection algorithm used in this paper can directly detect the transmitted signal without identifying the channel.