In this paper,convex optimization theory is introduced into the recognition of communication signals. The detailed content contains three parts. The first part gives a survey of basic concepts,main technology and recognition model of convex optimization theory. Special emphasis is placed on how to set up the new recognition model of communication signals with multisensor reports. The second part gives the solution method of the recognition model,which is called Logarithmic Penalty Barrier Function. The last part gives several numeric simulations,in contrast to D-S evidence inference method,this new method can also generate reasonable recognition results. Moreover,this new method can deal with the form of sensor reports which is more general than that allowed by the D-S evidence inference method,and it has much lower computation complexity than that of D-S evidence inference method. In addition,this new method has better recognition result,stronger anti-interference and robustness. Therefore,the convex optimization methods can be widely used in the recognition of communication signals. The first part gives a survey of basic concepts, main technology and recognition model of convex optimization theory. Special emphasis is placed on on to set up the new recognition model of communication signals with multisensor reports. The second part gives the solution method of the recognition model, which is called Logarithmic Penalty Barrier Function. The last part gives several numeric simulations, in contrast to DS evidence inference method, this new method can also generate reasonable recognition results. Moreover, this new method can deal with the form of sensor reports which is more general than that allowed by the DS evidence inference method, and it has much lower computation complexity than that of DS evidence inference method . In addition, this new method has better recognition result, stronger anti-interference and robustness. Ther efore, the convex optimization methods can be widely used in the recognition of communication signals.